Preference and willingness to pay for small ruminant market facilities – Discrete choice experiment data

Data in Brief 35 (2021) 106887 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Preference and willingness to pay for small ruminant market facilities – Discrete choice experiment data Fresenbet Zeleke Abshiro a, Girma T. Kassie b,∗, Jema Haji a, Belaineh Legesse a a b School of Agricultural Economics & Agribusiness, Haramaya University, P. O. Box 138, Dire Dawa, Ethiopia International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco a r t i c l e i n f o Article history: Received 18 November 2020 Revised 12 February 2021 Accepted 12 February 2021 Available online 16 February 2021 Keywords: Choice experiment Generalized multinomial logit Market services Small ruminants Willingness to pay a b s t r a c t The data described in this brief were collected in 2018 as part of a national study to elicit preferences and estimate willingness to pay (WTP) for small ruminant market facilities in Ethiopia. We employed multistage sampling method to identify respondents. First, Menz Gishe area was selected from North Shewa administrative zone for its high small ruminant population. Second, three districts from five districts found in Menz Gishe were selected randomly. Then, eight Corresponding author. E-mail addresses: [email protected] (F.Z. Abshiro), [email protected] (G.T. Kassie), [email protected] (J. Haji), [email protected] (B. Legesse). (G.T. Kassie) Social media: ∗ https://doi.org/10.1016/j.dib.2021.106887 2352-3409/© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 2 F.Z. Abshiro, G.T. Kassie and J. Haji et al. / Data in Brief 35 (2021) 106887 Kebeles1 from fifty one Kebeles were selected randomly. Finally, 360 farmers were randomly selected proportional to the total number of farm households in each Kebele. We used discrete choice experiments to elicit preferences from the 360 respondents across the three districts whereby we presented 12 choice situations to each of them and hence generated 4320 observations. Generalized multinomial logit model (GMNL) and latent class model were used to investigate preferences for the market and heterogeneities around them. We also estimated the GMNL in WTP space to estimate the WTP values for the facilities. The dataset complements an original article entitled “Preference and Willingness to Pay for Small Ruminant Market Facilities in the Central Highlands of Ethiopia”2 and will be useful in replicating results for academic purposes and or employing the data for further development of choice behavior models. © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Specifications Table Subject Specific subject area Type of data How data were acquired Data format Parameters for data collection Description of data collection Data source location Data accessibility Related research article 1 2 Livestock Marketing, Livestock sciences, Agricultural Economics Preference elicitation using discrete choice experiments and estimation of implicit prices of small ruminants market facilities using Generalized Multinomial Logit and Latent Class Models. Table In person interviews using discrete choice experiments. Raw: .dta, .csv Menz Gishe is an area where small ruminants are important asset of the community. The sample respondents were identified from a list of farm households who were keeping small ruminants. The head of the household or his/her spouse was interviewed. Sample households were randomly drawn from eight Kebeles found in the three districts in Menz-Gishe area. Identification and selection of the market facilities was done with a series of individual and group interviews before the structured survey with which this dataset was collected. These data were was collected by trained enumerators in person using closed ended questions and choice cards which pictorially described experimentally developed small ruminant market. The respondents were briefed about the purpose of the study and the procedures of the choice experiment were explained to them before the interview began. Menz Gishe in North Shewa administrative zone of the Amhara administrative region in Central Ethiopia All the data are in a public repository: Repository name: Mendeley. Data identification number: doi: 10.17632/4754fk2tw7.2 Direct URL to data: https://data.mendeley.com/datasets/4754fk2tw7/2 Fresenbet Zeleke, Girma T. Kassie, Jema Haji, Belayneh Legesse. (2020) Preference and Willingness to Pay for Small Ruminant Market Facilities in the Central Highlands of Ethiopia, Journal of International Food & Agribusiness Marketing, https://doi.org/10.1080/08974438.2020.1838385 Kebele [plural Kebeles] is the smallest administrative unit in Ethiopia. The article has been revised and resubmitted to the Journal of International Food & Agribusiness Marketing. F.Z. Abshiro, G.T. Kassie and J. Haji et al. / Data in Brief 35 (2021) 106887 3 Value of the Data • The data shall benefit private and public investors to check the empirical analysis and concurrently prioritize the market facilities to invest on. • The dataset will be important for researchers and agricultural extension workers to enhance their efforts to improve livestock markets and identify potential strategies for sustainable provision of market services to the livestock keepers. • The dataset will be useful for broader studies that intend to compare preferences for livestock market facilities in the developing world. • The dataset shall be useful for academicians and researchers interested in meta-analysis and development of broadly applicable choice behaviour models. 