Despite the values associated with riparian habitats (RH), in Nairobi County these habitats are under pressure from human activities such as: - urban farming, informal settlements and dumping of solid wastes. Recently, the Kenyan National Environmental Management Authority (NEMA) demolished structures along RH to promote their health. The intervention could be rational with economic and environmental implications on RH protection, but empirical evidence is lacking. Therefore, understanding welfare effects associated with change in Elicitation Formats (EF) could explain the observed behavior. Multistage sampling procedure was used to sample 774 households. Stochastic Payment Card (SPC) and Multiple Bound Discrete Choice Payment Card (MBDC) generated the data. Data were: - collected through interview schedule, analyzed using Two Stage Random Valuation model and processed with STATA. MBDC willingness to pay (WTP) seemed inconsistent even though it was 1.26 times that of SPC. At 1% significance level, a statistical difference in mean WTP values was observed between the SPC and MBDC data, leading to rejection of null hypothesis in favor of the alternative (There’s a significant difference in mean WTP value between SPC and MBDC formats). Determinants (Age, Gender, Income, Distance, Necessity to protect and Land ownership) significantly influenced WTP across the three models. Standard deviations of WTP distributions were significantly influenced by (Distance, Age, Gender, Household size, Certainty of future incomes, Necessity to protect and Land ownership). The Kenyan residents were willing to pay positive amounts towards RHP. SPC valuation format was most preferred for valuation of RHP since it led to underestimation of RHP in Kenya. Change in EF positively influenced welfare estimates at 1% significance level leading to the rejection of the overall null hypothesis (Changing the EF does not significantly affect individual welfare estimates towards RHP in Kenya). Therefore, city authorities can now use the mean and SD estimates to benchmark their budget and policy proposals for RHP, with adjustments for individual WTP uncertainties, socio-economic and other characteristics of individuals, given they have proved to be important drives of welfare estimate decisions. Valuation estimates can now be used to formulate policies for restoration and protection of RH in Kenya and beyond to enhance their functioning. Moreover, more comparative studies can be done on valuation of other environmental goods and services with change in in EF as a variable.
| Published in | International Journal of Environmental Protection and Policy (Volume 10, Issue 4) |
| DOI | 10.11648/j.ijepp.20221004.12 |
| Page(s) | 80-91 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Contingent Valuation, Stochastic Payment Card (SPC), Multiple Bound Discrete Choice (MBDC), Willingness to Pay
| [1] | Acharya, A. S., Prakash, A., Saxena, P. and Nigam, A. (2013). Sampling: Why and How of it? Indian journal of medical specialties, Vol. 4 (2): 330-333. |
| [2] | Alberini, A., & Cooper, J. (2000). Application of the contingent valuation method in developing countries. Journal of Economic and Social Development 146: 7-22. |
| [3] | Braun, C., Rehdanz, K, &Schmidt, U. (2016). Validity of Willingness to Pay Measures under Preference Uncertainty. PLoS ONE 11 (4): e0154078. DOI: 10.1371/journal.pone.0154078. |
| [4] | Cameron, T. A. and Huppert, D. D. (1989). OLS versus ML estimation of non-market resource values with payment card interval data. Journal of Environmental Economics and Management, 17: 230-246. |
| [5] | Carson, R. T., Florece. N. E., & Meade, N. F. (2001). Contingent valuation: controversies and evidence. Journal of environmental and resource economics vol. 19, pp. 173-210. |
| [6] | Carson, R. T. (2000). Contingent valuation: a user’s guide. Environmental Science and Technology, Vol. 34 (8), 1413-1418. doi: 10.1021/es990728j. |
| [7] | Colby, B. & Orr, P. (2005). Economic Tradeoffs in preserving Riparian Habitat. Natural resources Journal, vol. 45, Issue 1. |
