Abstract:The recent growth in local food markets has resulted in various local food economic impact assessments. However, drawing overarching conclusions from these studies is difficult. Data collection is challenging, and the handful of studies with transparent and well-defined methodologies have generally used data and modeling techniques with narrow geographic and market scope. While these studies have found positive regional economic impacts, the impacts have been modest, and many economic aspects of local food sys… Show more
“…If modification of IMPLAN is needed (Module 7), it is necessary to budget for adequate time and resources and to have a representative sample of vendors willing and able to provide the needed data. Future directions of research in this area may focus on developing better, more efficient, and less burdensome methods to gain primary data (O'Hara & Pirog, 2013), by increasing producer participation and overcoming survey fatigue. As one of the first pilots of the USDA AMS toolkit, we hope our study motivates further investigation of the economic impacts of local food systems and encourages collaboration to improve methods and results.…”
Section: Resultsmentioning
confidence: 99%
“…Secondary data sources may not exist or may inadequately reflect conditions in the field, yet primary data collection is time-consuming and difficult and methods are not well established (Conner et al, 2013;O'Hara & Pirog, 2013). Moreover, many economic impact studies rely on faulty assumptions and tend to overstate economic impact, and proponents are often eager to tout these studies to support their positions (Eathington & Swenson, 2007;Swenson, 2006b).…”
In this paper we report the results of a field test of an economic impact toolkit recently commissioned by the U.S. Department of Agriculture (USDA). The toolkit was created as a guide for food systems organizations to frame issues and collect and analyze data in order to credibly measure economic and other benefits of their initiatives. To test the toolkit, we applied it to an economic contribution study of a local food-buying program in a large regional hospital in Vermont. Our findings indicate that by working with a dedicated and motivated community partner, we were able to agree on the scope and objectives of the project, obtain highquality data, and enter these data into an inputoutput model to measure broader economic contributions (Modules 1 though 6 of the toolkit). We experienced difficulty, however, in obtaining data from a sufficient number of the hospital's vendors to modify the model from its default settings to better reflect local food system actors' purchase patterns (the subject of Module 7). Our experience suggests that practitioners need to work with community partners and consider which stages of the analysis meet their project objectives; in particular, they should focus on the difficulty and expense of incorporating Module 7. Our implications focus on strategies for decreasing the cost and effort of data collection for Module 7.
“…If modification of IMPLAN is needed (Module 7), it is necessary to budget for adequate time and resources and to have a representative sample of vendors willing and able to provide the needed data. Future directions of research in this area may focus on developing better, more efficient, and less burdensome methods to gain primary data (O'Hara & Pirog, 2013), by increasing producer participation and overcoming survey fatigue. As one of the first pilots of the USDA AMS toolkit, we hope our study motivates further investigation of the economic impacts of local food systems and encourages collaboration to improve methods and results.…”
Section: Resultsmentioning
confidence: 99%
“…Secondary data sources may not exist or may inadequately reflect conditions in the field, yet primary data collection is time-consuming and difficult and methods are not well established (Conner et al, 2013;O'Hara & Pirog, 2013). Moreover, many economic impact studies rely on faulty assumptions and tend to overstate economic impact, and proponents are often eager to tout these studies to support their positions (Eathington & Swenson, 2007;Swenson, 2006b).…”
In this paper we report the results of a field test of an economic impact toolkit recently commissioned by the U.S. Department of Agriculture (USDA). The toolkit was created as a guide for food systems organizations to frame issues and collect and analyze data in order to credibly measure economic and other benefits of their initiatives. To test the toolkit, we applied it to an economic contribution study of a local food-buying program in a large regional hospital in Vermont. Our findings indicate that by working with a dedicated and motivated community partner, we were able to agree on the scope and objectives of the project, obtain highquality data, and enter these data into an inputoutput model to measure broader economic contributions (Modules 1 though 6 of the toolkit). We experienced difficulty, however, in obtaining data from a sufficient number of the hospital's vendors to modify the model from its default settings to better reflect local food system actors' purchase patterns (the subject of Module 7). Our experience suggests that practitioners need to work with community partners and consider which stages of the analysis meet their project objectives; in particular, they should focus on the difficulty and expense of incorporating Module 7. Our implications focus on strategies for decreasing the cost and effort of data collection for Module 7.
“…A thorough review of the literature, shows a problem of access to data that prevent a comprehensive assessment, both qualitative and quantitative, of the benefits assigned to SFSCs (Sonnino and Marsden, 2006;Martinez et al, 2010;Kneafsey et al, 2013;O'Hara and Pirog, 2013). Most studies on SFSCs are based on case studies that are restricted to a particular region.…”
“…DM production is closely aligned with Lyson's definition of civic agriculture, which caters to local markets selling products direct to the end consumer; is central to rural communities; focuses on quality over quantity; is more labor-and land-intensive and smaller in scale; and uses site-specific knowledge and practices [21]. DM agriculture as a sector can be considered an economic development strategy, as economic studies have demonstrated the impact of DM farms on local economies, with larger economic multipliers for DM production as compared to other agricultural production and increasing local economic activity by attracting shoppers to businesses near farmers markets [23,[28][29][30][31][32]. The environmental benefits of DM production across the landscape stems from its use of diverse crops and production practices that include integrated pest management, hedgerows, cover crops, and other measures to encourage agricultural and natural biodiversity.…”
Abstract:We conducted interviews with 18 direct market (DM) farmers to explore the implications of the Oregon minimum wage (MW) increase for the state's DM agricultural sector. How, if at all, will DM farms in the Willamette Valley (OR, USA) adjust their production and marketing practices in response to the MW increase? How will these adjustments affect DM farm viability, farmworkers, the environment, and the communities in which the farms are embedded? This region has a vibrant food system with many small-to-mid sized, diversified farms that sell through direct and intermediated marketing channels. The diversified production and marketing practices of these DM farmers are labor intensive and, in many respects, environmentally friendly. These practices result in relatively high costs and the farmers' ability to respond by increasing prices is constrained by mainstream retail prices. Most growers reported that they will adjust to the MW increase by reducing their production and marketing costs with a decrease in total labor hours being an important strategy. This study, while small and exploratory, is the first in Oregon (and perhaps nationally) to collect empirical farm-level data about how DM farms will adjust to a MW increase. It sets the stage for future research.
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