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The number of food hubs—businesses that aggregate and distribute local food—in the United States is growing, fueled in part by increasing public support. However, there have been few data-driven assessments of the economic impacts of these ventures. Using an input-output-based methodology and a unique data set from a successful food hub, we measure net and gross impacts of a policy supporting their development. We estimate a gross output multiplier of 1.75 and an employment multiplier of 2.14. Using customer surveys, we estimate that every $1 increase in final demand for food hub products generates a $0.11 reduction in purchases in other sectors.
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Assessing the Economic Impacts of Food Hubs on Regional
Economies: A Framework that Includes Opportunity Cost
B. B. R. Jablonski, T. M. Schmit and D. Kay
Agricultural and Resource Economics Review / Volume 45 / Issue 01 / April 2016, pp 143 -
DOI: 10.1017/age.2016.9, Published online: 29 April 2016
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How to cite this article:
B. B. R. Jablonski, T. M. Schmit and D. Kay (2016). Assessing the Economic
Impacts of Food Hubs on Regional Economies: A Framework that Includes
Opportunity Cost. Agricultural and Resource Economics Review, 45, pp 143-172
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Assessing the Economic Impacts of
Food Hubs on Regional Economies:
A Framework that Includes
Opportunity Cost
B. B. R. Jablonski, T. M. Schmit, and D. Kay
The number of food hubsbusinesses that aggregate and distribute local foodin
the United States is growing, fueled in part by increasing public support. However,
there have been few data-driven assessments of the economic impacts of these
ventures. Using an input-output-based methodology and a unique data set from a
successful food hub, we measure net and gross impacts of a policy supporting
their development. We estimate a gross output multiplier of 1.75 and an
employment multiplier of 2.14. Using customer surveys, we estimate that every
$1 increase in nal demand for food hub products generates a $0.11 reduction in
purchases in other sectors.
Key Words:economic impact assessment, food hub, food policy, food systems,
local food
As policymakers, researchers, and practitioners seek new opportunities to
support rural communities and agri-businesses, interest in local food systems
B. B. R. Jablonski is an Assistant Professor in the Department of Agricultural and Resource
Economics at Colorado State University. T. M. Schmit is an Associate Professor in the Dyson
School of Applied Economics and Management at Cornell University. D. Kay is a Senior
Extension Associate for the Community and Regional Development Institute in the Department
of Development Sociology at Cornell University. Correspondence: B. B. R. Jablonski Department
of Agricultural and Resource Economics Clark B 337 Colorado State University Fort Collins,
CO 80523 Phone: +1.970.491.6133 Email
This work was supported by Cooperative Agreement 12-25-A-5568 with the Agricultural
Marketing Service of the U.S. Department of Agriculture (USDA), Competitive Grant 2012-
67011-19957 from the USDA National Institute for Food and Agriculture, and Grant GNE11-021
from the Northeast Region Sustainable Agriculture Research and Education Program. The
authors thank Jim Barham (our original cooperator at the USDA Agricultural Marketing
Service), as well as Dana Staord and Regional Access, Inc. for access to the nancial data
necessary to complete the analysis, along with their vendor farmers and customers who agreed
to be interviewed/surveyed for this research. Additionally, we recognize Molly Riordan and Dan
Moran, graduate students in the Cornell University Department of City and Regional Planning,
for their help with interviews of vendors and surveys of customers.
The views expressed are the authorsand do not necessarily represent the policies or views of
any sponsoring agencies.
Note that the U.S. Department of Agriculture does not have a denition for local food,
acknowledging that it is complex and varies with the purpose, geography, and data availability
(Low et al. 2015).
Agricultural and Resource Economics Review 45/1 (April 2016) 143172
© The Author(s) 2016. This is an Open Access article, distributed under the terms of the Creative
Commons Attribution licence (, which permits
unrestricted re-use, distribution, and reproduction in any medium, provided the original work is
properly cited.
continues to grow (Clancy 2010, Jensen 2010,Kingetal.2010, Low et al. 2015,
OHara and Pirog 2013, National Research Committee on Twenty-rst Century
Systems Agriculture 2010). The role of small-scale and medium-scale producers
in developing local and regional food systems has also attracted renewed
attention as their importance in supplying alternative food markets has gained
recognition (Low and Vogel 2011). Despite the purported potential for local
food systems to increase farm sales, particularly for small-scale and mid-scale
producers, and support rural economic development, the U.S. Department of
Agriculture (USDA) acknowledged a lack of distribution systems for moving
local foods into mainstream markets(Martinez et al. 2010, p. iv) as posing a
barrier to eorts to scale uplocal food and meet consumer demand.
Access to markets that provide positive returns on investment is increasingly
dicult for small and mid-sized farms as supply chains become more vertically
integrated and consolidated. Large-scale supermarket retail and wholesale
operations demand large volumes, low prices, and consistent quantities and
qualities that meet increasingly strict safety standards, and the procurement
systems in such markets are often vertically and horizontally integrated,
global in scale, and aimed at maximizing eciency (King et al. 2010, Richards
and Pofahl 2010, Sexton 2010, Tropp, Ragland, and Barham 2008).
In their eorts to support smaller-scale producers and foster opportunities
for rural development, public agencies and private foundations are
increasingly nancing and promoting development of food hubs:
Business(es) or organization(s) that actively manage the aggregation, distribution, and
marketing of source-identied food products primarily from local and regional producers
to strengthen their ability to satisfy wholesale, retail, and institutional demand. (Barham
et al. 2012,4)
For example, the Food Hub Collaboration is a public-private eort between
USDA, the Wallace Center at Winrock International, the National Good Food
Network (NGFN), the Farm Credit Council, and other organizations that work to:
Ensure the success of existing and emerging food hubs in the United States by building
capacity through connection, outreach, research, technical assistance, and partnership.
(NGFN 2010)
Similarly, USDAsKnow Your Farmer, Know Your Food(KYF2) task force has
established a regional subcommittee to address food hubs. That committee
prepared a list in 2011 of fteen agency programs that provided funding to
support food hubs, including Rural Development, USDAs Agricultural
Marketing Service (AMS), the National Institute of Food and Agriculture, the
Farm Service Agency, the Natural Resources Conservation Service, and USDAs
Risk Management Agency (KYF2 Regional Food Hub Subcommittee 2011).
The Know Your Farmer, Know Your Foodtask force is a USDA-wide eort to carry out
President Obamas commitment to strengthening local and regional food systems (USDA 2013).
Agricultural and Resource Economics Review144 April 2016
Some state governments have also used public funds to support development of
food hubs; for example, in February 2013 the governor of New York announced
$3.6 million in state funding to support four new food hubs (Cuomo 2013). As of
2013, there were more than 220 self-reported food hubs across the United
States, an increase of 68 percent since 2008 (USDA 2013).
Despite this growing interest in food hubs and a burgeoning literature
describing food hub development (e.g., Abatekassa and Peterson 2011,
Barham 2011, Barham et al. 2011, Clancy and Ruhf 2010, Conner et al. 2011,
Day-Farnsworth and Morales 2011, Diamond and Barham 2011, Feenstra
et al. 2011, Hardesty et al. 2014, Jablonski, Perez-Burgos, and Gomez 2011,
Schmidt et al. 2011, Stevenson and Pirog 2008), no comprehensive, data-
driven assessments of the economic impact of such hubs have been
completed to date. Nor is there an agreed-upon methodology for conducting
such economic assessments (OHara and Pirog 2013).
Analyses of economic impacts model and measure the economic activity
associated with the sequential eects of linked purchases. An (exogenous)
nal demand-driven change in food hub goods and services triggers changes
in levels of production in other industry sectors in the economy. Of key
importance for policy is the extent to which food hubs increase overall
demand for and consumption of locally grown agricultural products versus
diverting farm sales from an existing local market (e.g., a farmersmarket) to
the hub. Additionally, policymakers need to understand the percentage of the
sales price typically retained by the farmer from sales to a food hub versus
sales to other market outlets. Policymakersprimary interest in food hubs
relates to their potential to support economic development and, in particular,
to increase the viability of the local farm sector.
This analysis specically addresses whether increases in nal demand for
goods and services from a food hub divert sales from other industry sectors
(e.g., wholesale trade), leading to a beggar thy neighborphenomenon (Boys
and Hughes 2013, Thilmany et al. 2005), or increase farm protability. We
explicitly account for where businesses would have purchased products if the
food hub did not existor, as we dene it, the opportunity cost. Attention to
this type of impact can better inform public and private programs that
support food hub initiatives.
Eorts to assess the impacts of local food systems are often complicated by a
lack of data identifying major inter-industry sales and purchasing links among
the systems participants. Those data insuciencies are exacerbated when
evaluating food hubs because the hubs are not considered to be a separate
sector; inter-industry links must be identied from data on existing industry
sectors in the economy.
The primary objective of this study is to demonstrate the value of a replicable
methodology by which to evaluate the economic impacts of an increase in nal
demand for food hub goods and services on local economies and participating
farms. This is accomplished by developing a data-driven empirical framework
that is applicable to a variety of food hub structures. Since USDA distinguishes
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 145
food hubs from other traditional food aggregators and distributors in part based
on their purchases being primarily from local and regional producers(Barham
et al. 2012, p. 4), variations in the spending patterns of hubs and more traditional
operations can be modeled to determine their relative eects on the regional
economy, including impacts on local agricultural sectors.
We apply the resulting framework to a case study of a food hub in New York.
There are limits to the ability to generalize the results of an individual case
study to other food hubs, but we can use adjusted expenditure patterns in
contexts in which other food hubs exhibit similar attributes (i.e., perform
similar types of business functions and/or serve a similar number of farms
or similarly scaled markets). When food hubs are more dissimilar in terms of
activities and purchasing and sales patterns, a more comprehensive data set
is advised.
