Willingness to Contribute Time versus Willingness to Pay for the Management of Harmful Algal Blooms
Abstract
:1. Introduction
2. Materials and Methods
2.1. Contingent Valuation Survey
2.2. Econometric Models
3. Results
3.1. CV Survey Data
3.2. Drivers of WTP and WTCT
4. Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
No. | Author (Year) | Resource and Location | Significant Variables in | ||
---|---|---|---|---|---|
Only WTP Model | Only WTCT Model | Both WTP and WTCT Models | |||
1 | Hung et al., (2007) [33] | Forest fire prevention, Vietnam | none | education | farming occupation |
2 | O’Garra (2009) [22] | Marine resource conservation, Fiji | income, education, fishing occupation, anthropocentric view of resource | hours of work, number of children, supports resource protection for future generations | gender, involved in management decisions, works outside resource area |
3 | Casiwan-Launio et al., (2011) [28] | Marine protected areas (MPAs), Philippines | education | age, gender, knowledge about number of mpas | income |
4 | Alam (2013) [29] | River ecosystem restoration, Bangladesh | income, head of household | education, sex, age, dependency on resource | occupation type, concern for river |
5 | Schiappacasse et al., (2013) [27] | Forest restoration, Chile | income, size of farmland, believes that forests can be exploited sustainably | age, non-work hours, believes that forests are important symbols | none |
6 | Tilahun et al., (2013) [20] | Forest conservation, Ethiopia | income, education, interaction of education, and length of residence | gender, family size (15- to 64-year-olds), interaction of age and length of residence | none |
7 | Kalisa (2014) [25] | Rural electrification using dangerous gas from lake, Rwanda | income, age, years at current residence, location of residence, owns residence, knowledge about electricity project | knowledge about gas extraction for electricity generation | has electricity at home, has home-based business |
8 | Tilahun et al., (2017) [34] | Invasive tree species mitigation, Ethiopia | none | extent of invasion by species on pasturelands and government lands | occupation, non-farm income, physical injury by invasive species |
9 | Girma et al., (2021) [24] | Lake restoration, Ethiopia | farm income | education, distance from lake to farm | size of farmland |
10 | Ting et al., (2021) [30] | Ecotourism and conservation in National Park, China | income | none | supports tourism, payment vehicle type, location of residence |
11 | Petcharat et al., (2022) [31] | Endangered wetland plant habitat conservation, Thailand | age | income | values improved biodiversity, values improved water quality, values improved upstream conditions |
12 | Xu et al., (2022) [32] | Ecosystem conservation in nature reserves, China | community attachment, degree of acceptance to pay | degree of acceptance to work | income, education |
13 | Ofori (this study) | Invasive seaweed management and coastal protection, Ghana | income, education, sex, family size, years of residence | distance to the beach, occupation type, house ownership | attitudes about invasive seaweeds, political affiliation |
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Continuous Variables | Mean | Std. Dev. | Min | Max | Obs. |
---|---|---|---|---|---|
WTP (GHC, monthly) | 30.51 | 106.03 | 0 | 1000 | 328 |
WTCT (hours, monthly) | 9.14 | 16.61 | 0 | 110 | 167 |
Family size | 6.71 | 5.93 | 1 | 54 | 497 |
Years of residence | 25.56 | 15.81 | 0.17 | 85 | 483 |
Categorical Variables | Pct. | Obs. | Categorical Variables | Pct. | Obs. |