1. Data Description Raw data: file “IJFAM_2020.dta” is the raw data used in all the analyses reported in the article indicated above. It has the socioeconomic variables characterizing the sample population and the trait preference data elicited using discrete choice experiment. The variables in the discrete choice experiment data are described in Table 1 below. The names of the variables as presented in the data set, their definition and the levels or ranges of values they take are summarized in the table. Table 1 Variables from the choice experiment. Variable name Label Levels/range hhid obsid cset alt choice sfen sunf VET hld wat tcln tncl fdsh feec Household/case identifier Order of observations for each household Choice set identifier Alternative Choice indicator (chosen=1) Fenced market shed Unfenced market shed Veterinary clinic Holding barn Watering trough Toilet with a cleaner Toilet without cleaner Feed shop/stall Market service fee in Birr∗ /animal 1–360 1–36 1–4315 1–3 Yes, No Fenced shed, no-shed Unfenced shed, no-shed Vet clinic, Not-vet clinic Holding barn, no-holding barn Watering, No watering Toilet with a cleaner, no-toilet Toilet without cleaner, no-toilet Feed shop, No feed shop 5, 7.5, 10, 12.5 Note: ∗ Birr is the official Ethiopian currency and currently 1 USD = 35 Ethiopian Birr. Table 2 similarly summarizes the socioeconomic variables collected in the survey and used in the analysis of preference heterogeneity. These variables were all checked as covariates to explain the unobserved heterogeneity in preferences. Finally, only those variables that explained part of the unobserved heterogeneity were included in the models estimated. 2. Experimental Design, Materials and Methods Small ruminant market attributes preference data were elicited using a discrete choice experiment. The experiment started with identification of important market attribute and attribute levels. The decision on the attributes to be included in the choice experiment was made following iterative processes of focus group discussion (FGDs) and key informant interviews (KIIs). The FGDs and KIIs were conducted in eight selected small ruminant markets using checklists. 4 F.Z. Abshiro, G.T. Kassie and J. Haji et al. / Data in Brief 35 (2021) 106887 Table 2 Socioeconomic variables. Variable Label district District 1. Menz Gera 2. Menz Keya 3. Menz Mama Sex of the respondent (male=1) Main source of income 1. Farming 2. Petty trading 3. Runs one’s own business 4. Temporary employment 5. Permanent employment Age of the respondent in years Education in years Household size in adult equivalent Distance to Market in walking hours Frequency of market visit Small ruminant herd size in TLU Total land holding in hectare gender maininco ageinyrs educ_yrs hhd_size distmakt freqlivm smrumtlu frminha N Mean/% 120 120 120 360 33.3 33.3 33.3 77.50 352 8 97.78 1.67 1 1 360 360 360 360 360 360 360 0.28 0.28 43.789 4.342 5.231 0.615 3.253 0.961 0.905 St. dev. Min. Max. 13.720 3.983 1.805 0.476 2.502 0.899 0.543 18.0 0 0 0.0 0 0 1.0 0 0 0.010 0.0 0 0 0.0 0 0 0.0 0 0 78.0 0 0 30.0 0 0 10.0 0 0 3.0 0 0 24.0 0 0 6.300 3.0 0 0 Pair-wise ranking was used to determine the set of market attributes, attribute levels and the distribution of values of the fee for alternative market scenarios included in the study. Once the attributes and their levels were determined, we proceeded with a Bayesian efficient experiment to determine the optimum number of choice situations. The design determined the number of profiles of markets over several draws taken from random prior distributions of parameter values [1,4,5]. The needed prior values of the parameters were derived from a preliminary model estimated based on the data obtained from a pilot survey of twenty households. Using the Bayesian efficient method, a design of 24 choice scenarios (CSs) was generated using Ngene Version 1.2. Each scenario consisted of a combination of two small ruminant market alternatives and an opting out option. To reduce response fatigue, the 24 choice sets were blocked into two where 12 choice sets were presented to each respondent. To assist farmers’ visualization of the hypothetical market alternatives, pictorial cards were prepared and presented during the survey. Each choice set was presented separately for the respondent and he/she chooses an alternative or opt-out from all 12 choice sets assigned to him/her. So, the data contain the choice indicator [1 for selected alternative and 0 otherwise] and the levels of the traits which characterized each of the alternatives. The opt-out option is included not to force choice and disinterest in the two hypothetical alternatives. This option does not indicate any level of traits and hence the variables take no value or are coded as missing. Respondents for the survey were drawn from household heads in three districts (district); i.e., Menz Gera, Menz Keya, and Menz Mama of Menz Gishe area of central highlands of Ethiopia. We employed multistage sampling method to identify respondents. First, Menz Gishe area was selected from North Shewa administrative zone for its high small ruminant population. Second, three districts from five districts found in Menz Gishe were selected randomly. Then, eight Kebeles from fifty one Kebeles were selected randomly. Finally, 360 farmers were randomly selected proportional to the total number of farm households in each Kebele. Before the actual survey was started, we conducted a pre-test with the enumerators and selected farmers to ensure that there is a clear understanding of the process both by enumerators and respondents. The survey questionnaire consisted of both the socio-demographic and choice experiment questions. The data were analysed using the Generalized Multinomial Logit (GMNL) model [2,3]. The WTP values were estimated using GMNL model in WTP space. We used NLOGIT Version 6 to F.Z. Abshiro, G.T. Kassie and J. Haji et al. / Data in Brief 35 (2021) 106887 5 estimate the GMNL model. Latent class [LC] models were also used to investigate trait preference heterogeneity and heuristics. We used LatentGold version 5.1 to estimate the LC models. Ethics Statement For the data obtained through the survey, we confirm that informed consent was obtained from the respondent before the beginning of the interview. Declaration of Competing Interest Authors declare no conflicts of interest with respect to authorship and publication of this article. Acknowledgment This work was undertaken as part of the CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI). Funding support for this study was provided by CGIAR Research Programs on Livestock (Livestock CRP) and Policies, Institutions, and Markets (PIM). The first author appreciates the financial and technical support received from the International Center for Agricultural Research in the Dry Areas (ICARDA). Supplementary Materials Supplementary material associated with this article can be found in the online version at doi:10.1016/j.dib.2021.106887. References [1] M.C.J. Bliemer, J.M. 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