Our secondary objective is to better understand the extent to which food hubs
increase overall demand for and consumption of local food products. We collect
additional information on the nature of purchases of outputs from food hubs to
determine whether those purchases represent increased demand for local
goods and services or simply represent substitution of purchases previously
made through an existing local source such as a conventional wholesale
This additional information allows us to ascertain the direct
value of food hub purchases, osets of purchases from other sectors, and the
potential of the hub in our case study to expand overall demand for local
food products.
Assessments of the Economic Impacts of Local Food
Most prior assessments of the economic impacts of local food used regional
input-output (IO) models with data and software generated by IMPLAN
A handful of studies measured the impacts of specic
marketing channels, such as farmersmarkets (e.g., Henneberry, Whitacre,
and Agustini 2009, Hughes et al. 2008, Myles and Hood 2010, Otto and
Varner 2005, Sadler, Clark, and Gilliland 2013), farm-to-school programs
We dene a conventional wholesale food distributor as a company that participates in a
commodity food chain. As described in a 2013 USDA Rural Development report (Matson,
Sullins, and Cook 2013, p. 4), a commodity food chain is one in which agricultural products are
mixed together and combined or aggregated into larger groups to be sold, usually with no
identication of the farm where they were grown.By preserving the identity of the source of
the agricultural products, a hub can distinguish itself from conventional wholesale food-
distribution companies.
Technically, we incorporate our analysis into a regional IMPLAN social accounting matrix. A
typical social accounting matrix maps each household into a functional category, usually based
on household income classes; however, the IMPLAN matrix does not serve that purpose except
under restricted conditions (see Alward and Lindall 1996).
Agricultural and Resource Economics Review146 April 2016
(Gunter and Thilmany 2012, Tuck et al. 2010), and key pieces of infrastructure
such as smaller-scale meat processing facilities (Swenson 2011).
These studies demonstrate the two main challenges generated by the lack of
adequate data. The rst is what OHara and Pirog (2013, p. 4) referred to as an
interpretation challenge: stipulating how the opportunity cost’… is dened.As
they rightly pointed out, measuring opportunity cost is not a straightforward
task. In our case, it requires information about the extent to which increased
consumer purchases of locally grown food oset other types of purchases,
change market prices and/or characteristics of the supply chain, and impact
land use. Only a handful of economic-impact assessments of local food
systems have explicitly acknowledged the need to consider opportunity costs
(Conner et al. 2008, Hughes et al. 2008, Gunter and Thilmany 2012, Tuck
et al. 2010, Swenson 2010). Those studies made assumptions about the
sectors in which purchases decreased (or land use changed) in response to
increases in consumption of local food. In other words, none collected the
data necessary to comprehensively measure the opportunity costs associated
with increasing local purchases.
The second challenge is the implicit assumption used in most of the studies
(except Gunter and Thilmany (2012) and Schmit, Jablonski, and Mansury
(2013)) that spending patterns of participants in local food systems are
essentially identical to spending patterns for the aggregated agricultural
sectors provided by IMPLAN. Each IMPLAN sector is represented by a single,
static production function that reects the average purchase pattern for all of
the rms in the sector. Information needed to disaggregate a purchase
pattern by a specic characteristic (i.e., scale of operation or marketing
channel) is not available (Lazarus, Platas, and Morse 2002, Liu and Warner
The IMPLAN expenditure and sales data more accurately reect rms
that contribute a relatively large proportion of total output in the sector
(typically large rms) (Lazarus et al. 2002). Since participants in local food
systems usually are smaller in scale and represent a relatively small portion
of the overall agricultural-sector transactions (Low and Vogel 2011),
estimates of impacts based on existing IMPLAN data may be misleading if
participants in a local food system vary from larger, more commercial
operations in how they spend for inputs and where they buy them. Since one
of the distinctive denitional attributes of food hubs is that they more often
While computable general equilibrium models provide an alternative approach to
endogenously modeling changes in things like prices or land use through assumed or estimated
change parameters, a search of the literature found no studies that used such an approach to
assess the impacts of local food systems. IO and social accounting matrix models accommodate
the opportunity cost through a more ad-hoc analysis and assumptions that are external to the
model itself.
For an in-depth discussion of how production functions are constructed in IMPLAN, see
Lazarus et al. (2002).
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 147
purchase regional inputs, we expect to nd dierences between large-scale
operations and food hubs; the extent of such dierences is unknown.
A limited number of studies of the impacts of local food systems have
disaggregated key sectors and augmented the IMPLAN data by collecting
primary data on expenditure patterns. Gunter and Thilmany (2012) used a
combination of survey data and National Agricultural Statistics Service data
to create a customized farm-to-school farm sector in IMPLAN to reect the
dierential production function of farm-to-school producers. Schmit,
Jablonski, and Mansury (2013) collected detailed data on expenditures and
sales from farms in upstate New York and found that the spending patterns
of small and mid-scale farms participating in direct-to-consumer markets
were dierent from the patterns depicted in the default data for the
agricultural sector in IMPLAN. They concluded that assessments of the
economic impacts of local food that use the default IMPLAN agricultural
sectors to estimate economy-wide impacts may underestimate the impacts of
labor income and value added contributions. Swensons(2011) study is the
only one of its kind to provide evidence that farms are not the only
participants in local-food supply chains that are not well represented by
default IMPLAN sectors. His research on the small-scale meat-processing
sector in Iowa demonstrated that expenditure patterns dier based on the
scale of the operation and thus pointed to the inability to obtain true
estimates of impacts when using the default IMPLAN sector data to describe
infrastructures required by participants in local food systems, which are
typically smaller in scale than the average operation reected in the IMPLAN
Empirical Framework
To assess the impacts of food hubs using a regional IO framework like IMPLAN,
we must dene the industry sectors of interest and their links with other
industries. This is not straightforward since traditional data sources such as
IMPLAN do not separately dene a food hub sector and its transactions.
Additional information is required.
Formally, we do not create a single aggregated food hub sector for the
analysis. Instead, we model the impacts of the food hub sector by allocating
the hubs expenditures (associated with revenue resulting from nal
demand for its output) to its input suppliers (including regional farms),
employees, and owners. This is an analytically equivalent alternative known
as analysis by parts.
Conceptually, the allocated expenditures represent the
rst round of indirect inter-industry purchases and payments to value
added by the food hub, and each purchase or payment triggers additional
For more information on analysis by parts, please refer to the IMPLAN website: www.implan.
Agricultural and Resource Economics Review148 April 2016
indirect and induced eects. Dening the scope of a food hub in IMPLAN
therefore requires detailed data on the hubs annual outlays, including (i)
purchases of input commodities and the proportion of those expenditures
made within the dened local economy, (ii) payments for value added
components, and (iii) other institutional purchases (e.g., payments to
households and government spending).
In addition, one should consider whether the default IMPLAN production
functions (i.e., technical coecients) associated with sectors from which the
hub purchases products adequately represent the production technologies
(input combinations) of those rms. If not, additional information will be
required from rms representing the upstream sectors. This concern is
perhaps most acute for farm production sectors that supply food products to
a hub. Are the farms that sell food to a hub adequately represented by the
default farm data? To assess the size and implications of any dierences, we
construct two alternative modelsone that incorporates additional data
collected from farms selling to the hub and one that does not.
Expenditure categories from the data for the hub must be mapped to
appropriate industry, value added, and institutional sectors in IMPLAN. We
start by dening industries using the two-digit North American Industry
Classication Systems (NAICSs) aggregation scheme in IMPLAN and leave
sectors of particular interest or importance to food hubs disaggregated. We
then create a separate aggregated farm-product sector that includes only the
sectors from which the hub purchases food products.
Similarly, we separate
processed food and beverage products the hub purchases from nonfarm
manufacturers for resale using the NAICS scheme and consolidate them into
a manufactured-food sector.
Dening Farms that Sell to Food Hubs
Understanding how farms that sell to food hubs interact with other sectors of
the economy is important in improving the precision of an impact
assessment. While the same can be said of any input-supplying sector,
purchases from farms generally represent a relatively large share of a hubs
total expenses. Moreover, we are particularly interested in how such farms
are aected by the hub given the role the interaction may play in rural/
regional economic development. Therefore, we devote special attention to
inter-industry links for farm suppliers.
For most of the nonfarm businesses that supply inputs, it is sucient for our
purposes and consistent with standard practice to assume that an individual
We dene the farm-product sector as including production of oilseeds, grains, vegetables,
melons, fruits, greenhouse and nursery products, oriculture products, all other crops, beef,
dairy cattle, milk production, poultry, egg production, and all other forms of animal production.
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 149
businessexpenditure patterns reect the pattern for the entire industry sector.
For example, a food hub is unlikely to purchase insurance from a specialty food
hub insurance provider. Consequently, assuming in IMPLAN that the production
function for the food hubs insurance company is similar to the production
function for the regions insurance sector is reasonable given the diculty
and cost of collecting necessary data. The same cannot be said of farms.
Recent studies have found that farms that participate in local food systems
are often (though not exclusively) smaller in scale and/or have patterns of
expenditures and labor requirements per unit of output that are not reected
by IMPLANs default agricultural sectors (Jablonski and Schmit 2015, Schmit,
Jablonski, and Mansury 2013).
Dening a separate food hub farm sector requires data on the farms outlays
that are analogous to the data required for the hubthe value and location
(local and nonlocal) of payments made by the farm to each industry sector
and value added component.