Household income (GHS, monthly) | 492 | Occupation: at least one household member has a sea-dependent occupation | 502 | ||
Less than 100 | 21.95 | Yes | 63.75 | ||
Between 100 and 500 | 49.59 | No | 36.25 | ||
Between 500 and 1000 | 19.51 | Sex of the household head | 501 | ||
Between 1000 and 5000 | 8.33 | Male | 51.90 | ||
Between 5000 and 10,000 | 0.00 | Female | 48.10 | ||
More than 10,000 | 0.61 | Other | 0.00 | ||
Education of the household head | 497 | House ownership | 499 | ||
No formal education | 14.69 | Own my house | 52.91 | ||
Primary: grades 1–6 | 16.50 | Do not own my house | 47.09 | ||
Junior high: grades 7–9 | 40.44 | Attitudes about invasive seaweeds | 476 | ||
Senior high: grades 10–12 | 16.10 | Seaweeds invasion is bad | 76.26 | ||
Tertiary | 12.27 | Seaweeds invasion is good | 15.34 | ||
Distance from house to beach | 502 | Indifferent | 8.40 | ||
Southern communities (nearest) | 27.29 | Political affiliation | 488 | ||
Central communities | 46.22 | Affiliated to party in office | 21.72 | ||
Northern communities | 26.49 | Affiliated to opposition party | 41.60 | ||
Neutral | 36.68 |
Variables | WTP Models | WTCT Models | ||||||
---|---|---|---|---|---|---|---|---|
Linear 1 | s.e. | Interval 2 | s.e. | Linear 1 | s.e. | Interval 2 | s.e. | |
Household income | 0.01 *** | (0.00) | 0.43 *** | (0.13) | 0.05 | (0.08) | −0.06 | (0.12) |
Education of the household head | 0.23 *** | (0.04) | 0.58 *** | (0.10) | 0.13 | (0.14) | 0.01 | (0.11) |
Family size | 0.03 * | (0.02) | 0.02 | (0.02) | 0.06 | (0.07) | 0.05 * | (0.03) |
Years of residence | −0.01 ** | (0.00) | −0.01 * | (0.01) | 0.00 | (0.00) | 0.01 | (0.01) |
Distance from house to the beach | −0.38 | (0.45) | −0.41 ** | (0.17) | −0.08 *** | (0.02) | −0.04 | (0.17) |
Occupation of household: sea-dependent vs. others | 0.05 | (0.10) | 0.26 | (0.27) | 0.28* | (0.15) | 0.22 | (0.25) |
Sex of the household head: male vs. female | −0.08 ** | (0.04) | 0.24 | (0.23) | −0.34 | (0.45) | −0.11 | (0.22) |
House ownership: own vs. do not own | 0.06 | (0.13) | 0.02 | (0.24) | −0.24 *** | (0.01) | −0.09 | (0.23) |
Attitudes: invasive seaweeds are good vs. bad | −0.51 * | (0.29) | −1.16 *** | (0.38) | −0.92 *** | (0.08) | −0.55 * | (0.32) |
Attitudes: indifferent vs. invasive seaweeds are bad | −1.28 *** | (0.32) | −0.78 * | (0.40) | 0.12 | (0.09) | 0.15 | (0.40) |
Political affiliation: opposition vs. party in office | −0.46 | (0.44) | −0.42 | (0.30) | −0.48 ** | (0.19) | −0.45 | (0.30) |
Political affiliation: neutral vs. party in office | −0.75 ** | (0.36) | −0.46 | (0.31) | −0.74 * | (0.38) | −0.58 * | (0.33) |
Constant | 2.94 *** | (1.09) | 0.36 | (0.69) | 1.52 *** | (0.26) | 1.75 *** | (0.64) |
Observations | 240 | 328 | 122 | 138 |
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Ofori, R.O. Willingness to Contribute Time versus Willingness to Pay for the Management of Harmful Algal Blooms. Phycology 2023, 3, 382-393. https://doi.org/10.3390/phycology3030025
Ofori RO. Willingness to Contribute Time versus Willingness to Pay for the Management of Harmful Algal Blooms. Phycology. 2023; 3(3):382-393. https://doi.org/10.3390/phycology3030025
Chicago/Turabian StyleOfori, Roland O. 2023. "Willingness to Contribute Time versus Willingness to Pay for the Management of Harmful Algal Blooms" Phycology 3, no. 3: 382-393. https://doi.org/10.3390/phycology3030025
APA StyleOfori, R. O. (2023). Willingness to Contribute Time versus Willingness to Pay for the Management of Harmful Algal Blooms. Phycology, 3(3), 382-393. https://doi.org/10.3390/phycology3030025