| [8] | Constitution of Kenya, Article 69 (a) of 2010. Kenya gazette. Nairobi, Kenya. |
| [9] | CBS (2001). Economic Survey (2001). Central Bureau of Statistics (CBS), Nairobi, Kenya. |
| [10] | CBS (2019). Economic Survey (2001). Central Bureau of Statistics (CBS), Nairobi, Kenya. |
| [11] | Champ, P. A., and Bishop, R. C. (2006). Is willingness to pay for a public good sensitive to the elicitation format? Journal of land economics, 82 (2), pp, 162–173. |
| [12] | Chanel, O., Makhloufi, K., & Abu-Zaineh, M. (2016). Can a Circular Payment Card Format Effectively Elicit Preferences? Evidence from a survey on a mandatory health insurance scheme in Tunisia. Applied Health, equity and development research working paper No. 2/2016. |
| [13] | Ebeto, C. (2017). Sampling and sampling methods. Biometrics and biostatistics international journal. Vol. 5 (6). |
| [14] | Environment Management and Coordination Act 1999. National council for law reporting press, Kenya. |
| [15] | Evans, M. F., Flores, N. E. & K. J. Boyle, (2003), ‘Multiple Bounded Uncertainty Choice Data as Probabilistic Intentions’. Land Economics, Vol. 79, Issue. 4, p549-560. |
| [16] | Fonta, W. M., Ichoku, H. E., &Ogujiuba, K. K. (2011). Estimating Willingness to Pay with the Stochastic Payment Card Design: Further Evidence from Rural Cameroon. Environ Dev Sustain (2010) 12: 179–193. DOI 10.1007/s10668-009-9188-1. |
| [17] | Gobbens, R. J. J. & Van Assen M. A. L. (2018). Associations of Environmental Factors with Quality of Life in Older Adults. The genontological society of America vol. 58, No. 1, pp 101-110. |
| [18] | Hughes, A. O. (2016). Riparian Management and Stream Bank Erosion in New Zealand, New Zealand. Journal of Marine and Freshwater Research, 50: 2, 277-290, DOI: 10.1080/00288330.2015.1116449. |
| [19] | Ichoku, H. E., Fonta, W. M., & Kedir, A. (2009). Measuring Individuals’ Valuation Distributions Using a Stochastic Payment Card Approach: Application to Solid Waste Management in Nigeria. Journal of Environ Dev Sustain, 11: 509–521. DOI 10.1007/s10668-007-9127-y. |
| [20] | IMF (2020). Regional economic outlook. Middle east and central Asia. International Monetary Fund, Publication Services P. O. Box 92780, Washington, DC 20090, USA Tel.: (202) 623-7430 Fax: (202) 623-7201. |
| [21] | Jeffrey, S. Koeckhoven, D. Trautman, B. Dollevoet, J. R. Unterschultz & Ross, C. (2014). Economics of Riparian Beneficial Management Practices for Improved Water Quality: A Representative Farm Analysis in The Canadian Prairie Region, Canadian Water Resources Journal / Revue canadienne des resources hydriques, 39: 4, 449-461, DOI: 10.1080/07011784.2014.965035. |
| [22] | Johnson, R. R., and Carothers, S. W. (1982). Riparian Habitats and Recreation: Interrelationships and Impacts in the Southwest and Rocky Mountain Region. Eisenhower Consortium Bulletin 12. |
| [23] | Kenya, Republic of. (2002). Water Act 2002: Laws of Kenya. Nairobi: Government Printer. |
| [24] | Laroche, M., Bergeron, J., Forleoa, B. (2001). Targeting Consumers Who Are Willing to Pay More for Environmentally Friendly Products. Journal of consumer marketing vol. 18 No. 6 pp 503-520. |
| [25] | Lewis, S. E., Popp, J. S., English, L. A., & Odetola, T. O. (2017). Willingness to Pay for Riparian Zones In Ozark Watershed. Journal of water resource planning and management, Vol. 143 (5); 04017006. |
| [26] | Matunda, J. M. (2015). Sustainable Management of Riparian Areas in Kenya: A Critique of the inadequacy of the Legislative Framework Governing the Protection of Sustainable Management of Riparian Zones in Kenya. A Dissertation Submitted to the School of Law, University of Nairobi in Partial fulfillment of Requirements for the Award of a Master of Laws Degree (Llm) Environmental Law. |
| [27] | Muketha, S. M. (2014). Riparian zone conservation in a changing urban land use environment: a case of Nairobi River basin, Kenya. A thesis submitted in fulfillment of the award of the degree of Doctor of Philosophy of the University of Nairobi, Department of Urban and Regional Planning School of Built Environment. |