An Impact Analysis that Considers the Opportunity Cost
We consider a scenario in which an exogenous shock (e.g., a federal incentive to
local school districts to expand purchases of locally grown food) increases nal
demand for a food hubs products and services. Given the absence of a discrete
food hub sector, the increase in nal demand must be fully allocated according
to the food hubs expenditure pattern. However, only a portion of the food hubs
expenditures to satisfy that increase in nal demand occur locally, and thus,
only purchases from local rms are included in the impact analysis (as rst-
round indirect eects).
In addition to a hypothetical positive shock via an increase in demand for hub
products, we consider potential negative impacts associated with decreased
spending in other sectors. We hypothesize that a hubs sales will oset some
local purchases that otherwise would have been made from existing
distributors. At the same time, though, consumersgreater purchases of local
products could be motivated by a hubs specialized marketing eorts and
resulting availability of its products and services. Consumers likely would
have greater awareness of and access to a set of goods that is dierentiated
from the goods available from other types of distributors (i.e., they have
more options when purchasing local goods). The net eect is indeterminate a
priori, but we can estimate it empirically.
To test this hypothesis and more fully reect the impact that increased
demand for food hub products has on other sectors, we collect two types of
information from the hubs customers: (i) the percentage who would have
purchased products from other local sectors had the hub outputs not been
available and (ii) for customers who purchased less product from other
sectors when making hub purchases, the amount of their reduction in
purchases from the other sectors.
Agricultural and Resource Economics Review150 April 2016
Case Study Application
Given the heterogeneous structure of food hub operations and need for detailed
data for impact assessments, we use a case study. This is a preliminary case
study and is not intended to provide denitive evidence of the economic
impacts of food hubs. Rather, the objective is to test our newly developed
framework and provide a base case study that can be used for comparison in
future studies.
We chose Regional Access, LLC (RA), a food hub based in New York, as the
subject of our case study because it ts within USDAsdenition of a regional
food hub (an aggregation and distribution business that is committed to
supporting local farmers and preserving identication of the source of its
products). In addition, RAs years in operation, diverse customer base, and
size make it a particularly interesting hub to examine. RA was established in
1989. By 2011, it had more than $6 million in sales annually and 32
employees. Using nine vehicles and a 25,000-square-foot warehouse, RA
aggregates and delivers products throughout New York. Its oerings cover
more than 3,400 products that include beverages, breads, cereals, ours,
meats, produce, prepared foods, grains, fruits, and vegetables. The hub
purchases products directly from 96 farm vendors, 65 specialty food
processors (nonfarm vendors), and several larger-scale food-service
distributors. Its more than 600 customers include individual households,
restaurants, institutions (such as schools), other distributors, fraternities and
sororities, buying clubs, retailers, manufacturers, and bakeries. RA also
provides freight services to a range of businesses.
We recognize that dening localis an important decision for this type of
study and not a one-size-ts-all approach. RA works primarily with farms
and customers in the state of New York, and, accordingly, references to local
in our empirical application refer to New York.
Deriving Patterns of Food Hub Expenditures
The basis of our data is information provided by RAa detailed 2011 prot and
loss statement and estimates of the amount expended locally in 2011 in each
category. Based on that data and follow-up discussions with RA personnel,
we mapped the hubs expenditures to IMPLAN sectors, value added
categories, and institutional components (see appendix 1 for details
regarding the IO sector aggregation and mapping scheme).
After assigning all of RAs expenditures to appropriate categories, we
determined the percentage of the hubs purchases devoted to each category
in total and from local and nonlocal suppliers. The ten largest expenditure
categories and a combined othercategory are shown in Figure 1. The two
largest are manufactured food (44 percent) and farm products (18 percent).
Together, RAs purchases of manufactured food and farm products represent
the cost of goods sold. Interestingly, RAs cost of goods sold is nearly
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 151
identical to the average reported by Fischer et al. (2013) based on data from
their national food hub survey (61 percent). A study of food hubs by the
Farm Credit Council and Farm Credit East (2013) reported an average cost of
goods sold of 68 percent and a study of food distributors by the Food
Marketing Institute (2008) reported an average of 71 percent.
RAs third largest expenditure item is employee compensation (16 percent).
The Farm Credit Council/Farm Credit East study of food hubs (2013)
reported that the average labor cost was 17 percent of sales. The Food
Marketing Institute (2008) reported total payroll and employee benets of
15 percent of total expenditures.
Figure 1 depicts RAs expenditures for each category as a percentage of its
total expenditures that is broken down into the local and nonlocal components.
The data show that 54 percent of all of RAs expenditures were local.
The largest local expenditures were for farm products (16 percent), employee
compensation (16 percent), manufactured food (7 percent),
nance and
Figure 1. Top Regional Access Expenditures as a Percentage of Total
Expenditure by Local New York State and Nonlocal Components
We found RAs lack of local purchases of manufactured food to be particularly interesting.
According to RAs president, three conditions were primarily responsible for the nonlocal
expenditures: (i) RA cannot obtain many of the specialty manufactured products in New York
Agricultural and Resource Economics Review152 April 2016
insurance (4 percent), proprietor income (3 percent), and automotive equipment
rental and leasing (3 percent). As a point of comparison, Fischer et al. (2013)also
asked food hubs about the percentage of their expenditures that were local. They
did not provide an average for all expenditures but found that no expenditure
averaged less than 50 percent spent in-state(Fischer et al. 2013,p.34);on
average, 85 percent of the hubsfood and product purchases took place within
the state in which it operated.
Food Hub Farms
For this study, we conducted in-person interviews with operators of 30 of the
86 farms in New York that sold products to RA (35 percent).
The farms
were located in every region of the state except New York City and Long
In terms of size, 50 percent of the respondents classied their
operations as small (annual gross sales of $1,000$249,999), 20 percent as
medium ($250,000$500,000), and 30 percent as large (more than
$500,000). When asked to classify their farmsprimary production category,
37 percent reported meat and livestock, 30 percent reported fruits and
vegetables, and 33 percent reported processed food products.
Table 1 presents the average expenditures reported by participants in the
interviews with local/nonlocal breakdowns. Per farm, the average total
expenditure was $601,110, 80 percent of which was spent in the local
economy ($483,741). In terms of categories, the largest percentage of total
expenditure was for employee compensation (24 percent), followed by farm
products (17 percent, representing intra-sector purchases of farm products
from other farms), nonfood manufactured goods (16 percent), and support
activities for agriculture and forestry (9 percent).
IMPLAN Model Construction
Using 2011 IMPLAN data, we construct two models for New York.
Both use
the data collected from RA about its sales and expenses. Model 1 assumes
but can purchase them elsewhere; (ii) many of the manufactured products are purchased from
New-Jersey-based companies and so, according to our denition, are nonlocal; and (iii) RA
obtains better bulk-pricing options from companies outside the state.
A copy of the interview protocol is available upon request.
Empire State Development has dened ten regions in New York: Western New York, Finger
Lakes, Southern Tier, Central New York, Mohawk Valley, North Country, Capital District, Mid-
Hudson, New York City, and Long Island. For more information, see
When a farm classies its primary production category as processed food products, we can
infer that the farm grew/raised a raw commodity that it then processed. Examples of processed
food products include cheese, butter, yogurt, honey, maple syrup, wine, and juice.
After aggregating the models, we exported the IMPLAN social accounting matrix of industry-
by-industry transactions to Microsoft Excel 2010. Margining, disaggregation of the default
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 153
Table 1. Average Expenditures per Year for Food Hub Farms by Total and
Local New York State Expenditures
Expenditure Category Total Local
Employee compensation (value added) $141,644 $141,644 100
Farm products $102,884 $95,282 93
Manufacturing (other) $99,089 $16,330 16
Support activities for ag and forestry $51,496 $47,377 92
Tax on production and imports (value added) $33,694 $33,694 100
Proprietor income (value added) $31,913 $31,913 100
Transportation and warehousing $24,755 $19,821 80
Wholesale trade $17,768 $9,349 53
Finance and insurance $15,403 $13,106 85
Other property type income (value added)
$14,334 $11,467 80
Construction $14,143 $13,980 99
Retail trade (other) $11,281 $9,360 83
Utilities $10,901 $10,901 100
Real estate and rental (other) $8,604 $8,604 100
Manufactured food $7,843 $5,872 75
Professional scientic and technical services $5,690 $5,569 98
Automotive and machinery repair and
$5,646 $5,646 100
Information $1,864 $1,793 96
Administrative and waste services $1,217 $1,217 100
Other services (other) $941 $817 87
Total $601,110 $483,741 80
Farm capital expenditures were allocated to other property type income (value added) following
IMPLANsdenition of other property type income to include a capital consumption allowance.As
we render other property type income exogenous in our model, its level of local expenditure does not
contribute to the multiplier impact.
Notes: Expenditure categories follow the model aggregation scheme in appendix 1 (industrysectors) and
include value added components where indicated. Average food hub farm purchases from RA were
$6,398 for freight service, other farm products (e.g., products for resale at a farm stand), and
warehousing and storage services. These expenditures were mapped evenly between the wholesale
trade and the transportation and warehousing industry sectors.
agricultural sector, and all of the computations that followed were conducted in Excel. This work
can be done in IMPLAN, but we determined that completing the work in Excel was more
Agricultural and Resource Economics Review154 April 2016
that the production function and local purchase coecients for the farms in the
study and for default farm products in IMPLAN are the same (i.e., we do not use
any data for the hub farms). Model 2 uses information provided in the
interviews to assign data for the default farm-product sectors to a food hub
farm sector or an other-farm sector.
In both models, the expenditures are margined in IMPLANs retail trade and
wholesale trade sectors, and the technical coecients are adjusted accordingly.
