Can Farmers Market Characteristics Predict Electronic Benefit Transfer Adoption?

2017 ◽  
Vol 117 (10) ◽  
pp. A145
Author(s):  
A. Hasin ◽  
S. Smith
Keyword(s):  
Benefit Transfer ◽  
Farmers Market ◽  
Electronic Benefit Transfer

scholarly journals Identifying and assessing factors affecting farmers’ markets Electronic Benefit Transfer sales in Hawai‘i

2020 ◽  
Vol 23 (9) ◽  
pp. 1618-1628
Author(s):  
G Wolff ◽  
DC Nelson-Hurwitz ◽  
OV Buchthal
Keyword(s):  
Low Income ◽  
Business Models ◽  
Benefit Transfer ◽  
Farmers Markets ◽  
Market Area ◽  
Farmers Market ◽  
Factors Affecting ◽  
Program Success ◽  
Electronic Benefit Transfer ◽  
The Impact

AbstractObjective:Electronic Benefit Transfer (EBT) placement at farmers’ markets can reduce access disparities for low-income consumers. However, resources needed to operate EBT programs may challenge markets’ business models. A conceptual model of factors impacting EBT program success was developed from literature, and an exploratory study conducted to assess the impact of model variables on market EBT sales.Design:Annual EBT sales data were obtained for all Hawai‘i farmers’ markets with EBT programs (n 22). Key informant interviews (n 19), along with records review, were performed to gather data on model variables. Exploratory analysis was conducted to estimate the impact of individual model variables on EBT sales.Setting:Farmers’ markets accepting EBT in the state of Hawai‘i.Participants:Market managers and EBT program partners (n 19).Results:Markets engaging in community partnerships $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ 852} \right)$, consumer education $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ {\rm{598}}} \right)$, social media promotion $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ {\rm{732}}} \right)$ or EBT incentives $\left( {{\mkern 1mu} {\mkern 1mu} \Delta \overline x = \$ {\rm{5}}0{\rm{9}}} \right)$ averaged higher sales than markets not reporting these practices. Sales increased by $3 for every ten additional SNAP-participating households and decreased by $35 for each competing EBT-accepting supermarket, grocery or farmers’ market within the market’s access area. Sales increased by $137/vendor for each additional hour/week the market was open.Conclusion:Factors suggested by the model, particularly community engagement and partnership, marketing methods, consumer base and competition for EBT sales in the market area substantively affected EBT sales. Assessing these factors may identify markets with the greatest chance of EBT success and suggest ways to strengthen struggling EBT programs.

Dietary Behaviors among Public Health Center Clients with Electronic Benefit Transfer Access at Farmers’ Markets

2017 ◽  
Vol 117 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Brenda Robles ◽  
Christine E. Montes ◽  
Tabashir Z. Nobari ◽  
May C. Wang ◽  
Tony Kuo
Keyword(s):  
Public Health ◽  
Health Center ◽  
Benefit Transfer ◽  
Dietary Behaviors ◽  
Farmers Markets ◽  
Public Health Center ◽  
Electronic Benefit Transfer

scholarly journals Less Cash, Less Crime: Evidence from the Electronic Benefit Transfer Program

2014 ◽  
Author(s):  
Richard Wright ◽  
Erdal Tekin ◽  
Volkan Topalli ◽  
Chandler McClellan ◽  
Timothy Dickinson ◽  
...  
Keyword(s):  
Benefit Transfer ◽  
Transfer Program ◽  
Electronic Benefit Transfer

scholarly journals The Impact of New York City’s Health Bucks Program on Electronic Benefit Transfer Spending at Farmers Markets, 2006–2009

2013 ◽  
Vol 10 ◽  
Author(s):  
Sabrina Baronberg ◽  
Lillian Dunn ◽  
Cathy Nonas ◽  
Rachel Dannefer ◽  
Rachel Sacks
Keyword(s):  
New York ◽  
Benefit Transfer ◽  
Farmers Markets ◽  
Electronic Benefit Transfer ◽  
The Impact
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