| [28] | Mwaura, F. Muwanika, F. R. and Okoboi, G. (2010). Willingness to pay for extension services in Uganda among farmers involved in crop and animal husbandry. Contributed paper presented at the joint 3rd African association of Agricultural economists’ association of South Africa conference, Cape Town, South Africa. |
| [29] | Neupane, D., Kunwar, S., Bohara, A. K., Risch, T. S., &Johnson, R. L. (2017). Willingness to Pay for Mitigatigating Human Elephant Conflict by Residents of Nepal. Journal of nature conservation, Vol. 36. pp 65-76. |
| [30] | Ndambiri, H., Brouwer, R. and Mungatana, E. (2017). Scope Effects of Respondent Uncertainty in Contingent Valuation, Evidence from Motorized Emission Reduction. In The City of Nairobi, Kenya. Journal of environmental planning and management, DOI: 10.1080/09640568.2016.1140024. |
| [31] | Ndambiri, H., Brouwer, R. and Mungatana, E. (2016). Comparing Welfare Estimates Across Stated Preference and Uncertainty Elicitation Formats for Air Quality Improvements in Nairobi, Kenya. Journal of environment and development economics, Vol. 21 (5). |
| [32] | Ndambiri, H., Brouwer, R. and Mungatana, E. (2015). Stated Preferences for Improved Air Quality Management in The City of Nairobi, Kenya. European journal of applied economics, vol 12 (2), pp. 16-26. |
| [33] | Nyongesa, J. M., Bett, H. K, Lagat, J. K and Ayuya, O. I. (2016). Estimating Farmers Stated Willingness to Accept Pay for Ecosystem Services, Case of Lake Naivasha Watershed Payment for Ecosystem Services Scheme –Kenya. Ecological processes 2016, No. 5: 15. |
| [34] | Onoh, P. A., Omeire, C. O, Echetama, J. A., Ukpongson, M. A., Ugwoke, F. O., Ejiogu-okereke, E. N., Onoh A. L., and Ogomuo, C. I. (2014). Analysis of Livestock Farmers’ Willingness to Pay for Agricultural Extension Services in South East Nigeria. Journal of agriculture and veterinary science, vol. 7 (7), pp 55-60. |
| [35] | Ozor, N., Garforth, C. J. & Madukwe, M. C. (2013). Farmers’ Willingness to Pay for agricultural Extension Service: Evidence from Nigeria. Journal of International Development, Vol. 25, Pp. 382–392 (2013). |
| [36] | Pate, J., & Loomis, J. (1997). The Effect of Distance On Willingness to Pay Values: A Case Study of Wetlands And Salmon In California. Journal of Ecological Economics 20 (1997), pp 199-207. |
| [37] | Qureshi, M. E and Harrison, S. (2002). Economic Instruments and Regulatory Approaches in Implementing Riparian Revegetation Options: Observations of the Queensland System. Australian Journal of Environmental Management, 9: 2, 89-98, DOI: 10.1080/14486563.2002.10648547. |
| [38] | Remoundou, K. & Koundouri, P. (2009). Environmental Effects on Public Health an Economic Perspective. International journal of environmental research and public health. Vol. 6 pp 2160-2178. |
| [39] | Ryan, R. L., Erickson, D. L., & Young, R. De. (2003). Farmers' Motivations for Adopting Conservation Practices along Riparian Zones in a Mid-Western Agricultural Watershed, Journal of Environmental Planning and Management, 46: 1, 19-37, DOI: 10.1080/713676702. |
| [40] | Svenningsen, L. S., &Jacobsen, J. B. (2018). Testing The Effect of Changes in Elicitation Format, Payment Vehicle and Bid Range On the Hypothetical Bias for Moral Goods. Journal of Choice Modelling (2018), DOI: 10.1016/j.jocm.2018.08.001. |
| [41] | Tran, H. T. & Navrud, S. (2007). Valuing Cultural Heritage in Developing Countries: Comparing and Pooling Contingent Valuation and Choice Modelling Estimates. Journal of Environ Resource Econ (2007) 38: 51–69. |
| [42] | Vossler, C. A. & Poe, G. L. (2005). Analysis of Contingent Valuation Data with Multiple Bid and Response Options Allowing Respondents to Express Uncertainty: A Comment. Journal of Environmental Economics and Management, 49, 197–200. |
| [43] | Vossler, C. A., Poe, G. L., Welsh, M. P. & Ethier, R. G. (2004). Bid Design Effects in Multiple Bounded Discrete Choice Contingent Valuation. Journal of Environmental and Resource Economics 29: 401–418, 2004. |