Specically, in our aggregation scheme, three sectors of RA expenditures require
margining: retail store-gasoline stations, wholesale trade, and other retail
trade. To account for margining in retail store-gasoline stations (sector 326),
we apply IMPLANs margin of 14.5 percent to the total retail fuel purchases.
Consequently, $54,438 is included in retail store-gasoline stations. The
balance, $320,998, is mapped to the production sector (petroleum reneries,
sector 115), and the local purchase percentage is taken from IMPLAN for that
sector (i.e., we multiply the local purchase percentage for petroleum
reneries by $320,998). The same approach is used for the other retail trade
and wholesale trade purchases.
After aggregating the relevant sectors and
accounting for margining, model 1 is complete.
Model 2: Creating a Food Hub Farm Sector
For model 2, we use the data on food hub farms to apportion transactions in
the farm-product sector to two subsectors: food hub farms and other farms.
The rst step in separating the food hub farm sector is to determine the total
size of the RA-farm sector in New Yorkeectively, calculating totals for a
new expenditure column and new sales row in the regional IO matrix. The
estimated average expenditures per farm from the interview data are multiplied
by the total number of RA farm vendors in the state (86).
The hub farmsexpenditures are then allocated to corresponding IMPLAN
sectors (with margining), and the same amount is subtracted from the
default farm-product sector to calculate the other-farm sector. This procedure
ensures that the size of the overall economy remains the same.
The farm operators identied both food products and goods and services
purchased from RA (e.g., food product resales, transportation, warehousing,
and wholesaling). We assign equal shares of those expenditures to IMPLANs
warehousing and wholesale trade/transportation sectors. Additionally, we
allocate the dierence between average sales and annual expense per farm
($601,110 $569,167 ¼$31,913) as payments to owners (proprietor income
within IMPLAN).
IMPLANs retail and wholesale margining is based on national data and varies by year.
In a regional IO framework, there is an accounting identity for which the value of total outlays
for each sector must equal the value of total output.
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 155
The food hub farmsoutput (or sales) must be similarly disaggregated into a
vector of sales (using regional IO row transactions). Average annual sales per
farm ($601,110) are allocated to sales to other farms, sales to households,
intermediated sales not to RA, intermediated sales to RA, and sales to
commodity markets (Table 2). The average amount sold to each group is
then scaled up by the size of the sector (86 farms). Sales designated by the
respondents as nonlocal are allocated to domestic trade (regional exports),
direct-to-consumer sales are assigned to households (treated as sales directly
to households), non-RA intermediated sales are assigned to the aggregated
manufactured-food sector, and RA intermediated sales are evenly
apportioned to the sectors in which RA sells products. Those sectors include
accommodation and food service, wholesale trade, education, manufactured
food, retail trade, and health and social services. Sales to commodity markets
are allocated to other manufacturing, and sales to other farms are assumed
to be intra-industry sales and are mapped to the food hub farm sector.
The results of this mapping process, presented in Table 3, show that food hub
farmsexpenditure patterns are quite dierent from those of the default farm-
product sector in IMPLAN. An important nding in terms of local economic
impacts is that the food hub farms spend an average of $0.80 per dollar of
output in the local economy versus an average of $0.65 by the aggregated
farm-product sector. The variance comes primarily from dierences in
purchases of local intermediate inputs; food hub farms spend $0.44 per
dollar of output while the traditional farm-product sector spends only $0.25.
However, the default sector spends slightly more per dollar of output for
value added components, $0.40 versus $0.36.
The food hub farms in our sample spend twice as much, on average, as the
default sector on employee compensation per dollar of output ($0.24
compared to $0.12) but allocate substantially less to proprietors income
($0.05 compared to $0.16). Combining these two value added components
results in very similar contributions to labor income per dollar of output
for the two sectors$0.30 for food hub farms and $0.29 for the default sector.
Notably, food hub farms spend $0.08 per dollar of output on support activities
for agriculture and forestry compared to just $0.02 by the default sector and
Even nationally, employment and earnings data are not collected on a commodity basis. The
Regional Economic Accounts program of the U.S. Bureau of Economic Analysis (BEA) estimates
county-level employment and income data, but those represent farm totals rather than
dierentiation by agricultural commodity (BEA 2014). As a result, IMPLAN has developed
procedures using a combination of the USDA Economic Research Services farm count by
commodity (as an indication of proprietors), employee compensation-to-output relationships
from the BEAs benchmark IO data (to obtain a rst estimate for wage and salary employment
by commodity), and application of the resulting U.S. relationships to state outputs to derive
state employment numbers. Given the data challenges, we are wary about making too ne a
point of the dierent value added component expenditures.
Agricultural and Resource Economics Review156 April 2016
spend $0.16 per dollar of output on purchases from other local farms compared
to just $0.06 by the default sector.
Customer Survey
An online survey of RAs customers was used to understand the extent to which
purchases from RA increase demand for locally grown farm products and oset
purchases from other sectors.
At the time of the survey, RAs customers
consisted of 110 households and 547 businesses; of those, 57 households
(51.8 percent) and 186 businesses (34.0 percent) participated in the survey.
To improve the response rate for business customers, we conducted follow-
up telephone interviews with businesses that had not responded online. The
telephone interviews generated an additional 62 business participants,
increasing the total number of responses to 305 (46 percent).
RAs business customers are highly diverse. Reported annual gross sales
averaged $5.7 million (median $515,000) and ranged from $3,000 to $414
million. On average, they had been in business 13 years (median of 8 years)
but the reports ranged from less than a year to more than 130 years. The
average number of full-time employees was 15 (median of 4). In terms of
primary business function, 2 percent of respondents identied the business
as a distributor, 3 percent as a grocery/meal-delivery service provider, 9
percent as a processor/manufacturer, 11 percent as a wholesaler, 25 percent
as a restaurant, 34 percent as a retailer, and 17 percent as other (e.g., bakery,
fraternity/sorority house, caterer, coee shop, farmersmarket vendor, and
institutional cafeteria).
To analyze the impact of a positive shock to nal demand for food hub
products, we consider a simultaneous negative shock to the wholesale trade
sector to account for the opportunity cost as previously described. The
Table 2. Average Sales per Year for Food Hub Farms by Total and Local
New York State Sales
Farm Sales by Outlet Average Dollars in Sales Percent Local
Other farms 102,884 93
Direct to consumers (households) 144,173 100
Food hub (RA) 37,200 100
Other intermediated sales 279,701 84
Commodity sales 37,152 100
Total 601,110 91
A copy of the online customer survey is available upon request.
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 157
results of the customer survey revealed that, on average, 49.4 percent of RA
business customers reduced their purchases from other distributors because
of purchases made from RA; the average dollar-volume decrease was 23.1
percent. Therefore, for every $1.00 in positive nal demand shock for food
hub products, we concurrently apply a negative shock of $0.11 to the
wholesale trade (i.e., 0.494 ×0.231 ¼$0.114). The wholesale-trade sector was
chosen because business customers reported that they had decreased their
Table 3. Summary of Expenditures Per Dollar of Output for the Default
Agricultural Farm-products Sector and the Food Hub Farm Sector
Selected Industry Sector/Value added
Farm Products
Food Hub
Agriculture production
$0.056 $0.159
Support activities for ag and forestry $0.018 $0.079
Utilities $0.015 $0.018
Construction $0.005 $0.023
Manufactured food $0.002 $0.010
Manufacturing (other) $0.022 $0.027
Wholesale trade $0.015 $0.016
Retail trade (total) $0.001 $0.016
Transportation and warehousing $0.012 $0.033
Finance and insurance $0.035 $0.022
Real estate and rental (total) $0.055 $0.014
Professional scientic and technical services $0.006 $0.009
Automotive and machinery repair and
$0.001 $0.009
Other sector purchases $0.009 $0.006
Total intermediate input purchases $0.250 $0.441
Employee compensation $0.117 $0.236
Proprietor income $0.159 $0.053
Other property type income
$0.124 $0.019
Tax on production and imports
$0.007 $0.056
Total payments to value added $0.393 $0.364
Intermediate imports $0.356 $0.195
For the impact analysis, other property type income and tax on production and imports are rendered
Notes: Recall that model 2 disaggregates the farm-product sector from model 1 into two components: the
food hub farm sector and the other-farm sector. This table compares the expenditure patterns for the
original farm-product sector (model 1) to the food hub farm sector (model 2) to identify the nature of
the dierences in expenditures between the default IMPLAN data and the sample of food hub farms.
Agricultural and Resource Economics Review158 April 2016
purchases from other distributors, which are included in IMPLANs wholesale-
trade sector.
Results of the Impact Analysis
We consider a scenario in which an exogenous shock increases nal demand for
food hub products and services by $1 million. In model 1, all of RAs local farm-
product purchases are allocated to the default farm-product sector; in model 2,
RAs local farm-product purchases are allocated to the food hub farm sector
previously created.
Since the initial stimulus is hypothetical, the specic magnitude of the shock is
less important to our analysis than the relative size of the impacts in the aected
sectors. However, relatively large changes in nal demand could induce changes
in the production-function proles of the industries aected, including the prole
of the food hub. A $1 million increase in nal demand translates to a 16 percent
increase in RAs total output. We assume that RAs spending pattern (expense per
dollar of output) is not changed by the change in food hub demand. RAs
additional purchases of farm products ($163,923) represent only 0.31 percent
of the entire food hub farm sectors total pre-shock output, and its additional
purchases of manufactured-food products ($71,048) are less than 0.001
percent of total pre-shock manufactured-food output. Thus, assuming constant
production proles for the two sectors is reasonable.
