A Practitioner’s Guide to Conducting an Economic Impact 

Assessment of Regional Food Hubs using IMPLAN: a step-bystep approach 

Todd M. Schmit, Becca B.R. Jablonski, and David Kay 

Author Contact: Todd M. Schmit, tms1@cornell.edu, 607-255-3015 

USDA Contact: Debra Tropp, Debra.Tropp@ams.usda.gov, 202-720-8326 

ACKNOWLEDGEMENTS 

This work was supported by: Cooperative Agreement Number 12-25-A-5568 with the 

Agricultural Marketing Service of the U.S. Department of Agriculture; Competitive 

Grant No. 2012-67011-19957 with the National Institute for Food and Agriculture, of the 

U.S. Department of Agriculture; and Grant No. GNE11-021 with the Northeast Region 

Sustainable Agriculture Research and Education Program. The authors wish to thank 

Allison Gunter Bauman and Florence Becot for their thoughtful review of this guide.

FULL REPORT 

This Practitioner’s Guide is based on Assessing the Economic Impacts of Regional Food 

Hubs: the Case of Regional Access, by T.M. Schmit, B.B.R. Jablonski, and D. Kay, 

Cornell University, September 2013. The full report on economic impact assessments of 

food hubs can be found at <http://dx.doi.org/10.9752/MS145.09-2013>. 

It is the policy of Cornell University actively to support equality of educational and employment 

opportunity. No person shall be denied admission to any educational program or activity or be denied 

employment on the basis of any legally prohibited discrimination involving, but not limited to, such factors 

as race, color, creed, religion, national or ethnic origin, sec, age or handicap. The University if committed 

to the maintenance of affirmative action programs which will assure the continuation of such equality of 

opportunity.
Table of Contents 

GLOSSARY 4

INTRODUCTION 7

DEFINING THE STUDY AREA 8

FUNCTIONAL ECONOMIC MARKET AREA 8

DEFINING FOOD HUB TRANSACTIONS 10

PRIMARY DATA COLLECTION: FOOD HUB 10

PRIMARY DATA COLLECTION: FOOD HUB FARM 12

TABLE 1: SAMPLE FOOD HUB EXPENDITURE PROFILE AND MAPPING TO IMPLAN CATEGORIES 13 

PRIMARY DATA COLLECTION: CUSTOMER DATA 14

TABLE 2: SAMPLE FOOD HUB FARM EXPENDITURE PROFILE AND MAPPING TO IMPLAN

CATEGORIES 16 

CONSTRUCTING THE MODEL IN IMPLAN 17

CREATING THE MODEL AND AGGREGATION SCHEME 17

CREATING A FOOD HUB FARM SECTOR 19

BOX 1: CREATING A FOOD HUB FARM INDUSTRY SECTOR 19 

FIGURE 1. CREATING A FOOD HUB FARM INDUSTRY SECTOR 21 

BOX 2: DELETE FOOD HUB FARM INFORMATION FROM ORIGINAL FOOD SOLD–FARM SECTOR 21 

FIGURE 2. DELETING FOOD HUB FARM INFORMATION FROM ORIGINAL FOOD SOLD–FARM SECTOR

22 

BOX 3. CUSTOMIZING THE FOOD HUB FARM SECTOR’S PRODUCTION FUNCTION 23 

FIGURE 3. CUSTOMIZING THE FOOD HUB FARM SECTOR’S PRODUCTION FUNCTION. 25 

BOX 4. CUSTOMIZING THE FOOD HUB FARM SECTOR’S COMMODITY PRODUCTION 26 

FIGURE 4. CUSTOMIZING THE FOOD HUB FARM SECTOR’S COMMODITY PRODUCTION 26 

BOX 5. CUSTOMIZING THE FOOD HUB FARM SECTOR’S REGIONAL PURCHASE COEFFICIENT (RPC)

27 

FIGURE 5. CUSTOMIZING THE FOOD HUB FARM SECTOR’S REGIONAL PURCHASE COEFFICIENT 

(RPC) 28 

IMPACT ANALYSIS 28

QUANTIFYING THE DIRECT EFFECTS 29

QUANTIFYING OPPORTUNITY COSTS 30

ANALYSIS BY PARTS (ABP) 30

BOX 6. ANALYSIS-BY-PARTS IMPACT ASSESSMENT 31 

FIGURE 6A. ANALYSIS-BY-PARTS IMPACT ASSESSMENT, INDUSTRY SPENDING PATTERN, STEP 1

32 

FIGURE 6B. ANALYSIS-BY-PARTS IMPACT ASSESSMENT, EMPLOYEE COMPENSATION, STEP 2 33 
FIGURE 6C. ANALYSIS-BY-PARTS IMPACT ASSESSMENT, PROPRIETOR INCOME, STEP 3 34 

FIGURE 6D. ANALYSIS-BY-PARTS IMPACT ASSESSMENT, OPPORTUNITY COST, STEP 4 35 

FIGURE 6E. ANALYSIS-BY-PARTS IMPACT ASSESSMENT, ANALYZE SINGLE REGION, STEP 5 36 

INTERPRETING YOUR RESULTS 36

SUMMARY RESULTS 36

FIGURE 7. SUMMARY RESULTS 37 

TABLE 3. SUMMARY ECONOMIC IMPACT RESULTS OF $250,000 INCREASE IN FINAL DEMAND FOR 

FOOD HUB PRODUCTS AND SERVICES, WITH OPPORTUNITY COSTS CONSIDERED 37 

TABLE 4. SUMMARY ECONOMIC IMPACT RESULTS OF $250,000 INCREASE IN FINAL DEMAND FOR 

FOOD HUB PRODUCTS AND SERVICES, WITHOUT CONSIDERING OPPORTUNITY COSTS (ALL FINAL 

DEMAND IS NEW DEMAND) 38 

DETAILED INDUSTRY IMPACT RESULTS 38

FIGURE 8. DETAILED INDUSTRY RESULTS (OUTPUT) 39 

 
GLOSSARY1

Aggregation—the combining of detailed subgroups to form a larger group. For example, 

the detailed industries available from the Bureau of Economic Affairs are aggregated to 

summary industries and sectors for publication. 

Commodity—a product or service, may be produced by one or multiple industries. 

Commodity output represents the total output of the product or service, regardless of the 

industry that produced it. If an industry and the commodity produced by the industry 

have the same name, the commodity is considered to be the primary product of that 

industry. 

Final Demand—the value of foods and services sold to institutions (or end users) during a 

calendar year. These goods and services disappear from the economy and are not used to 

generate more product(s). Exports are included in final demand since the commodity will 

not be used again to create more product(s) in the region. 

Food Hub Farm—a farm that sells product(s) to one or multiple food hubs. Note that 

producers in this category need not sell products exclusively through this channel, rather 

these sales are included in its marketing portfolio. Additionally, for the example used 

herein, this sector reflects the inter-industry linkages of farms that sell to our case study 

food hub (obtained via primary data collection), and is disaggregated from the default 

IMPLAN data available via the “food sold-farm” sector (see below). 

Food Sold-Farm—for the example used herein, we created two aggregated sectors to 

reflect the primary expenditure categories for food hubs. The food sold-farm sector 

reflects the food farm products for sale by the food hub, and includes the default 

IMPLAN expenditure profile from sectors 1-4, 6, and 10-14. 

Food Sold-Nonfarm—for the example used herein, we created two aggregated sectors to 

reflect the primary expenditure categories for food hubs. The food sold-nonfarm sector is 

defined as food products purchased from processors by food hubs, and includes the 

default IMPLAN expenditure profile from sectors 43-47, 50-70. 

Industry—a group of establishments engaged in the same or similar types of economic 

activity. 

Margin or Margin Costs—the value of the wholesale and retail trade services provided in 

delivering commodities from producers’ establishments to purchasers. The margin is 

                                                            

1 The definitions in the glossary are adapted from IMPLAN’s glossary as well as the IMPLAN 

Professional, version 2.0, 3rd edition (2004). The IMPLAN glossary is available at: 

<https://implan.com/index.php?option=com_glossary&task=list&glossid=13&letter=All> 
calculated as sales receipts less the cost of the goods sold. It consists of the trade margin 

plus sales taxes and excise taxes that are collected by the trade establishment. 

Multipliers—final demand drives input-output models. Industries respond to meet 

demand directly or indirectly (by supplying goods and services to industries responding 

directly). Each industry that produces goods and services generates demands for other 

goods and services and so on, round-by-round. Multipliers describe these iterations. 

Multipliers break the effects of stimuli on economic activity into three components: 

direct, indirect, and induced. Further, IMPLAN describes three types of multipliers: Type 

I, Type II, and Type SAM. 

1. Direct effects—changes in the industries to which a final demand change 

was made 

2. Indirect effects—changes in inter-industry purchases as they respond to 

the new demands of the directly affected industries 

3. Induced effects—changes in spending from households as income 

increases or decreases due to the changes in production 

4. Type I multiplier—measures the direct and indirect effects of a change in 

economic activity. It captures the inter-industry effects only, i.e., 

industries buying from local industries. 

5. Type II multiplier—captures the direct and indirect effects. In addition to 

the inter-industry effects, the Type II also takes into account the income 

and expenditures of households. The household income and the household 

expenditures are treated as industries. This internalizes (endogenizes) the 

household sector, including the induced or household spending effects. 

6. Type SAM multiplier—uses all information about the institutions selected 

to be included in the predictive model. 

Production Function—shows where an industry spends, and in what proportions, to 

generate each dollar of output. 

Regional Purchase Coefficient (RPC)—represents the portion of the total demand for a 

commodity by all users in local economy that is supplied by producers located in that 

economy. For example, an RPC of 0.6 for the commodity “vegetable and melons” means 

that local farmers provide 60% of the demand for vegetables and melon (by other 

farmers, processors, vegetable wholesalers, and others). The remaining 40% of demand is 

satisfied by imports. 
Sector—the institutional units that make up the total economy (including businesses, 

households, institutions, and general government). 

Value Added—the difference between an industry’s total output and the cost of its 

intermediate inputs. Value added consists of compensation of employees (inclusive of 

benefits) and proprietor (i.e., payments received by self-employed individuals), other 

property type income (payments for rents, royalties and dividends), and business taxes on 

production and imports less subsidies. 

 
INTRODUCTION 

Interest is increasing in locating funding for food hub development. These local food 

aggregation and distribution businesses are purported by their developers and funders to 

elicit substantial positive community economic impact. Yet prior to the report released by 

Schmit, Jablonski, and Kay (2013), Assessing the Economic Impacts of Regional Food 

Hubs: the Case of Regional Access, there had been no rigorous economic impact 

assessments of food hubs, nor had a replicable framework been defined for their conduct. 

This practitioner’s guide is complementary to the full report, and walks you through the 

steps of measuring the local economic impact of expanding food hub activities, either 

from the establishment of a new hub or the expansion of an existing hub. 

This guide is intended for individuals who are familiar with software and databases by 

IMPLAN Group LLC2

 (IMPLAN provides data and tools for economic analysis). In 

addition to the data available from IMPLAN, the proposed approach requires substantial 

data collection from the food hub, farm and non-farm product suppliers to the food hub, 

and food hub customers. While time-consuming to collect, the information can be 

effectively used through the framework detailed here to assess the impacts of food hub 

activities, particularly in computing the value of inter-industry linkages within the local 

economy as a result of expanded final demand for food hub goods and services. We have 

included sample tables containing the type of data you will need to make the necessary 

modifications within the IMPLAN database. 

Please note that this guide presents information about how to assess the economic impact

of food hubs, which is different from looking at the contribution of food hubs to the local 

economy. Impact analysis examines the marginal economic impact of a change in the 

economy (e.g., the opening or expansion of a business). Contribution analysis examines 

the contribution of the business to the local economy. Given the likelihood that most 

readers of this guide will want to estimate the impact of foundation or government 

funding used to develop or expand an existing hub, we think that economic impact 

assessments are a more useful and accurate measure as they provide the net changes in 

new economic activity.3 

Happy modeling! 

                                                            

2 <http://implan.com> 

3

 For more information on the differences between see: Watson, P., J. Wilson, D. Thilmany, and S. Winter. 

2007. "Determining Economic Contributions and Impacts: What is the difference and why do we care?" 

Journal of Regional Analysis & Policy. 37(2): 140-146. See also: 

http://implan.com/index.php?option=com_multicategories&view=article&id=201:impact-vs-contributionanalysis&Itemid=16 
DEFINING THE STUDY AREA 

The first step in conducting economic impact assessments is to define the appropriate 

study area—the boundaries of the “local” economy.4

 Determining what constitutes local 

can have a decisive impact on the results—it is nearly always true that the larger the 

definition of local, the more inter-industry linkages, and the larger the economic 

multiplier effect of a given change in the demand for local goods and services. To isolate 

the effects of an impact, make as small a study area as possible, while still including the 

areas necessary to capture all of the important effects. 

When defining the study area, consider the availability of secondary data for the region. 

The methodology described in this guide uses secondary data available from the 

IMPLAN Group, LLC. (IMPLAN), which is available by zip code, Congressional 

district, county and state (it is also easy to build models using multiple counties or 

states).5,6 Though data are available at a sub-county level, IMPLAN advises using the 

concept of a functional economic area to guide decisions about study area. A functional 

economic area is a semi-self-sufficient economic unit (and therefore ideal for this type of 

analysis). It includes the places where people live, work, and shop, and can sometimes be 

identified by physical or other characteristics. The closer the study area is to a functional 

economic area, the more robust the analysis will be. It is unlikely that a sub-county area 

(particularly if it is rural) is likely to fit this functional economic area definition. 

***************SIDEBAR*************** 

Functional Economic Market Area 

Functional Economic Market Areas (FEMAs) are frequently used to define economic 

relationships and flows that are not necessarily reflected by administrative boundaries. 

FEMAs are areas that are bound together by a relatively contained and cohesive network 

of trade links—the buying and selling of raw materials, industrial and consumer goods 

                                                            

4 The IMPLAN Professional, version 2.0, 3rd edition (2004) has a very helpful section on “defining a 

project” (see Chapter 9, p. 107). 

5

 Other commercially available input-output models are available (RIMS II or REMI). However, the U.S. 

Department of Commerce’s Bureau of Economic Analysis announced that due to the impacts of 

sequestration, they are discontinuing the RIMS II program. For more information, see: 

<https://www.bea.gov/regional/rims/rimsii/>. IMPLAN appears to be the most widely used impact analysis 

software and data. For example, several branches of the U.S. federal government used IMPLAN to estimate 

the impacts of the 2009 American Recovery and Reinvestment Act (see: 

<http://www.recovery.gov/Pages/default.aspx>). 

6

 For more information about purchasing data from IMPLAN, see: 

<http://implan.com/V4/index.php?option=com_virtuemart&page=shop.browse&category_id=1&Itemid=13

&vmcchk=1&Itemid=13>. 
and services, and labor. In the United States, most county boundaries were delineated 100 

or 200 years ago, and are not reflective of contemporary economies. As such, they very 

often do not capture a single FEMA. 

There is no universal approach to defining FEMAs, but there are a few things important 

to consider. One major consideration is scale or the size of the region chosen. If the scale 

is too small, then an economic impact assessment will not appropriately capture the ripple 

effects of a shock—say a new business moving to the area. On the other hand, if the 

region is too large, then the effects of a shock may be partially or completely lost in the 

economic noise generated by other economic activity in the area. Accordingly, it makes 

the most sense to choose the smallest reasonable area, which is still a FEMA. 

When looking for a FEMA, one should look for local and regional commuting, shopping, 

and supply chain patterns, since these are some of the most important trade links that will 

delineate a FEMA. By way of illustration, Cornell University is located in Tompkins 

County, NY, but to define Tompkins County as a FEMA is inappropriate on a number of 

levels. Cornell University, a major employer in the County, draws employees from 

surrounding (rural) counties who in turn spend the wages they earn in those adjacent 

locales. Thus, if Cornell University were to close or lay off a percentage of its labor 

force, it would have important economic impacts on those surrounding counties, which 

would not be captured if a multi-county region was not defined as the FEMA. 

***************************END SIDEBAR*************************** 

Consider the use of the assessment when determining an appropriate economic functional 

area: Why are you conducting this impact assessment? Do you want to show the 

economic impact of a food hub to a particular funder or government official? If your 

project is funded by a state agency, defining “local” by state boundaries may make sense. 

Or, if the food hub is funded by a county or facilitated through a Cooperative Extension 

agent who is funded through a county, then county borders may be the more appropriate 

definition of local. Perhaps you are trying to assess what the impact of a food hub is to 

participating producers. If this is the case then defining your study area based on the 

locations and distribution patterns of the participating farms might be most appropriate. 

You will also need to keep in mind the residential location of both the farm and food 

hub’s labor force, as their spending patterns are important to your study results. 

Defining the appropriate study area is ultimately the choice of the researcher, yet can 

have profound impacts on the results of the study. One way to understand how this 

definition affects results is to conduct the analysis for several study areas, each relying on 
differing assumptions. This strategy allows those reading the study to have a better 

understanding of the range of potential impacts.7 

DEFINING FOOD HUB TRANSACTIONS 

The second step in an impact assessment is to define the industry sector (or sectors) of 

interest and its (their) linkages with other industries. Example sectors might include fruit 

farming or wholesale trade (see glossary for definition). This is less straightforward in the 

case of food hub assessments as a separate food hub sector and its transactions with other 

industries do not exist within traditional data sources (including IMPLAN). The 

implication for an impact assessment is that additional information must be collected to 

develop a food hub sector or, alternatively and as described here, to describe the nature of 

all of its transactions. 

Depending on the data that are available in your locality, we recommend data collection 

from three sources: the food hub(s) that you are analyzing, farms (and potentially local 

processors) that sell product(s) to the food hub(s), and customers that purchase product 

from the food hub(s). 

Primary Data Collection: Food Hub 

This approach can be applied to analyze the impact of a single food hub or multiple food 

hubs. In both cases, you will need to collect information to understand the expenditure 

pattern of the food hubs in your study area—i.e., to understand the input requirements of 

the food hub for every dollar of sales. 

To begin, ask the food hubs in your study for a copy of its their profit and loss (P&L) 

statement, or similar income and expense records. This may require a confidentiality 

agreement with the food hub and agreement on disclosure procedures. A P&L statement 

summarizes the revenues and expenses incurred during a specific period of time. For the 

purpose of this assessment, you want a copy of the hub’s P&L statement for the most 

recently completed year of operation (e.g., January 1- December 31). 

You will need enough information to be able to assign the food hub expenditure 

categories to their corresponding IMPLAN sector. IMPLAN divides the economy into 

440 sectors, which do not always translate neatly to the ways that businesses record 

expenditures in a P&L statement. IMPLAN provides a lookup table to assist you in this 

mapping 

                                                            

7

 For an example, see: Gunter, A., and D. Thilmany McFadden. May 2012. Economic Implications of Farm 

to School for a Rural, Colorado Community. Rural Connections: 13-16. 

<http://wrdc.usu.edu/files/publications/publication/pub__9857945.pdf>. 
<http://implan.com/V4/index.php?option=com_docman&task=doc_download&gid=152

&Itemid=60>. The first two columns in Table 1 provide examples of the sector 

translations that must occur to assign food hub expenditure items to IMPLAN sectors. An 

expenditure item like “gasoline” would require additional questioning of the food hub 

operator to determine if the item was a) purchased from a retail gasoline station 

(IMPLAN sector 326), or b) purchased in bulk and delivered by a wholesaler (IMPLAN 

sector 319). Keep in mind that as a general rule, the bigger the sector, the more effect it 

has on the results, and the more important it is to work to “get it right.” 

It is also important to determine the percentage of each expenditure item that was 

procured in the functional economic market area (“local study area” in IMPLAN 

terminology). To obtain this information, you will need to work with the food hub 

operator to determine the percentage purchased locally for each expenditure category. For 

example, if the P&L statement shows that $12,000 was spent on office supplies, you will 

need to ask the food hub operator questions about where these items were purchased. 

Each expenditure item needs to be broken down by the percentage of the total purchased 

within and outside of the local study area. Table 1 provides an example of the data you 

need to gather and a helpful format in which it can be recorded. Note that some 

expenditure categories from the P&L statement may map to the same IMPLAN sector. 

In addition, you will want to keep in mind that any expenditure in the retail or wholesale 

trade sector requires accounting for “margining” – only the value of the wholesale and 

retail trade services in delivering products is included here, the value of the costs of 

goods sold is accounted for in other transactions (see glossary for definition of 

“margining”). As total gross output for these IMPLAN sectors reflects only the gross 

margin (revenue less cost of goods sold), the balance is reported in the supporting 

industry where the product is manufactured/produced. For example, our case study food 

hub reported expenditures on gasoline at retail stores to be $16,000, of which 83% was 

purchased locally. To account for margining, we apply the retail trade-gasoline stations 

margin (sector 326), of 14.5% (available within the IMPLAN database) such that only 

$2,320 (16,000 * 0.145) is reported under retail stores-gasoline stations in Table 1. The 

balance ($13,680) is mapped to petroleum refineries (sector 115) and the local purchase 

percentage is taken from IMPLAN for that sector. The same approach was used for office 

supply purchases, with the wholesale trade margin of 18.6% and the balance allocated to 

office supplies (except paper) manufacturing (sector 313). IMPLAN retail and wholesale 

margining is based on national data, and varies by year, see: 

http://implan.com/index.php?option=com_multicategories&view=article&id=680:680&It

emid=71. 

Finally, you will need to record expenditures on value added components, which include: 

employee compensation, taxes, other property type income, and proprietor’s income. 
Though value added may mean something different in other contexts, within an IMPLAN 

framework, and for the purposes of this guide, the four above-mentioned items comprise 

value added. Employee compensation (including benefits paid to employees) and taxes 

are included in the P&L statement and can be assigned directly. Note that payments to 

contractors and consultants are not regarded as employee compensation, but are included 

in different IMPLAN categories (see IMPLAN lookup table above for more detail). All 

taxes paid by the food hub should be recorded under “tax on production and imports”. 

We assume that the difference between total sales and total expenses is proprietor’s 

income, and may be positive or negative.8 

Primary Data Collection: Food Hub Farm 

Understanding how farms that sell product to food hubs (henceforth “food hub farms”) 

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, since 

purchases from farms generally represent a relatively large share of total food hub 

expenses and we are particularly interested in how food hub farms are impacted by food 

hubs, it is important to consider the inter-industry linkages for farm suppliers. 

Furthermore, for most of the businesses from which food hubs purchase inputs, it is 

sufficient to assume that the expenditure patterns of individual businesses reflect that of 

the entire industry sector. For example, a food hub is unlikely to purchase insurance from 

a specialty food hub insurance provider. Rather, the food hub’s insurance agency more 

likely funds a range of businesses with a variety of products. As such, assuming that the 

food hub’s insurance company has a similar expenditure profile to that of the relevant 

insurance sector within IMPLAN should be sufficient. By contrast, there is growing 

evidence that farms participating in local food system outlets are often (but not 

exclusively) smaller in scale, and/or have different patterns of expenditures and labor 

requirements per unit of output than is reflected in IMPLAN’s default agricultural 

sectors. For example, smaller-scale farms may utilize more labor and less mechanized 

equipment than their larger counterparts, thus having a different impact on the labor 

force. Accordingly, we recommend that any food hub impact assessment involve primary 

data collection from food hub farms regarding labor and purchase/use of equipment. 

Again, as above, the larger the purchases from the sector and the more the 

policy/intervention is intended to support that sector, the more important it is to work to 

“get it right.” 

                                                            

8

 For the purpose of this guide we assume the food hub farm sector has no allocation to other property type 

income. However, it may be appropriate in certain contexts, depending on the ownership structure of the 

food hub, to assign the difference between total sales and total expenses to the other property-type income 

component (e.g., corporate profits). 
Table 1: Sample Food Hub Expenditure Profile and Mapping to IMPLAN Categories a

Food hub expenditure category 

(from P&L statement) 

Food hub 

IMPLAN category 

Expense ($) 

Cost of food—from nonfarm Food sold–nonfarmb $ 494,000 Cost of food—from farm Food sold–farm (food hub farm)b $ 180,000 Gasoline (retail margin) Retail stores—gasoline stations (326) $ 2,320 Gasoline (production)d Petroleum refineries (115) $ 13,680 Lease trucks Automotive equipment rental and leasing (362) $ 13,000 Utilities Electric power generation, transmission, and distribution (31) $ 13,000 Insurance Insurance carriers (357) $ 12,000 Office supplies (wholesale margin) Wholesale trade (319) $ 2,232 Office supplies (production)e Office supplies (except paper) manufacturing (313) $ 9,768 Credit card merchant fees Nondepository credit intermediation and related activities (355) $ 10,000  Value added componentsEmployee compensation Employee compensation $ 160,000 Difference between total sales and total expensesc Proprietor income $ 30,000 Taxes (all) Tax on production and imports $ 60,000 Total $1,000,000 

a In addition to the information above, our sample food hub employs 4 people. b For additional explanation about these sectors, please refer to the section entitled “Creating your model and aggregation scheme” below (p.12c By assumption, the difference between total sales and total expenses is allocated to proprietor income. Note, that this can be a negative numbe

food hub sales are $1,000,000 and total expenses from the P&L statement is $970,000, leaving $30,000 allocated to proprietor’s income. 

d Balance of total retail gasoline purchases after margining (margin = 14.5%), % local taken from IMPLAN default e Balance of total office supply purchases after margining (margin = 18.6%). % of local taken from IMPLAN default.
The goal of the primary data collection is to come up with an average food hub farm 

expenditure and outlay profile that can then be scaled up by the total number of farms in 

the study area to create the food hub farm sector within the IMPLAN model. To begin, 

you will need to ask the food hub(s) for a complete list of the farms from which they 

purchase product, as well as farm contact information. 

Next, you will need to collect sales and expenditure information from the farms 

analogous to what is required from the food hub described above (i.e., the P&L statement 

from each food hub farm, along with location of expenditures, and total number of food 

hub farm employees).9

 Please note that direct face-to-face contact with food hub farms 

may be the most efficient way of obtaining such sensitive information. In order for our 

research team to gain access to financial information from food hub farms, members of 

our research team were obliged to conduct in-person interviews, as farmers were wary to 

provide financial information via a telephone, online, or mail survey. Additionally, our 

team made assurances to the farmers that farm financial information would only be 

presented in aggregate, and it would never be possible to identify this information on an 

individual farm basis. 

Table 2 provides a sample of an average food hub farm expenditure profile. This is the 

information you should have on hand once you are finished with your data collection.10

As we did before and explained earlier, we assume the difference between total sales and 

total expenses is allocated to proprietor income, and retail and wholesale purchases are 

margined. 

Primary Data Collection: Customer Data 

Before building your model, the final pieces of information that you need to collect are 

from food hub customers (or potential customers). Collecting information from food hub 

customers serves two important purposes. First, customer data can be used to understand 

the scalability of the food hub sector—i.e., what is a realistic amount of additional sales 

by which final demand (see glossary for definition) for the food hub sector can grow. 

Second, customer data can be used to estimate the opportunity cost associated with 

increases in final demand for food hub goods and services. Opportunity cost impacts 

reflect what would have occurred had the increased final demand for food hub products 

not have happened. It is unrealistic to assume, without supporting evidence, that all 

purchases from food hubs represent new final demand, rather some purchases from food 

hubs probably represent reduced purchases in other sectors. Including opportunity cost in 

                                                            

9 Employees should be counted based on the number of paid, full-time, part-time, or temporary positions. 

Unpaid or family labor should be excluded. 

10 You will probably have more categories of expenditures than indicated in the sample table. 
the impact assessment is very important; for example in the authors’ case study of 

Regional Access, we found that excluding opportunity cost overestimated total impacts 

by over 10%.11 

 

                                                            

11 Note that the sectors in which opportunity costs are reflected are unclear. For example, additional 

purchases from the food hub sector may increase purchases in the retail trade/grocery store sector as 

households may be more likely to cook at home. 
Table 2: Sample Food Hub Farm Expenditure Profile and Mapping to IMPLAN Categories a 

Food hub expenditure category 

(from P&L statement) 

Food hub 

IMPLAN category 

Expense ($) P

L

Items purchased from other farms Food sold-farmb $ 30,000 $1Items purchased from food processors Food sold-nonfarmb $ 10,000 $ Gasoline (retail margin) Retail stores – gasoline stations (326) $ 1,450 $ Gasoline (production)f Petroleum refineries (115) $ 8,550 $ Freight Transport by truck (335) $ 5,000 $ Repair and maintenance 

Maintenance and repair construction of nonresidential structure (39) $ 5,000 $ 

Utilities Electric power generation, transmission, and distribution (31) $ 3,000 $ insurance Insurance carriers (357) $ 2,000 $  Value added componentsEmployee compensation Employee compensation $ 160,000 Difference between total sales and total expensesc Proprietor income $ 30,000 Taxes (all) Tax on production and imports $ 60,000 Total $1,000,000 

a This example assumes that there are 50 farms in the food hub farm sector and that, on average, farms have 5 employees. Thus the total employ

farm sector = 50 * 5= 250. 

b For additional explanation about these sectors, please refer to the section entitled “Creating your model and aggregation scheme” below (p.12c By assumption, the difference between total sales and total expenses is allocated to proprietor income. Note that this can be a negative number

food hub farm sales per farm are $100,000 and total expenses are $95,000, leaving $5,000 allocated to proprietor’s income. 

d Numbers may not add up due to rounding. e The numbers shaded in grey represent intermediate expenditures. The sum of the shaded column area represents the total intermediate expense

sector. 

f Balance of total retail gasoline purchases after margining (margin = 14.5%), % local taken from IMPLAN default. 
To more accurately gauge opportunity costs, we recommend designing a short online 

survey for food hub customers (or potential customers) that asks the following questions: 

1. Is your business interested in purchasing additional items from the food hub if the 

food hub expanded in some way (i.e., carried additional items/quantities, offered 

different delivery options, etc.)? (yes/no/unsure) 

2. If yes, can you quantify the amount of additional purchases that your business is 

interested in making from the food hub on a weekly basis? 

3. If yes, based on the additional purchases your business would make from the food 

hub, would your business purchase fewer items from other local businesses? 

(yes/no/unsure) 

4. If yes, what percentage of new purchases from the food hub would represent 

displaced purchases from other local sources? 

Based on the responses from customers, calculate: 

1. The percentage of customers who responded “yes” they are interested in 

purchasing additional items from food hubs; 

2. The average $ amount of additional purchases that business customers are 

interested in making annually; 

3. The total dollar amount of additional final demand for food hub goods and 

services (i.e., total number of customers multiplied by percentage of customers 

interested in purchasing more product multiplied by average additional 

purchases). This number will be used later as the hypothetical “shock” in our 

model; 

4. The percentage of customers who responded “yes” that food hub purchases result 

in fewer purchases from other sources; 

5. The average decrease in consumer purchases from other sources represented by 

food hub purchases; 

6. The opportunity cost associated with $1 of additional demand for food hub 

products (i.e., the percentage of customers from #3, above, multiplied by the 

average decrease in #4). 

CONSTRUCTING THE MODEL IN IMPLAN 

Once you have collected the requisite information from the food hubs, the food hub 

farms, and the food hub customers you can develop the IMPLAN model. 

Creating the Model and Aggregation Scheme 

To begin, select the data file within IMPLAN based on the region defined by the study 

(i.e., county, multi-county, or state, and year). Once you have selected your data file, go 
to User Preferences within the IMPLAN software and adjust the event year. The event 

year should reflect the year that changes in final demand occur, i.e., the year in which the 

customers will increase their food hub purchases.12

Next, create an aggregation scheme (see glossary for definition). It is preferable to leave 

the model as disaggregated as possible, to minimize aggregation bias.13 That being said, 

for some key sectors it may be difficult to avoid some level of aggregation. As one can 

see from the above description, translating the P&L statement to the appropriate 

IMPLAN industry sector can be complex and require some informed judgments. 

For the example used here, we made the decision to create two aggregated sectors to 

reflect the primary expenditure categories for food hubs: 1) agricultural and food 

products from farms; and, 2) food products from processors for resale. For farm product 

purchases, we created an aggregated sector representing the types of agricultural industry 

sectors that sell products to a food hub. We defined this to include IMPLAN sectors 1-4, 

6, and 10-14, and entitled it “food sold–farm.” Second, with respect to the food products 

purchased from processors, we created an aggregated sector reflecting the types of 

processed products purchased by food hubs, IMPLAN sectors 43-47, 50-70. We entitled 

this aggregated sector “food sold–nonfarm.” Ultimately the decision of how to aggregate 

the model should be defined based on your research objectives and data availability. 

However, as mentioned above, it is preferable to leave the model more disaggregated to 

minimize aggregation bias. 

No matter what decisions you make in regard to your aggregation scheme, you will need 

to identify one sector that does not contain any economic transactions in your study 

area.

14 This sector will be used to create a food hub farm sector. The food hub farm sector 

will be constructed subsequently, using the farm-level data you collected. In our study, 

we used the “tobacco farming” sector as it contains no sales or expenditure information in 

                                                            

12 For more information about changing the event year, see: 

<http://implan.com/v4/index.php?option=com_multicategories&view=article&id=561:561&Itemid=71>. 

13 Aggregation bias stems from the loss of detail that occurs when you aggregate a region’s sectors before 

generating the multipliers. For more information, see: 

<http://implan.com/index.php?option=com_glossary&id=227>. 

14 You can identify a useable sector by looking in the Explore > Study Area Data for sectors that contain 

no output. 
NYS. Once you have completed your aggregation scheme, you will need to reconstruct 

your model.15 

Creating a food hub farm sector 

Next, customize a food hub farm sector within IMPLAN that reflects the average 

expenditure pattern of the farms in your study that sell to food hubs.16 Conceptually, the 

food hub farm sector customization has three steps within the IMPLAN software. The 

first step is to create a food hub farm sector, customized with data collected from the food 

hub farms to include the total number of employees, total output, and value added 

components. See Table 2 for data to include and Box 1 for details on IMPLAN model 

construction.17 A screen shot for our example is shown in Figure 1. 

Box 1: Creating a food hub farm industry sector 

(utilize data from Table 2) 

1. Customize Tasks Bar > Study Area Data (Note that if the Customize menu is not 

activated, you can show this menu by checking Enable Model Customization in 

User Preferences.

2. Edit an industry. 

3. Highlight tobacco farming, and click Rename industry button. 

4. Rename to “food hub farm.” 

5. Make sure that the lock feature (under Output, Value Added) is unchecked for the 

newly renamed “food hub farm” sector. 

6. Under Employment fill in the Total Employment in the food hub farm sector. 

7. Select Edit Options > Edit totals then update per worker values. 

                                                            

15 For more information about how to aggregate a model within IMPLAN, see: 

http://implan.com/V4/index.php?option=com_multicategories&view=article&id=556:556&Itemid=10 

16 For more information, see IMPLAN’s “Customizing Study Area Data” tutorial: 

http://implan.com/v4/index.php?option=com_multicategories&view=article&id=545:545&Itemid=14 

17 All of our boxes and screen shots include the sample data from Tables 2. Additionally, we assume New 

York State data and event year = 2011. 
8. Enter Total Output and Value Added information, including employee 

compensation, proprietor’s income, other property type income,18 and tax on 

production and imports. 

9. Click Update and double-check that your numbers haven’t changed.19 The 

computed value in Intermediate Expenditures should match the total of the 

shaded numbers in Table 2 (perhaps with a small amount of rounding). 

10. Click Save.

The second step is to reduce the default food sold–farm sector by the same amount that 

you added to the newly created food hub farm sector (see Box 2 below for details). A 

screen shot from our example is shown in Figure 2. This is important because the food 

hub farm expenditures and sales are already accounted for within the IMPLAN database. 

Thus, these first two steps remove food hub farm contributions from the aggregate 

agricultural sector, and assign them to a uniquely defined “food hub farm” sector. This 

ensures you are not double counting the food sold through the food hub. 

 

                                                            

18 In our case, we change other property type income to zero as we do not have other property type income 

in our example food hub farm sector. 

19 There appears to be a bug in IMPLAN that changes the other property type income after first clicking 

update. If this happens, re-enter the correct number (in our case, 0) and click update again. This should 

resolve the problem. 
Figure 1. Creating a food hub farm industry sector

Box 2: Delete food hub farm information from original food sold–farm sector 

1. While you are still in the Edit an Industry tab, select food sold farm. 

2. Under Employment edit the Total Employment (reduce by the same amount added 

to the food hub farm sector). 

3. Select Edit Options > Edit totals then update per worker values (reduce by the 

same amount added to the food hub farm sector).

4. Edit total Output and Value Added information, including employee 

compensation, proprietor’s income, other property type income, and tax on 

production and imports. Make sure to reduce each category by the same amount 

added to the food hub farm sector categories. 

5. Click Update and double-check that your numbers haven’t changed.20 The 

computed value in Intermediate Expenditures should match the total of the food 

sold farm sector minus the total value of the shaded numbers in Table 2 (perhaps 

                                                            

20 Note that there appears to be a bug in IMPLAN that changes the other property type income after first 

clicking update. If this happens, re-enter the correct numbers and click update again. This should resolve 

the problem. 
with a small amount of rounding). 

6. Click Save.

7. Click Close.

8. Reconstruct model (Options > Construct > Multipliers). 

Figure 2. Deleting food hub farm information from original food sold–farm sector

The third step customizes the expenditure pattern (or the production function, see 

glossary for definition) of the food hub farm sector. In other words, it is not just 

important to know the total size (employment, output, total intermediate expenditures, 

and value added) of the food hub farm sector, but also its inter-industry linkages or how 

much of the total size can be attributed to which categories. There are three parts 

involved in this step within IMPLAN. The first part of this step is to customize the 

industry production function, based on the data in Table 2 (last column).21 By creating the 

food hub farm sector above (Box 1), you defined the amount of expenditures per unit of 

output that go to all intermediate inputs (defined in IMPLAN as the total absorption 

                                                            

21 For more information, see IMPLAN’s guide to editing industry production: 

<http://implan.com/v4/index.php?option=com_multicategories&view=article&id=546:546&Itemid=14>. 
value) and value added components (defined in IMPLAN as the value added coefficient). 

The sum of these two components, by definition, must equal one. Now you compute the 

gross absorption coefficient for each industry the food hub farm sector buys from based 

on your survey data; i.e., compute the ratio of expenditures and total output for each 

industry (see Table 2). The sum across all industries must equal the total absorption 

value. 

Since the sectors included in the production function of the food hub farm sector are 

likely to be similar to the food sold–farm sector, we recommend that you export the food 

sold–farm sector production function and then import it into food hub farm sector. This 

should minimize the number of commodities to add to the production function of the food 

hub farm sector. Any commodities purchased by the food hub farm sector but not 

included in the imported production function profile can be added by clicking on Options 

> Add a commodity, selecting the commodity and adding the gross absorption coefficient. 

Detailed steps are shown in Box 3 and the screen shot below shows the results based on 

the sample food hub; i.e., using the gross absorption coefficients from the last column of 

Table 2. The applicable screen shot for our example is shown in Figure 3. 

Box 3. Customizing the food hub farm sector’s production function

1. Select Customize > Industry Production 

2. Export the production function of the food sold farm sector. Select Food Sold 

Farm sector (industries are selected on the left side of the window, production 

function will be pre-populated based on local economy) > Options > Library > 

Export > Ok) 

3. Select the food hub farm sector on left side of window (production function will 

be pre-populated, in our example with that of the “tobacco farming” sector based 

on national data) 

4. Import production function of the food sold farm sector into the food hub farm 

sector. With the food hub farm sector highlighted select Options > Library > 

Import > Select Food Sold Farm > Import > Yes). 

5. Add the food hub farm sector commodity in order to account for intra-sector 

purchases. (Options > Add a Commodity > Tobacco farming (still has the old 

name, but this IS the food hub farm commodity) > enter gross absorption 

coefficient (based on primary data collection) > Ok. In our case, we assume the 

food hub farms purchase food sold–farm commodities but not food hub farm 

commodities. 
6. Edit gross absorption coefficients based on the survey data and sector scheme for 

all of the commodities from which the food hub farm sector purchases products 

and/or services. For any sector with zero purchases, enter zero.22

7. Make sure to enter all of the non-zero gross absorption coefficients but one, and 

then click balance. Before you click balance, each of the coefficients should be 

fixed (i.e., make sure that the fixed box is checked) except the last one that you 

are leaving for IMPLAN to balance. IMPLAN will automatically compute the last 

commodity such that the sum of the gross absorption coefficients equals the total 

absorption value. 

8. Click Save > Close 

9. Reconstruct model (Options > Construct > Multipliers). 

                                                            

22 If a sector contains zero purchases, you can try removing the commodity from the list. However, we 

found what appears to be a bug within the IMPLAN software removing a commodity field may cause the 

program to crash. 
Figure 3. Customizing the food hub farm sector’s production function. 

The second part of this step is to customize the commodity production for the food hub 

farm sector that we have created. Commodities are produced by industries, some of 

which produce multiple commodities (generally a primary commodity and one or more 

byproducts). The depiction of an industry’s commodity production is shown under 

Customize > Commodity Production (see screen shot below). For example, the food 

sold–farm sector in our case produces mostly food sold farm commodities (97.5%), along 

with some forestry, fishing, and hunting products (1.0%), support activities for 

agriculture and forestry (1.0%), and arts, entertainment and recreation products or 

services (0.5%). Without additional data showing otherwise, we assume that the food hub 

farm sector produces only its primary commodity (i.e., agricultural products) with no 

byproducts. As byproducts for most agricultural production sectors represent a fairly 

small percentage of total commodities produced, this should not materially affect the 

impact results. In our application, we replaced the original “tobacco farming” sector in 

IMPLAN (zero production) with our food hub farm information. While zero in 

production, IMPLAN still prepopulates the allocation of total output to commodities 

produced using national data. This step changes the default values such that only the 

primary commodity is produced. Detailed instructions are given in Box 4 and a screen 

shot (Figure 4) is provided below. Once you complete this part, you must reconstruct 

your model before continuing.
Box 4. Customizing the food hub farm sector’s commodity production

1. Customize > Commodity Production > Select Food Hub Farm Sector (formerly 

“tobacco farming”) > Adjust coefficients so that the sector produces only its 

primary commodity (no byproducts). 

2. The primary commodity will still be listed as “tobacco farming” although this is 

the food hub farm commodity. Enter 1 for this commodity, make sure the fixed 

box is checked, and click Balance. The other byproducts should go to zero. Click 

Save > Close.

3. Reconstruct model (Options > Construct > Multipliers). 

Figure 4. Customizing the food hub farm sector’s commodity production

The final part of this step is to customize trade flows to reflect the portion of commodities 

purchased by the food hub farm sector from local sources.23 Specifically, you will edit the 

regional purchase coefficients (RPCs, see glossary for definition) of the food hub farm 

sector for each commodity purchased by them to reflect the amount they procure locally. 

The estimated RPCs will be based on the data you collect from the food hub farms on the 

                                                            

23 For more information about editing trade flow information, see IMPLAN’s guide: 

<http://implan.com/v4/index.php?option=com_content&view=article&id=548:548&catid=235:KB18> 
percentage of expenditures procured locally (See “Local Expense” column in Table 2). 

Note that the RPCs can be among the most important and often most difficult parameters 

to correct to local conditions based on local data rather than using the IMPLAN 

estimates. By default in IMPLAN, RPCs for a commodity are assumed the same across 

all sectors that purchase them (e.g., the RPCs for the commodity utilities are the same 

across all sectors—each sector procures the same proportion of utilities locally). In this 

part, you will edit the RPCs for only the food hub farm sector’s commodities purchased. 

Box 5 details the steps, while the screen shot shows the results for purchases of food 

sold–farm products by the food hub farm sector in our simple example. After editing all 

RPCs you must reconstruct your model. A screen shot for purchases of food sold–farm 

commodities is shown is shown in Figure 5. 

Box 5. Customizing the food hub farm sector’s regional purchase coefficient (RPC)

1. Select Customize > Trade Flows. 

2. Select Industry/Institution RPC tab. 

3. For each commodity purchased by the food hub farm sector, select the 

commodity from the dropdown list. 

4. Under Industry Intermediate Demand for Commodities, scroll down to Food Hub 

Farm sector and edit RPC for that commodity. The RPC should be entered as a 

decimal. Make sure to click Save after each edit. 

5. Repeat steps 3 and 4 for each commodity purchased. 

6. Reconstruct model (Options > Construct > Multipliers). 
Figure 5. Customizing the food hub farm sector’s regional purchase coefficient (RPC)

The development of your model is now complete. You can review the multipliers of the 

final sectors in your model, by going to Explore > Multipliers. A useful comparison is 

between the computed multipliers in the edited food sold farm sector and the newly 

created food hub farm sector.

IMPACT ANALYSIS 

Once the IMPLAN model is customized to reflect a distinct food hub farm sector, we can 

perform the impact analysis. Recall from our discussion earlier that a food hub sector 

does not exist within IMPLAN. Accordingly we model a hypothetical change in final 

demand for food hub goods and services based on how its revenues are allocated to 
component expenditures, an analytically equivalent method known as “analysis-by-parts” 

(ABP).24 

As a food hub’s production ramps up due to the increase in final demand (i.e., the direct 

effect), its expenditures represent increasing demand for inputs from other local sectors 

(i.e. the indirect effect) and from imports. It is the portion of spending that occurs locally 

that drives impacts. In other words, only a portion of input expenditures are incurred with 

local firms, and it is only these local expenditures that are included in the impact analysis 

(i.e., as first-round indirect effects). The remaining non-local purchases represent 

leakages from the local economy and are excluded. In general, only a fraction of 

payments to value added expenditures are regarded as generating impacts. While 

spending on employee compensation and proprietor income generate induced impacts 

and are included, payments to other property type income and indirect business taxes are 

generally excluded from impact analyses. Similarly, any payments to government 

institutions (e.g., for municipal water) are excluded. This is because government policy 

decisions and long- term investment income do not typically follow the same re-spending 

patterns as other kinds of value added incomes/revenues. 

An example may be helpful here. Consider a $100 increase in final demand for food hub 

products. To satisfy this increase in demand, the food hub spends $60 to input sectors 

(such as products from farms and non-farm vendors, insurance, utilities), $25 to 

employee compensation, $10 to proprietor’s income and $5 on indirect business taxes. 

The $100 represents the direct output effect. One-half of the inputs ($30) are procured 

locally, the other $30 represent leakages. The $25 and $10 value added components are 

included in the analysis, but the $5 payment is not. Applying these values to our model 

will generate the entire cumulative impact from all inter-industry transactions. 

Quantifying the Direct Effects 

To quantify a reasonable scenario for evaluating an increase in final demand for food hub 

products and services, we recommend collecting information from customers as 

discussed above. In our example food hub (Table 1), let’s suppose that customers 

reported an interest in purchasing an additional 25% of products from the food hub, or a 

$250,000 increase in demand for food hub products. The positive shock ($250,000) is 

then allocated into the corresponding industry sectors (intermediate inputs) and value 

added components based on the food hub’s pattern of expenditures (Table 1).25 The direct 

                                                            

24 See IMPLAN’s “Case Study: Analysis-By-Parts” for more information: 

<http://implan.com/v4/index.php?option=com_multicategories&view=article&id=730:case-study-analysisby-parts&Itemid=71>. 

25 The increase in output may also need to be allocated to institutional purchases if applicable; e.g., 

purchases directly from households. 
effect for output is the $250,000, while the direct effect for employment, labor income, 

and total value added can be computed based on the data in Table 1. Assuming 

proportional changes, the 25% expansion will have a direct employment effect of 1 

employee (0.25 x 4), a direct labor income effect of $47,500 (0.25 x (160,000+30,000)), 

and a direct total value added effect of $62,500 (0.25 x (160,000 + 30,000 + 60,000)). 

Quantifying Opportunity Costs 

In addition to the positive shock from an increase in demand for food hub products, we 

need to consider possible negative impacts due to decreased spending by purchasers in 

other sectors as a consequence of an increase in purchases of food hub products. From the 

customer survey results, you should have generated an estimate of how much the increase 

in demand for food hub purchases also involves a shift of demand away from other 

sectors. For our analysis, we assume that these offsets would occur in the wholesale trade 

sector (in our case study, business customers reported decreasing purchases from other 

distributors, which are included in IMPLAN’s wholesale trade sector). Thus, in addition 

to the positive shock in the food hub sector, we consider a simultaneous negative shock to 

the wholesale trade sector in order to account for the opportunity cost. For ease of 

exposition, assume for our sample food hub that, on average, for every additional dollar 

of food hub products purchased, ten cents worth of product purchases are decreased in 

the wholesale trade sector (i.e., a 10% offset). With an increase of $250,000 of output of 

food hub products, this would imply a minus $25,000 shock to the wholesale trade sector. 

Analysis by Parts (ABP) 

You now have sufficient information to construct your impact analysis in IMPLAN using 

ABP. To do so requires you to set up activities that reflect the industry spending pattern 

of the food hub for intermediate input expenditures, labor income changes for employee 

compensation and proprietor income, and the opportunity costs. In each of the activities 

you enter in a set of events, and then define a scenario that includes your activities. After 

analyzing your scenario the scenario results are displayed in IMPLAN. The detailed steps 

are outlined in Box 6 below, along with screen shots for the sample food hub scenario 

(Figures 6a-6e). Before you begin, make sure the Advanced box is checked under User 

Preferences (File > User Preferences > Analysis tab). 

 
Box 6. Analysis-by-Parts Impact Assessment

1. Define Industry Spending Pattern. 

a. Click Setup Activity > New Activity > Industry spending pattern (entitle it: 

“Hub Input Spending Pattern per dollar of output”). Set Activity level to 

amount of increase in final demand for food hub products and services, the 

direct output effect, in our case $250,000. (Adjust this number based on your 

customer survey results). Click Save

b. Click New Event for each intermediate input sector purchase. Enter food hub 

intermediate input spending coefficient, along with the Local Purchase 

Percentage (LPP); the amount of each input commodity the food hub procures 

locally (See Table 1). Note that the food hub will be purchasing farm products 

from the newly created food hub farm sector rather than the food sold farm 

sector. Purchases of food hub farm commodities will be reflected under 

‘tobacco farming’ – that is the food hub farm commodity. 

2. Define Labor Income Change for Employee Compensation 

a. Click Setup Activity > New Activity > Labor Income Change (entitle it “Food 

Hub Labor”). Set Activity Level to 1.000. Click Save. 

b. Click New Event and select sector 5001 Employee Compensation. Enter change 

in employee compensation associated with the increase in final demand for 

food hub products and services. In our case, the proportional change in 

employee compensation is 0.25*$160,000 = $40,000. Edit LPP if less than 

100%. We assume the increase is all local. 

3. Define Labor Income Change for Proprietor Income 

a. Click Setup Activity > New Activity > Labor Income Change (entitle it “Food 

Hub Proprietor Income”). Set Activity Level to 1.000. Click Save. 

b. Click New Event and select sector 6001 Proprietor Income. Enter change in 

proprietor income associated with the increase in final demand for food hub 

products and services. In our case, the proportional change in proprietor 

income is 0.25*$30,000 = $7,500. Edit LPP if less than 100%. We assume the 

increase is all local. 

4. Define Opportunity Cost 

a. Click Setup Activity > New Activity > Industry spending pattern (entitle it: 

“Opportunity Cost Wholesale Trade”). Set Activity level to the negative of the 

amount of increase in final demand for food hub products and services, in our 

case $-250,000. The proportion of offset is defined next. Click Save

b. Click New Event and select the Wholesale Trade sector. Enter the proportional 

offset under the coefficient column based on the customer survey data. In our 

case, 0.10. As we assume all of these offsets are from other local purchases, we 

leave the LPP at 100%. 

5. Click Next

6. Analyze Impact Scenarios 
a. After clicking Next you will be brought to the Analyze Scenarios screen.26 If 

this is the first scenario you are establishing, a screen will open asking you to 

name the New Scenario and enter the Scenario Level. Choose a name 

descriptive enough to understand its components. We name our scenario 

“Food Hub $250K output with OppCost”. Set the level to 1.000. Click Save. 

b. The activities you previously set up are now shown on the left side of the 

screen. Select all activities you would like to use in your scenario. In our case 

all four activities are selected. 

c. Click Analyze Single Region 

d. Click Yes to view results 

Figure 6a. Analysis-by-Parts Impact Assessment, Industry Spending Pattern, Step 1

                                                            

26 For more information on analyzing scenarios within IMPLAN, see: 

<http://implan.com/v4/index.php?option=com_multicategories&view=article&id=524:524&Itemid=14>. 
Figure 6b. Analysis-by-Parts Impact Assessment, Employee Compensation, Step 2 
Figure 6c. Analysis-by-Parts Impact Assessment, Proprietor Income, Step 3 
Figure 6d. Analysis-by-Parts Impact Assessment, Opportunity Cost, Step 4 
Figure 6e. Analysis-by-Parts Impact Assessment, Analyze Single Region, Step 5

INTERPRETING YOUR RESULTS 

Congratulations! You have made it to the fun part, interpreting your results. IMPLAN 

displays results in a summary report that covers the total economic impacts, detailed 

industry reports that can be sorted by impact type (employment, output, labor income, 

and value added), and a tax impact report. By definition, IMPLAN shows no direct 

effects since you defined them. In this methodology, using an ABP approach, you 

provided the first round indirect effects (industry spending pattern) and labor income 

changes that provide induced effects. Following the quantification of direct effects above, 

we show the final summary results in Table 3 below, along with the applicable screen 

shot in Figure 7. 

Summary Results 

The generative impacts of the increase in final demand for food hub products and 

services are displayed in Table 3. As illustrated, for every additional employee added to 

the food hub’s payrolls, an additional 2.5 jobs are generated in backward-linked 

industries (i.e. industries on the supply chain from which the food hub purchases its 

inputs in order to produce its output). In this example, the employment multiplier effects 

are relatively strong, particularly contributing from backward linkages of the food hub 

farm sector (see detailed results). Similarly, the initial $250,000 of direct final demand 
output generates a total of over $385,000 of impact when all backward linked industry 

effects are considered, or an implied multiplier of 1.54. 

Figure 7. Summary Results 

Table 3. Summary Economic Impact Results of $250,000 Increase in Final Demand for 

Food Hub Products and Services, with opportunity costs considered 

Impact Type Employment Labor Income Value Added Output 

Direct Effect 1.00 $47,500 $62,500 $250,000 

Indirect Effect 2.20 $20,170 $25,239 $87,706 

Induced Effect 0.30 $17,770 $31,462 $47,311 

Total Effect 3.50 $85,440 $119,201 $385,017 

Implied Multiplier 3.50 1.80 1.91 1.54 
It may be useful to compare your multiplier results with other industries in your study 

area. However, recall that this analysis importantly includes opportunity costs, so direct 

comparison is problematic. If you want to compute the implied multipliers without 

opportunity costs (for comparison with other industry multipliers), create an additional 

scenario that does not include opportunity cost as one of the activities. The summary 

results for this scenario (follow the same general steps in Box 6, but without selecting the 

opportunity cost activity) are shown in Table 4. The differences in results in Tables 3 and 

4 highlight the importance of considering opportunity costs in your analysis. 

Table 4. Summary Economic Impact Results of $250,000 Increase in Final Demand for 

Food Hub Products and Services, without considering opportunity costs (all final 

demand is new demand)

Impact Type Employment Labor Income Value Added Output 

Direct Effect 1.00 $47,500 $62,500 $250,000 

Indirect Effect 2.40 $33,517 $47,953 $118,233 

Induced Effect 0.40 $21,306 $37,725 $56,728 

Total Effect 3.80 $102,323 $148,178 $424,961 

Implied Multiplier 3.80 2.15 2.37 1.70 

Detailed Industry Impact Results 

Given the objectives of your study, it may also be useful to examine what industries are 

primarily affected via the indirect and induced effects (see glossary for definition). The 

screen shot below (Figure 8) shows the first page of detailed industry results for output 

(considering opportunity costs), ranked by total (indirect + induced) effects. Not 

surprisingly, of the total $135,018 of indirect and induced effects, the top two affected 

industries are the food hub farm ($40,500) and food sold–nonfarm ($27,932) sectors, 

from which the hub’s primary purchases/expenditures accrue. The food sold–farm sector 

(net of the food hub farm sector) also demonstrates relatively large impacts ($13,950), 

primarily due to the purchases of the food hub farm sector from food sold farm (Table 2). 

Given the relatively disaggregate model (390 sectors in our example), you will likely 

want to aggregate some of the other sectors following your model runs, perhaps 

following the two-digit NAICS aggregation scheme, to helpfully summarize your results. 

However, even with the disaggregate model results, one can see that the imputed rental 
activity (all induced), electric power utilities, insurance carriers, and non-depository 

credit intermediaries (mostly indirect), and real estate and other monetary authorizes 

(mostly induced) are also relatively important sectors affected by food hub expansion. 

Note that the net change in the Wholesale Trade sector was -$21,928 (not shown), as 

some of the -$25,000 of opportunity cost are offset by positive influences to the 

wholesale trade sector from food hub expansion by other indirect industry effects. 

Figure 8. Detailed Industry Results (Output)