| [44] | Vossler, C. A., (2003). Multiple Bounded Discrete Choice Contingent Valuation: Parametric and Nonparametric Welfare Estimation and A Comparison to The Payment Card. Online at https://mpra.ub.uni-muenchen.de/38867/ MPRA Paper No. 38867, posted 18 May 2012 12: 47 UTC. |
| [45] | Wang, H. & Jie, J. (2010). Estimating Individual Valuation Distributions with Multiple Bounded Discrete Choice Data. Applied Economics, first published on: 13 July 2010. To link to this Article: DOI: 10.1080/00036840903299789. URL: http://dx.doi.org/10.1080/00036840903299789 |
| [46] | Wang, H. & Whittington, D. (2005). Measuring Individuals’ Valuation Distributions Using a Stochastic Payment Card Approach, Journal of Ecological Economics, 55, 143–54. |
| [47] | Wang, H. (1997). Treatment of ‘Don’t-Know’ Responses in Contingent Valuation Surveys: A Random Valuation Model. Journal of Environmental Economics and Management, 32, 219–32. |
| [48] | Water Act No. 43 of 2016 of the laws of Kenya, Environmental laws of Kenya. Kenya government press, Nairobi Kenya. |
| [49] | Water Resources Management Rules (WRMR) 2007. Legislation number 171 of 2007. Kenya government press, Nairobi Kenya. |
| [50] | Welsh, M. & G. L. Poe (1998). Elicitation Effects in Contingent Valuation: Comparisons to a Multiple Bounded Discrete Choice Approach’. Journal of Environmental Economics and Management, 36, pp. 170-185. |
APA Style
Esther Machana Magembe, Hilary Kabiru Ndambiri, Jared Isaboke Mose. (2022). Elicitation Format Effects on Welfare Estimates of Riparian Habitat Protection in Kenya. International Journal of Environmental Protection and Policy, 10(4), 80-91. https://doi.org/10.11648/j.ijepp.20221004.12
ACS Style
Esther Machana Magembe; Hilary Kabiru Ndambiri; Jared Isaboke Mose. Elicitation Format Effects on Welfare Estimates of Riparian Habitat Protection in Kenya. Int. J. Environ. Prot. Policy 2022, 10(4), 80-91. doi: 10.11648/j.ijepp.20221004.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijepp.20221004.12,
author = {Esther Machana Magembe and Hilary Kabiru Ndambiri and Jared Isaboke Mose},
title = {Elicitation Format Effects on Welfare Estimates of Riparian Habitat Protection in Kenya},
journal = {International Journal of Environmental Protection and Policy},
volume = {10},
number = {4},
pages = {80-91},
doi = {10.11648/j.ijepp.20221004.12},
url = {https://doi.org/10.11648/j.ijepp.20221004.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepp.20221004.12},
abstract = {Despite the values associated with riparian habitats (RH), in Nairobi County these habitats are under pressure from human activities such as: - urban farming, informal settlements and dumping of solid wastes. Recently, the Kenyan National Environmental Management Authority (NEMA) demolished structures along RH to promote their health. The intervention could be rational with economic and environmental implications on RH protection, but empirical evidence is lacking. Therefore, understanding welfare effects associated with change in Elicitation Formats (EF) could explain the observed behavior. Multistage sampling procedure was used to sample 774 households. Stochastic Payment Card (SPC) and Multiple Bound Discrete Choice Payment Card (MBDC) generated the data. Data were: - collected through interview schedule, analyzed using Two Stage Random Valuation model and processed with STATA. MBDC willingness to pay (WTP) seemed inconsistent even though it was 1.26 times that of SPC. At 1% significance level, a statistical difference in mean WTP values was observed between the SPC and MBDC data, leading to rejection of null hypothesis in favor of the alternative (There’s a significant difference in mean WTP value between SPC and MBDC formats). Determinants (Age, Gender, Income, Distance, Necessity to protect and Land ownership) significantly influenced WTP across the three models. Standard deviations of WTP distributions were significantly influenced by (Distance, Age, Gender, Household size, Certainty of future incomes, Necessity to protect and Land ownership). The Kenyan residents were willing to pay positive amounts towards RHP. SPC valuation format was most preferred for valuation of RHP