Model 1: Multiplier, Employment, and Distributional Impacts
The results for model 1 (no separate food hub farm sector) are shown in
Table 4. They illustrate the combined indirect-plus-induced output eects
from the $1 million increase in nal demand for food hub products. For ease
of exposition, the sectors are listed by the magnitude of the impact (following
the aggregation scheme described in appendix 1). When we do not account
for the opportunity cost, the total combined indirect and induced output
eect is $683,642. Thus, when accounting for the direct eect of $1 million,
the total output eect is $1,683,642, a gross output multiplier of 1.68. That
is, for every dollar increase in nal demand for food hub products, an
additional $0.68 is generated in backward-linked industries. This result is
similar to the eect in sectors that conduct activities that are, at least in part,
similar to those of a food hub. For example, the output multipliers for
wholesale trade, truck transportation, and warehousing and storage are 1.60,
1.69, and 1.73, respectively. The total indirect multiplier eect of $0.46 and
total induced multiplier eect of $0.22 (not shown) indicate that most of the
multiplier eect is attributable to business-to-business transactions.
Table 5 reports the results of increased demand on employment. The direct
eect on employment is 5.19 jobs. The combined indirect-induced employment
eect in model 1 when not accounting for the opportunity cost is 4.46 jobs
2.86 from indirect eects and 1.60 from induced eects. Thus, the total
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 159
employment eect is 9.65 jobs.
Accordingly, the total-employment multiplier is
1.86, of which 0.55 is indirect impacts and 0.31 is induced impacts. As expected,
the employment impact is greatest for the farm-product sector at 1.52 jobs.
Table 4. Summary of the Impacts on Output of a One Million Dollar
Increase in Food Hub Final Demand in Model 1
Indirect and Induced Impacts (dollars)
Industry Sector
Opportunity Cost
Opportunity Cost
Agriculture production (farm products) $180,742 $180,606
Finance and insurance $89,424 $80,860
Real estate and rental (total) $83,706 $73,067
Manufactured food $78,588 $77,988
Health and social services $37,270 $30,319
Retail trade (total) $30,280 $25,914
Professional scientic and technical services $23,894 $16,265
Other services (total) $19,328 $17,045
Utilities $18,967 $17,261
Information $18,079 $14,435
Accommodation and food service $17,753 $15,264
Wholesale trade $17,534 $100,236
Manufacturing (other) $14,029 $12,246
Transportation and warehousing $11,222 $8,032
Administrative and waste services $8,962 $5,544
Management of companies $7,253 $4,901
Government $6,060 $4,698
Educational services $5,517 $4,471
Construction $5,280 $4,624
Arts, entertainment, and recreation $4,505 $3,576
Support activities for agriculture and forestry $3,199 $3,196
All other sectors $2,047 $1,934
Total $683,642 $502,011
Notes: Model 1 uses the default agriculture sectors in IMPLAN aggregated per appendix 1. For ease of
exposition, we order the results (indirect þinduced eects) by size of impact. The direct output eect
is $1 million for both cases, with and without opportunity cost (i.e., a concurrent negative impact on
wholesale trade of 11.4 percent or $114,000).
The direct employment eect was computed by multiplying RAs employment-output ratio
(32 / $6.16 million) by the direct output eect of $1 million.
Agricultural and Resource Economics Review160 April 2016
Now consider the results from model 1 when accounting for the opportunity cost.
In that case, the additional negative shock to the wholesale-trade sector ($114,000
for output and 0.58 jobs) results in a total indirect-induced eect of $502,011,
an output multiplier of 1.50 (Table 4), 4.42 jobs, and an employment multiplier
of 1.66. In other words, the indirect and induced eects decrease by more than
Table 5. Summary of the Impacts on Employment of a One Million Dollar
Increase in Food Hub Final Demand in Model 1
Indirect and Induced Impacts (jobs)
Industry Sector
Opportunity Cost
Opportunity Cost
Agriculture production (farm products) 1.517 1.516
Health and social services 0.414 0.337
Retail trade (total) 0.385 0.329
Finance and insurance 0.272 0.246
Real estate and rental (total) 0.268 0.242
Accommodation and food service 0.248 0.213
Other services (total) 0.240 0.213
Manufactured food 0.167 0.166
Support activities for agriculture and forestry 0.134 0.134
Professional scientic and technical services 0.132 0.090
Administrative and waste services 0.121 0.075
Transportation and warehousing 0.095 0.068
Wholesale trade 0.090 0.512
Educational services 0.076 0.062
Government 0.064 0.050
Arts, entertainment, and recreation 0.062 0.049
Information 0.048 0.038
Construction 0.042 0.037
Manufacturing (other) 0.030 0.027
Management of companies 0.029 0.020
Utilities 0.020 0.018
All other sectors 0.007 0.007
Total 4.462 3.423
Notes: Model 1 uses the default agriculture sectors in IMPLAN aggregated per appendix 1. For ease of
exposition, we order the results (indirect þinduced eects) by size of impact. The direct employment
eect is 5.19 jobs for both cases: RAs employment to output ratio (32/$6,163,720) multiplied by the
direct output eect of $1 million. The case with opportunity cost imposes a concurrent negative
impact on wholesale trade equivalent to 0.58 jobs.
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 161
26 percent for output and 23 percent for employment and the corresponding
multipliers drop by 11 percent. In short, opportunity costs matter.
Figure 2 supplements Table 4 by providing a visual representation of the
industry eects along with the contributions of the indirect and induced
components. Since the relative distribution across industries is similar for both
versions of model 1 (except, obviously, for the impact to wholesale trade), we
restrict our attention to the model that explicitly accounts for the opportunity
cost. The impact is greatest for the farm-product sectora $180,606 positive
change from the increase in nal demand that arises almost entirely from
indirect eects. The second-largest impact is in the nance and insurance
sector ($80,860, of which 69 percent is from indirect eects). The third-largest
is in the manufactured-food sector ($77,988, which is due almost entirely to
indirect eects), followed closely by real estate and rental (which includes
automotive equipment rentals and leases) ($73,067, of which 47 percent is
due to indirect impacts). The impact on the health and social services sector
ranks a distant fth ($30,319, nearly all due to induced impacts).
Model 2: Multiplier, Employment, and Distributional Impacts
Tables 6 and 7present the results for model 2 and are comparable to those in
Tables 4 and 5for model 1. In model 2, food hub purchases accrue to the new
Figure 2. Indirect and Induced Output Impacts of a One Million Dollar
Increase in Food Hub Final Demand for the Top Ten Impacted Sectors in
Model 1 with Opportunity Cost
Agricultural and Resource Economics Review162 April 2016
food hub farm sector rather than to the default farm-product sector. We nd
similar relative reductions in output and employment when the opportunity
cost is considered in model 2. However, relative to model 1, the impact of
indirect and induced output is 9.4 percent greater when opportunity cost is
not considered and 12.8 percent greater when it is (Table 6). Perhaps more
Table 6. Summary of Impacts on Output of a One Million Dollar Increase in
Food Hub Final Demand in Model 2
Indirect and Induced Impacts (dollars)
Industry Sector
Opportunity Cost
Opportunity Cost
Agriculture production (food hub farm þ
other farm)
$202,358 $202,222
Finance and insurance $89,695 $81,131
Real estate and rental (total) $80,621 $69,982
Manufactured food $80,513 $79,912
Health and social services $40,224 $33,272
Retail trade (total) $35,125 $30,759
Professional scientic and technical services $26,374 $18,745
Other services (total) $21,897 $19,615
Utilities $20,477 $18,771
Information $19,527 $15,884
Wholesale trade $19,226 $98,544
Accommodation and food service $18,614 $16,125
Manufacturing (other) $16,657 $14,873
Transportation and warehousing $16,402 $13,212
Support activities for agriculture and forestry $15,481 $15,477
Administrative and waste services $9,826 $6,407
Construction $9,089 $8,433
Management of companies $7,604 $5,252
Educational services $5,874 $4,828
Government $5,807 $4,445
Arts, entertainment, and recreation $4,802 $3,873
All other sectors $1,882 $1,769
Total $748,074 $566,443
Notes: Model 2 uses the dierentiated food hub farm and other-farm sectors based on the farm survey
data from the aggregate default agriculture sector in IMPLAN; see appendix 1. For ease of exposition, we
order the results (indirect þinduced eects) by size of impact. The direct output eect is $1 million for
both cases, with and without opportunity cost (i.e., a concurrent negative impact on wholesale trade of
11.4 percent or $114,000).
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 163
striking is the change in the indirect and induced employment eects. Relative
to model 1, those eects are 32 percent greater when opportunity cost is not
considered and 42 percent greater when it is. In terms of both output and
employment, model 2 shows a strong increase in the agricultural-production
Table 7. Summary of the Impacts of Employment of a One Million Dollar
Increase in Food Hub Final Demand in Model 2
Indirect plus Induced Impacts (jobs)
Industry Sector
Opportunity Cost
Opportunity Cost
Agricultural production (food hub farm þ
other farm)
2.174 2.173
Support activities for agriculture and forestry 0.647 0.647
Retail trade 0.447 0.391
Health and social services 0.446 0.369
Finance and insurance 0.273 0.247
Other services (total) 0.272 0.246
Real estate and rental (total) 0.260 0.234
Accommodation and food service 0.260 0.225
Manufactured food 0.172 0.170
Professional scientic and technical services 0.145 0.103
Transportation and warehousing 0.139 0.112
Administrative and waste services 0.132 0.086
Wholesale trade 0.098 0.503
Educational services 0.081 0.067
Construction 0.073 0.068
Arts, entertainment, and recreation 0.066 0.053
Government 0.061 0.047
Information 0.051 0.042
Manufacturing (other) 0.036 0.032
Management of companies 0.030 0.021
Utilities 0.022 0.020
All other sectors 0.003 0.005
Total 5.895 4.856
Notes: Model 2 uses the dierentiated food hub farm and other-farm sectors based on the survey data
from the aggregate default agriculture sector in IMPLAN; see appendix 1. Forease of exposition, we order
the results (indirect þinduced eects) by size of impact. The direct employment eect is 5.19 jobs for
both cases: RAs employment to output ratio (32/$6,163,720) multiplied by the direct output eect of
$1 million. The case with opportunity cost imposes a concurrent negative impact on wholesale trade
equivalent to 0.58 jobs.
Agricultural and Resource Economics Review164 April 2016
sector (primarily from food hub farms) and in support activities for the
agriculture and forestry sector.
Given the direct eect ($1 million of output and 5.19 jobs), the multipliers
under model 2 are 1.75 for output and 2.14 for employment when the
opportunity cost is not considered and 1.57 for output and 1.94 for
employment when it is. Models 1 and 2 show similar reductions in the
multipliers when opportunity cost is considered. The output multipliers from
model 2 are approximately 4 percent greater than the output multipliers
from model 1 while model 2s employment multipliers are more than 16
percent greater. Thus, using the default agricultural-production-sector data in
IMPLAN will underestimate the total eects on output and, in particular,
employment generated by increases in nal demand for food hubs.
In both model 1 and model 2, the relative distribution of eects is similar for
the two versions (with and without opportunity cost). Figure 3 provides a visual
representation of the largest indirect and induced eects in model 2 when
opportunity cost is included. The greatest positive impact ($202,222) is on
agricultural-production sectors (food hub farms primarily and other farm
sectors) and consists almost entirely of indirect eects. In addition, relative
Figure 3. Indirect and Induced Output Impacts of a One Million Dollar
Increase in Food Hub Final Demand for the Top Ten Impacted Sectors in
Model 2 with Opportunity Cost
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 165
to the farm-level impacts on output in model 1 (Table 4), the total indirect and
induced output eect in model 2 increases nearly 12 percent.
The model-2 ranking of the top ve sectors most aected by the increase in
demand is the same as in model 1. Notably, however, the indirect-induced
eect for support activities for agriculture and forestry is considerably
greater in model 2 ($15,477, Table 6) than in model 1 ($3,196, Table 4). This
dierence reects the greater number of industry links between support
activities for the agriculture and forestry sector and food hub farms. The
eect on employment for the support-activities sector is equally strong; it
increases from 0.134 in model 1 (Table 5) to 0.647 in model 2 (Table 7).
Discussion and Conclusions
This study develops a replicable empirical framework for conducting
assessments of the impacts of food hub organizations. By collecting detailed
expenditure and sales information from an existing food hub, we were able to
use an IO analysis-by-parts approach to estimate the multiplier eects of a
change in nal demand for food hub products. In addition, by collecting
similar detailed information about the farms that sold products to the hub,
we reduced the downward bias associated with using default agricultural-
production data and thus produced more-accurate assessments of a food
hubs economic impacts. Finally, by collecting detailed downstream
information on purchasing patterns of the hubs customers, we identied
important factors that aect the opportunity cost when purchases of
products from food hubs increase (osets via decreases in purchases in other
Our particular application considered RA, a food hub operating in
New York. We demonstrate that the production functions of the farms that
sold outputs to the food hub were dierent from the production functions
constructed from IMPLANs aggregated statewide farm sector; the technical
coecients and regional purchase coecients in the default IMPLAN
agricultural sectors do not accurately reect the activities of the food hub
farms in our study. Comparing the results of the two models (i.e., with
relative to without a separate food hub sector) indicates that the indirect
and induced eects associated with the food hub expansion increase by 9
percent and 32 percent for output and employment, respectively, when
opportunity costs are considered, and 13 percent and 42 percent,
respectively, when opportunity costs are not considered. In terms of the
nal estimated multipliers for the food hub, the output (employment)
multipliers in the models that treat food hub farms as a separate sector are
around 4 percent (15 percent) greater than the multipliers from the default
If future research nds that the farms analyzed in this case study are typical
for food hub participants, studies of the economic impacts of food hubs that rely
on IMPLANs default agricultural-sector data likely underestimate the true
Agricultural and Resource Economics Review166 April 2016
magnitude of local impacts in general, and employment impacts in particular.
Furthermore, additional spending per unit of output by a food hub farm on
employee compensation, other agricultural sectors, and support activities for
agriculture and forestry may be particularly important for rural economies.
Food hubs may strengthen the interlinked networks of business-to-business
and business-to-customer sales in rural regions.
The model that uses a separate food hub farm sector produces a gross output
(employment) multiplier of 1.75 (2.14). However, using data on the hubs
customers, we nd that every additional $1.00 of nal demand for food hub
products results in a $0.11 net oset in fewer purchases from other sectors.
After accounting for this osetting negative shock, the output (employment)
multiplier drops to 1.57 (1.94), which represents a reduction of 10.4 (9.3)
percent from the case in which the opportunity cost is ignored. Thus,
future assessments of impacts of food hubs should account for the
opportunity cost.
The results of the survey of hub customers provide evidence that there are
opportunities for expansion in the food hub sector, primarily through
improved logistics (e.g., lower minimum order sizes and increased frequency
of deliveries) and expanded product oerings. Based on our ndings, policies
that increase nal demand for food hub products will have an overall positive
economic impact on the community even when opportunity costs are
As previously noted, our results are based on a single case study of a hub
covering the state of New York. Extending the results beyond methodological
recommendations may be problematic, particularly for food hubs that use a
considerably dierent business model (e.g., a hub that includes food
processing). Though we caution against generalizing the results of our case
study to other food hubs, the methodology developed here is likely to be
preferable to using IMPLANs default farm-sector data in contexts in which
the food hub(s) is similar to New Yorks in terms of involving producers of
similar scale who grow similar commodities and performs similar functions.
Furthermore, two prior studies estimated a similar cost of goods sold
(Fischer et al. 2013) and similar expenditures on employee compensation
(Farm Credit Council 2013), pointing to the potential for RA to provide an
average or representative example for food hubs. In any case, the data-
collection procedure described can be used by researchers interested in
conducting similar studies of food hub operations.
Future Research
Our results provide evidence that assessments of the economic impacts of food
hubs will underestimate the magnitude of local impacts and the distribution of
the impacts in terms of sectors when they depend on IMPLANs default data.
Consequently, future studies could be improved by collecting data about the
farms that participate in the hubs. The challenge is that this type of detailed
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 167
data is time-consuming and expensive to collect. USDAs Agricultural Resource
Management Survey could provide a valuable source of information on farm
expenditure patterns, but its sample of participants in local food systems (not
to mention those who sell to a food hub) is extremely small and the survey
provides little useful information on the locations of expenditures (i.e.,
whether local or nonlocal). Such information could facilitate more-frequent
evaluations of the impacts of food hubs.
Broader application of the recommendations from this case study would
clearly benetfromrenement of the methodology via a learning community.
How, for example, do the economic impacts of food hubs change when a hub
works only with producers of fresh products (no value added products)?
Furthermore, our survey of the food hub farms was designed to correspond
to IMPLANs sectors rather than to farm prot and loss statements. The
approach presents both merits and weaknesses, and since the sort of data
needed continues to be collected, future studies to identify more-standardized
data-collection protocols will be extremely important, particularly for eorts to
compare results across studies.
We also recommend additional research comparing other models and
structures for aggregating and moving locally grown products into other
types of market outlets. Studies of market channels similar to Hardesty and
Le(2010) and LeRoux et al. (2010) could improve our understanding of the
net impact of a food hub on participating producers, particularly relative to
other available market outlets.
Finally, we introduce a relatively small hypothetical shock to demand for the
food hub sector that results in relatively small additional direct purchases from
the food hub farms. Consequently, we do not incorporate supply-side
countervailing eects. Swenson (2010) provides a useful discussion of the
amount of land required to increase local fruit and vegetable production and
how to calculate economic impacts when assuming that the amount of
available crop land is xed. We recommend that future studies examine the
impacts of larger relative shocks and incorporate supply-side countervailing
eects into the analysis.
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Appendix 1. Input Output Model Aggregation Scheme within IMPLAN
Regional Access
Expense Category Model Sector
sectors Revised sectors
Ag (other), forestry,
shing & hunting
119 5, 79, 1518
Food costs from farms Farm products 14, 6, 1014
Support activities for ag
& forestry
Mining 2030 2030
Utilities electric Utilities 3133 3133
Warehouse repair &
Construction 3440 3440
Other manufactured
Manufacturing (other) 41318 4142, 4849,
Food costs from
Manufactured food 4347, 5070
Fuel Expense Petroleum reneries 115
Wholesale trade
Wholesale trade 319 319
Jablonski, Schmit, and Kay Economic Impacts of Food Hubs 171
Appendix 1. Continued
Regional Access
Expense Category Model Sector
sectors Revised sectors
Retail trade (margin) Retail trade (other) 320331 320325,
Fuel expense (margin) Retail stores gasoline
General freight trucking Transportation and
332340 332340
internet, satellite
Information 341353 341353
Insurance, interest, bank
Finance and insurance 354359 354359
Real estate and rental
360366 360361,
Truck leasing & rental Automotive equipment
rental & leasing
Computer consulting,
accounting and legal
Professional scientic&
technical services
367380 367380
Management of
381 381
contractors, waste
Administrative and
waste services
382390 382390
Educational services 391393 391393
Health and social
394401 394401
Arts-entertainment and
402410 402410
Motels, travel meals Accommodation and
food service
411413 411413
Parking expenses Other services (other) 414426,
Truck and equipment
repairs & maintenance
Automotive and
machinery repair &
414, 417
Postage, truck
registration &
92 Government 427432,
The table is delineated by row sections according to IMPLANs original 2-digit NAICS sector aggregation
scheme. The revised sector aggregation scheme is shown in the last column.
Agricultural and Resource Economics Review172 April 2016
... To estimate the impact of buffer zones creation and repurposing of agricultural land, we used the input-output IMPLAN model (Impact Analysis for Planning; IMPLAN Group, LLC., Huntersville, NC, USA) with 2016 data at the county level to match the land use survey year. Input-output models can study the impacts in the economy of changes in agriculture, investment in solar energy generation and storage, food industry, and other sectors (Bae and Dall'erba, 2016;Jablonski et al., 2016;Mayzelle et al., 2015;Parajuli et al., 2018). IMPLAN uses multipliers that measure the intersectoral relationships in the regional economy, which allows to measure the implications for the regional economy from a change in the economic value output (direct effect) of a particular sector and its spillover effects (indirect and induced effects). ...
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Low-income, rural frontline communities of California's Central Valley experience environmental and socioeconomic injustice, water insecurity, extremely poor air quality, and lack of fundamental infrastructure (sewage, green areas, health services), which makes them less resilient. Many communities depend financially on agriculture, while water scarcity and associated policy may trigger farmland retirement, further hindering socioeconomic opportunities. Here we propose a multi-benefit framework to repurpose cropland in buffers inside and around (400-m and 1600-m buffers) 154 rural disadvantaged communities of the Central Valley to promote socioeconomic opportunities, environmental benefits, and business diversification. We estimate the potential for (1) reductions in water and pesticide use, nitrogen leaching, and nitrogen gas emissions, (2) managed aquifer recharge, and (3) economic and employment impacts associated with clean industries and solar energy. Retiring cropland within 1600-m buffers can result in reductions in annual water use of 2.18 km³/year, nitrate leaching into local aquifers of 105,500 t/year, greenhouse gas emissions of 2,232,000 t CO2-equivalent/year, and 5388 t pesticides/year, with accompanying losses in agricultural revenue of US$4213 million/year and employment of 25,682 positions. Buffer repurposing investments of US$27 million/year per community for ten years show potential to generate US$101 million/year per community (total US$15,578 million/year) for 30 years and 407 new jobs/year (total 62,697 jobs/year) paying 67 % more than prior farmworker jobs. In the San Joaquin Valley (southern Central Valley), where groundwater overdraft averages 2.3 km³/year, potential water use reduction is 1.8 km³/year. We have identified 99 communities with surficial soils adequate for aquifer recharge and canals/rivers within 1600 m. This demonstrates the potential of managed aquifer recharge in buffered zones to substantially reduce overdraft. The buffers framework shows that well-planned land repurposing near disadvantaged communities can create multiple benefits for agriculture and industry stakeholders, while improving quality of life in disadvantaged communities and producing positive externalities for society.
... 5 For example, the Agricultural Marketing Service facilitates "the strategic marketing of agricultural products in domestic and international markets…," while the Farm Service Agency "administers credit and loan programs, and manages conservation, commodity, disaster and farm marketing programs" (US Department of Agriculture (USDA), n.d.). By definition, farmers and ranchers selling through LRFS markets are adding value to the products they produce through differentiation (Jablonski, Schmit, & Kay, 2016), which often 4 The "Farm Bill" is an omnibus bill that governs a wide range of U.S. agricultural and food programs and is reviewed and renewed roughly every five years. The Farm Bill can be considered similar to the Common Agricultural Policy in the European union. ...
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As society experiences greater food- and agriculture-related crises, including those related to climate change and the COVID-19 pandemic, it is necessary to rethink conventional silos of hierarchical government. Know Your Farmer Know Your Food (KYF2) was an ambitious collaborative interagency model to address local and regional food system (LRFS) development across a multitude of policies and programs. KYF2, as a public management strategy for implementing public policy, was associated with an investment of more than $1 billion through more than 40,000 LRFS initiatives. Our aim is to document and evaluate the extent to which KYF2 changed the way the USDA implements LRFS policy. Guided by public management, policy implementation, and collaboration literature, we use a mixed methods approach by: 1) conducting a document analysis to determine the internal implementation goals of KYF2, and 2) surveying USDA staff members involved in KYF2 and using statistical and network analysis of survey data to evaluate the evidence about whether KYF2 achieved internal goals. We find that KYF2 legitimized LRFS work within USDA agencies, changed and institutionalized the ways in which daily business is conducted, and elicited new cross-agency collaborations. KYF2, as a cross-boundary innovation, enabled the USDA to coordinate implementation of LRFS policies across 17 agencies, integrating LRFS department-wide and creating policy feedbacks that resulted in legislative change. The development and passage of public policy is often a focus for change, but this study suggests that management strategies to coordinate existing policies can also significantly impact the way in which governments engage in complex, multi-sector issues.
This two-volume set, IFIP AICT 663 and 664, constitutes the thoroughly refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2022, held in Gyeongju, South Korea in September 2022. The 139 full papers presented in these volumes were carefully reviewed and selected from a total of 153 submissions. The papers of APMS 2022 are organized into two parts. The topics of special interest in the first part included: AI & Data-driven Production Management; Smart Manufacturing & Industry 4.0; Simulation & Model-driven Production Management; Service Systems Design, Engineering & Management; Industrial Digital Transformation; Sustainable Production Management; and Digital Supply Networks. The second part included the following subjects: Development of Circular Business Solutions and Product-Service Systems through Digital Twins; “Farm-to-Fork” Production Management in Food Supply Chains; Urban Mobility and City Logistics; Digital Transformation Approaches in Production Management; Smart Supply Chain and Production in Society 5.0 Era; Service and Operations Management in the Context of Digitally-enabled Product-Service Systems; Sustainable and Digital Servitization; Manufacturing Models and Practices for Eco-Efficient, Circular and Regenerative Industrial Systems; Cognitive and Autonomous AI in Manufacturing and Supply Chains; Operators 4.0 and Human-Technology Integration in Smart Manufacturing and Logistics Environments; Cyber-Physical Systems for Smart Assembly and Logistics in Automotive Industry; and Trends, Challenges and Applications of Digital Lean Paradigm.
Governments are facing the challenge of feeding students in schools in a city environment in a process that revolves around the coordination of multiple producers, distributors, logistics operators and traders of perishable foods. This paper aims to analyse food collection and distribution to assess the potential of urban food systems regarding school feeding. To do so, we compared school feeding distribution systems in Brazil and France to identify the main issues and investigate the role of local food systems. Our results showed that all cities are concerned about the involvement of short food supply chains to provide vegetables and fruits for schools to promote small local farming but there are cultural characteristics that require the use of different approaches. KeywordsSchool canteensLocal productionOrganic food productionShort food supply chains
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This article synthesizes the evidence on food value chains (FVCs) and regional food hubs (RFHs) through a systematic literature review and suggests future research directions based on the gaps identified in the review. The number of publications on FVCs and RFHs is increasing, indicating that these topics are gaining interest among scholars from different countries and disciplines. Bibliometric analysis and preferred reporting items for systematic review and a meta-analysis (PRISMA) flow chart are used to identify the data from Scopus. The results show that FVCs are an innovative solution to improve the skills and capacity of smallholder farmers through collaborative networks that can match the functions of RFHs. RFHs connect local producers and customers by operating a business based on social entrepreneurship and ecological approaches to increase local economic viability and the sustainability of agriculture products. FVCs and RFHs are designed to respond to supply chain insecurity with value-based approaches in order to achieve sustainable nutrition for the local community. Further research on FVCs and RFHs emphasizes that the business model of regional development in developing countries can improve food security sustainability based on social entrepreneurship, and emphasizes the environmental aspect that it can use to support the sustainability of developing countries local food.
In this chapter, we discuss how input-output (I-O) models can be used to estimate the economic impacts of different local food system investments, programs, or policies. In the first section, we describe the methods for conducting economic impact assessments and some techniques to more accurately capture the economic impacts of exogenous shocks to the food system. We also discuss how the characteristics of local food systems may result in higher economic impacts than commodity-oriented production. In the second section, we provide an example of I-O modeling from a recently completed assessment of nutrition incentive programs. This example could guide those interested in undertaking their own I-O studies. Finally, we discuss future research needs. We propose integrating economic impact assessments with methodological approaches that incorporate other types of impacts resulting from food systems efforts – including environmental – in order to understand broader effects and potential trade-offs.
Community-based systems dynamics (CBSD) integrates members of the public into food systems modeling processes in order to shape food policy and effect systemic change. Food systems present policymakers with many examples of persistent and challenging problems–food insecurity, food waste, diet-related chronic disease, access to healthcare for food sector employees, and more. Viewing food systems as a commons, a shared and collectively governed resource, implies a need for collective, community-based decisions around food systems management and policy. CBSD specifically seeks to incorporate the lived experiences and wisdom of stakeholders and community members into the modeling process. Whereas other types of modeling processes invite feedback from the public along the way, CBSD participants build capacity in systems dynamics and collectively create systems models themselves. In addition, CBSD emphasizes empowering communities that have been historically marginalized within research and policymaking processes. Integrating lay members of the public into a modeling process presents practical challenges, but it also promises a shared vision of actionable and community-based policy changes. This chapter includes a practical overview of the CBSD modeling process, and provides a case study of foodNEST 2.0, a multiyear CBSD food system project based in Cleveland, OH that extends the CBSD model to incorporate a deliberative and situated systems approach.
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Low-income, rural frontline communities of California’s Central Valley experience environmental and socioeconomic injustice that makes them less resilient, including lack of fundamental infrastructure (sewage, green areas, health services), water insecurity, and the lowest air quality in the United States. These communities often depend financially on agriculture, but water scarcity and regulations may cause farmland retirement in California, further hindering local economy and employment. Here we propose a multi-benefit framework to repurpose cropland inside and around small disadvantaged communities to promote socioeconomic and environmental opportunities, and income and industry diversification. We simulated cropland retirement inside and around 156 disadvantaged communities in buffers of 400 m and 1600 m. We estimated (1) reduction in water and pesticide use, nitrogen leaching, and nitrogen gas emissions, (2) potential for aquifer recharge, and (3) economic and employment impacts of retiring and repurposing cropland into clean industries and solar energy. Retiring cropland within 1600 m from disadvantaged communities can reduce 2.18 km3 water use/year, 105,500 t nitrate leaching into local aquifers/year, 2,232,000 t CO2-equivalent emissions/year, and 5,390 t pesticides/year, with revenue losses up to US$ 4,213 million/year and 25,682 job positions. Investments up to $27 million/year per community for ten years potentially generate $101 million/year (total $15,830 million/year) for 30 years and 436 new jobs (total 68,066) paid +66% than farmworker jobs. In the San Joaquin Valley (southern Central Valley), where groundwater overdraft is 2.22 km3/year, potential water use reduction is 1.79 km3/year, which combined with adequate aquifer recharge can offset the overdraft. We found 99 communities with soils adequate for aquifer recharge with canals or rivers within 1600 m. This framework shows that well-planned new opportunities near disadvantaged communities may bring multiple benefits for agriculture and industry stakeholders, while improving the quality of life in the communities and producing positive externalities for society.
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Los mercados de productores son una creciente estrategia de generación de soberanía alimentaria y sustentabilidad; sin embargo, poco se conoce de los impactos que han generado en términos sociales, ambientales y económicos. El objetivo de esta investigación fue desarrollar un marco analítico que permitiera determinar dicho impacto. El marco metodológico resultante está compuesto por 20 indicadores distribuidos en siete grupos: indicadores de proximidad, rentabilidad, beneficios percibidos, áreas de oportunidad, impacto económico, impacto social e impacto ambiental. Esta propuesta metodológica fue puesta a prueba en un mercado de productores agroecológicos de la Ciudad de México. Entre los resultados más sobresalientes sobre los datos descriptivos se pudo observar que los principales beneficios se encuentran en indicadores de interacción socioeconómica, mientras que los indicadores de impacto ambiental son los menos considerados. La metodología que se propone puede constituir una guía para orientar la política pública en el diseño, puesta en marcha, comparación y monitoreo de este tipo de iniciativas a mediano y largo plazo. (Versión en inglés:'_markets)
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A series of coordinated case studies compares the structure, size, and performance of local food supply chains with those of mainstream supply chains. Interviews and site visits with farms and businesses, supplemented with secondary data, describe how food moves from farms to consumers in 15 food supply chains. Key comparisons between supply chains include the degree of product differentiation, diversification of marketing outlets, and information conveyed to consumers about product origin. The cases highlight differences in prices and the distribution of revenues among supply chain participants, local retention of wages and proprietor income, transportation fuel use, and social capital creation.
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This comprehensive overview of local food systems explores alternative definitions of local food, estimates market size and reach, describes the characteristics of local consumers and producers, and examines early indications of the economic and health impacts of local food systems. There is no consensus on a definition of —local” or —local food systems” in terms of the geographic distance between production and consumption. But defining —local” based on marketing arrangements, such as farmers selling directly to consumers at regional farmers‘ markets or to schools, is well recognized. Statistics suggest that local food markets account for a small, but growing, share of U.S. agricultural production. For smaller farms, direct marketing to consumers accounts for a higher percentage of their sales than for larger farms. Findings are mixed on the impact of local food systems on local economic development and better nutrition levels among consumers, and sparse literature is so far inconclusive about whether localization reduces energy use or greenhouse gas emissions.
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This report provides an overview of local and regional food systemsacross several dimensions. It details the latest economic information onlocal food producers, consumers, and policy, relying on findings fromseveral national surveys and a synthesis of recent literature to assess thecurrent size of and recent trends in local and regional food systems. Dataare presented on producer characteristics, survival rates and growth, andprices. The local food literature on consumer willingness to pay,environmental impacts, food safety regulations, and local economicimpacts is synthesized when nationally representative data areunavailable. Finally, this report provides an over-view of Federal andselected State and regional policies designed to support local foodsystems and collaboration among market participants.
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As consumer interest in locally grown food increases, farmers and organizations are working on inventive ways to supply fresh and affordable local food to residents. The Intervale Center, a nonprofit in Burlington, Vermont, partnered with small and midscale farmers to create the Intervale Food Hub, a collaborative of staff and farmers that aggregates, markets, and distributes local products through both a multifarm community supported agriculture (CSA) program and wholesale. Informed by surveys conducted to assess supply and demand in the region, the Food Hub provides businesses, restaurants, retailers, institutions, and individuals with year-round access to a diverse mix of fresh and value-added local food. The Intervale Center serves as a local distributor, purchasing products from up to 30 farmers and coordinating packaging, marketing, distribution, and business operations. Year-round, shared space is available to conduct business operations, including packaging and short-term storage. After three years of operation, the Food Hub has begun exploring ownership structures and geographic expansion. Using a participatory action research approach, this case study reviews the enterprise's development and outcomes. We provide a qualitative assessment of farmer and staff perceptions of successful practices and limitations, and conclude with recommendations for future research.
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In response to low margins in traditional commodity markets and consumer demand for decommodified food, food value chains have emerged in the last decade as strategies for differentiating farm products and opening new, more financially viable market channels for smaller farmers. These business networks incorporate strategic coordination between food producers, distributors, and sellers in pursuit of common financial and social goals. Our analysis of the aggregation, distribution and marketing functions of eight food value chains of diverse character across the United States reveals four summary findings that encapsulate the challenges and opportunities facing these business organizations: (1) private infrastructure investment should match the organizational stage of development and market capacities; (2) identity preservation is a critical market differentiation strategy; (3) informal networks can be highly effective tools for coordinating the marketing efforts of diverse agricultural producers; and (4) nonprofits and cooperatives both can play key roles in value chain development, but should recognize their organizational competencies and limitations.
This chapter explores the advantages of value-based supply chains for midsize agrifood enterprises. Value chains have been successful for industries that follow economies of scale practice and have differentiated products. The chapter presents the importance of identifying the agrifood system, which follows the same practices mentioned above, to take maximum advantage of value chains. The importance of value chains in reducing production and procurement transaction costs are discussed, as are their ability to produce a better-quality product. The common features of successful value chains, including high levels of trust among the strategic business partners, performance evaluation systems, and continuous improvement are also addressed. This chapter furthermore explores the three interrelated economic components of value chains, which are explained through examples of midsize food value chains.
Since 2009 the US Department of Agriculture (USDA) has funded over 2600 local food initiatives. However, the economic impacts of these policies remain unclear largely due to data deficiencies that preclude the understanding of differential expenditure patterns of farms participating in these local market channels (both in terms of what inputs they require, and where the inputs are purchased—local or not). This paper utilizes two unique data sets from samples of producers in New York State (NYS) to build expenditure profiles for local food system participants. We employ USDA Agricultural Resource Management Survey data as a robustness check on our results. The primary contribution of this paper is to provide preliminary evidence that local food system participants in NYS have different expenditure patterns than farmers who do not sell through local food markets. We show that farmers with local food sales have higher reliance on local labor and other variable expenses as primary inputs than farms without local food sales, and that local food producers spend a higher percentage of total expenditure in the local economy. Based on our results, we recommend that future economic impact assessments utilize revised expenditure profiles that more accurately reflect inter-industry linkages of the local food sector.
Values-based value chains and farm to school programs are two aspects of the alternative agri-food system that have received a great deal of attention recently from scholars and practitioners. This paper chronicles two separate pilot efforts to create value chains for mid-scale farms to supply large school districts' food-service operations with more healthful, local, and sustainably produced foods, using a modified farm to school model. Early farm to school efforts were mostly farm-direct, a model that poses difficulty for large districts, which often require some kind of intermediary to procure the volume and form of products required for the scale of their food-service operations. Value chains have the potential to address this issue, as part of a more broad-based sustainable school food procurement model that can met the needs of large districts. The lessons learned about the various roles scholars and community partners might play in creating, sustaining, and monitoring performance of these value chains are highlighted.
Despite the relative absence of wholesale distribution in much of the planning profession's academic and grey literature, emerging models promise to remake the relationship between producers and their regional markets. In this article, key lessons from the value(s) chain literature are illustrated with examples from comparative case studies con¬ducted by the University of Wisconsin–Madison Center for Integrated Agricultural System to acquaint professional planners and allied professionals with strategies for imbuing mid- to high-volume local food distribution with normative values such as transparency and fairness. The research presented here is not a comprehensive analysis of regional wholesale food distribution. Rather, we have focused on organizational, logistical, and marketing characteristics of local and regional food value(s) chains. We utilize an exploratory comparative case study method to identify innovations in food distribution focusing on midtier food value(s) chains. We then describe larger system interventions that planners could employ to better accommodate midtier food distribution needs in the regional planning and food regulatory environment. These interventions include documentation of existing wholesale food system infrastructure; incorporation of agricultural industry clusters into regional economic development planning; cultivation of regional culinary identities to enhance marketing and branding efforts; and collaboration with policy makers and food safety regulators to foster zoning and regulation that protect public safety and welfare and build the capacity and market access of local food entrepreneurs.