since it led to underestimation of RHP in Kenya. Change in EF positively influenced welfare estimates at 1% significance level leading to the rejection of the overall null hypothesis (Changing the EF does not significantly affect individual welfare estimates towards RHP in Kenya). Therefore, city authorities can now use the mean and SD estimates to benchmark their budget and policy proposals for RHP, with adjustments for individual WTP uncertainties, socio-economic and other characteristics of individuals, given they have proved to be important drives of welfare estimate decisions. Valuation estimates can now be used to formulate policies for restoration and protection of RH in Kenya and beyond to enhance their functioning. Moreover, more comparative studies can be done on valuation of other environmental goods and services with change in in EF as a variable.},
year = {2022}
}
TY - JOUR T1 - Elicitation Format Effects on Welfare Estimates of Riparian Habitat Protection in Kenya AU - Esther Machana Magembe AU - Hilary Kabiru Ndambiri AU - Jared Isaboke Mose Y1 - 2022/07/28 PY - 2022 N1 - https://doi.org/10.11648/j.ijepp.20221004.12 DO - 10.11648/j.ijepp.20221004.12 T2 - International Journal of Environmental Protection and Policy JF - International Journal of Environmental Protection and Policy JO - International Journal of Environmental Protection and Policy SP - 80 EP - 91 PB - Science Publishing Group SN - 2330-7536 UR - https://doi.org/10.11648/j.ijepp.20221004.12 AB - Despite the values associated with riparian habitats (RH), in Nairobi County these habitats are under pressure from human activities such as: - urban farming, informal settlements and dumping of solid wastes. Recently, the Kenyan National Environmental Management Authority (NEMA) demolished structures along RH to promote their health. The intervention could be rational with economic and environmental implications on RH protection, but empirical evidence is lacking. Therefore, understanding welfare effects associated with change in Elicitation Formats (EF) could explain the observed behavior. Multistage sampling procedure was used to sample 774 households. Stochastic Payment Card (SPC) and Multiple Bound Discrete Choice Payment Card (MBDC) generated the data. Data were: - collected through interview schedule, analyzed using Two Stage Random Valuation model and processed with STATA. MBDC willingness to pay (WTP) seemed inconsistent even though it was 1.26 times that of SPC. At 1% significance level, a statistical difference in mean WTP values was observed between the SPC and MBDC data, leading to rejection of null hypothesis in favor of the alternative (There’s a significant difference in mean WTP value between SPC and MBDC formats). Determinants (Age, Gender, Income, Distance, Necessity to protect and Land ownership) significantly influenced WTP across the three models. Standard deviations of WTP distributions were significantly influenced by (Distance, Age, Gender, Household size, Certainty of future incomes, Necessity to protect and Land ownership). The Kenyan residents were willing to pay positive amounts towards RHP. SPC valuation format was most preferred for valuation of RHP since it led to underestimation of RHP in Kenya. Change in EF positively influenced welfare estimates at 1% significance level leading to the rejection of the overall null hypothesis (Changing the EF does not significantly affect individual welfare estimates towards RHP in Kenya). Therefore, city authorities can now use the mean and SD estimates to benchmark their budget and policy proposals for RHP, with adjustments for individual WTP uncertainties, socio-economic and other characteristics of individuals, given they have proved to be important drives of welfare estimate decisions. Valuation estimates can now be used to formulate policies for restoration and protection of RH in Kenya and beyond to enhance their functioning. Moreover, more comparative studies can be done on valuation of other environmental goods and services with change in in EF as a variable. VL - 10 IS - 4 ER -
Email: