April 2017 EB 2017-01

A Practitioner’s Guide to Conducting 

an Economic Impact Assessment of 

Regional Food Hubs using IMPLAN: 

A Systematic Approach

Todd M. Schmit

and

Becca B.R. Jablonski

Charles H. Dyson School of Applied Economics and Management

College of Agriculture and Life Sciences

Cornell University Ithaca, New York 14853-7801
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, sex, age or handicap. The 

University is committed to the maintenance of affirmative action programs 

which will assure the continuation of such equality of opportunity.
A Practitioner’s Guide to Conducting an Economic Impact Assessment of Regional Food 

Hubs using IMPLAN: A Systematic Approach

Todd M. Schmit1,3 and Becca B.R.2 Jablonski

April 17, 2017

1 Associate Professor, Charles H. Dyson School of Applied Economics and Management, Cornell University

2 Assistant Professor, Department of Agricultural and Resource Economics, Colorado State University

3 Author Contact: 350A Warren Hall, Charles H. Dyson School of Applied Economics and Management, Cornell University, 

Ithaca, NY 14853-7801, tms1@cornell.edu, +1-607-255-3015

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. A companion file detailing the 

computations completed for the example used in this guide is included with the written guide (PGUIDE 

companion.xlsx).

Any users wanting to follow the recommended procedures outlined in the Practitioner’s Guide for modifying 

the ACCESS data in IMPLAN must obtain an amended license agreement by IMPLAN.

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.
i

TABLE OF CONTENTS

GLOSSARY III

1. INTRODUCTION 1

2. DEFINING THE STUDY AREA 2

FUNCTIONAL ECONOMIC MARKET AREA 2

3. DEFINING FOOD HUB TRANSACTIONS 3

3.1 DATA COLLECTION - FOOD HUB 3

TABLE 1. SAMPLE FOOD HUB BUSINESS EXPENDITURE PROFILE AND MAPPING TO IMPLAN CATEGORIES (2014 DATA YEAR). 5

TABLE 2. MARGIN CALCULATIONS FOR FOOD HUB EXPENDITURES. 6

TABLE 3. SUMMARY OF FOOD HUB SALES, INCLUDING MARGINING AND EXPORTS. 7

3.2 DATA COLLECTION - FOOD HUB FARM 8

TABLE 4. SAMPLE AVERAGE FOOD HUB FARM EXPENDITURE PROFILE AND MAPPING TO IMPLAN CATEGORIES (2014 DATA YEAR). 9

TABLE 5. MARGIN CALCULATIONS FOR FOOD HUB FARMS. 10

TABLE 6. SUMMARY OF AVERAGE FOOD HUB FARM SALES, INCLUDING MARGINING AND EXPORTS. 10

3.3 DATA COLLECTION – FOOD HUB CUSTOMERS 11

4. CONSTRUCTING THE MODEL IN IMPLAN 12

4.1 CREATING THE MODEL AND AGGREGATION SCHEME 12

4.2 CREATING A FOOD HUB FARM SECTOR 12

4.2.1 CUSTOMIZE STUDY AREA DATA 12

FIGURE 1. CREATING THE FOOD HUB FARM SECTOR. 14

FIGURE 2. CREATING THE OTHER FARM SECTOR. 15

4.2.2 CUSTOMIZE INDUSTRY PRODUCTION 16

TABLE 7. EDITING GROSS ABSORPTION COEFFICIENTS (GACS) FOR INDUSTRIES PURCHASING FOOD HUB FARM COMMODITY. 16

FIGURE 3. EDITED FOOD HUB FARM PRODUCTION FUNCTION. 18

FIGURE 4. EDITED RETAIL – FOOD AND BEVERAGE STORES PRODUCTION FUNCTION. 19

4.2.3 CUSTOMIZE COMMODITY PRODUCTION 20

FIGURE 5. COMMODITIES PRODUCED BY THE OTHER FARM SECTOR. 21

FIGURE 6. COMMODITIES PRODUCED BY THE FOOD HUB FARM SECTOR. 22

4.2.4 CUSTOMIZE TRADE FLOWS 23

FIGURE 7. RPCS AND DEMAND FOR OTHER FARM COMMODITIES. 25

FIGURE 8. RPCS AND DEMANDS FOR FOOD HUB FARM COMMODITY, PRIOR TO CUSTOMIZING TRADE FLOWS. 26

FIGURE 9. CORRECTED RPCS BY INDUSTRIES FOR THE FOOD HUB FARM COMMODITY. 27

FIGURE 10. CORRECTED RPCS BY FOOD HUB FARM INDUSTRY FOR RETAIL GASOLINE. 28

TABLE 8. OTHER FARM AND FOOD HUB FARM REGIONAL COMMODITY BALANCES. 29

TABLE 9. OTHER FARM AND FOOD HUB FARM REGIONAL INSTITUTIONAL DEMAND. 31

FIGURE 11. FINAL DEMANDS, SUPPLIES, AND RPCS FOR THE FOOD HUB FARM COMMODITY. 32

FIGURE 12. FINAL DEMANDS, SUPPLIES, AND RPCS FOR THE OTHER FARM COMMODITY. 33

5. IMPACT ANALYSIS 34
ii

5.1 QUANTIFYING THE DIRECT EFFECTS 34

5.2 QUANTIFYING OPPORTUNITY COSTS 34

5.3 ANALYSIS-BY-PARTS (ABP) 35

FIGURE 13. INDUSTRY SPENDING PATTERN ACTIVITY FOR THE FOOD HUB INCREASE IN FINAL DEMAND. 37

FIGURE 14. EMPLOYEE COMPENSATION ACTIVITY FOR THE FOOD HUB INCREASE IN FINAL DEMAND. 38

FIGURE 15. PROPRIETOR INCOME ACTIVITY FOR THE FOOD HUB INCREASE IN FINAL DEMAND. 39

FIGURE 16. OPPORTUNITY COST ACTIVITY FOR THE FOOD HUB INCREASE IN FINAL DEMAND. 40

FIGURE 17. THE ANALYZE SCENARIO SCREEN AFTER SELECTING ACTIVITIES. 41

FIGURE 18. THE SCENARIO RESULTS SCREEN, SUMMARY RESULTS. 42

5.4 INTERPRETING RESULTS 43

5.4.1. SUMMARY RESULTS 43

TABLE 10. SUMMARY OF IMPACT RESULTS, WITH AND WITHOUT OPPORTUNITY COSTS. 43

5.4.2 DETAILED RESULTS 44

FIGURE 19. THE SCENARIO RESULTS SCREEN, DETAIL RESULTS FOR OUTPUT. 45

6. SUMMARY 3

7. REFERENCES 3
iii

GLOSSARY

The definitions below are adapted from IMPLAN’s online glossary (available at http://support.implan.com), as well 

as IMPLAN Professional, Version 2.0, 3rd edition (2004). 

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.

Aggregation can be a useful tool in IMPLAN to aggregate industry sectors and reduce the number of industries. It 

does, however, introduce some aggregation bias.

Commodity—a product or service that may be produced by one or more 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). 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 local region. 

Food Hub Farm—a farm that sells product(s) to one or more food hubs. Producers in this category need not sell 

products exclusively to food hubs; rather, these sales are included as part of its marketing portfolio. 

Food Hub Farm Industry – the industry constructed, using the example Food Hub Farm data, to represent all Food 

Hub Farms selling to the example food hub in our study area. The representative Food Hub Farm data is scaled up 

to an industry level based on the number of Food Hub Farms (in our example, 50). This industry includes a portion 

of the default IMPLAN sectors 1-4, 6, and 10-14 (sectors 5, and 7-9 are tiny or do not exist in our study area example 

(New York)), and is representative of the types of farm industry firms the food hub purchases (fresh) food products 

from for resale. Initially aggregating these default IMPLAN sectors results in an aggregated industry we define as 

the Farm Industry. Subsequently, this is separated into the Food Hub Farm Industry and the Other Farm (Other 

Farm) Industry. 

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

Manufactured Food Industry —the industry constructed by aggregating the default IMPLAN food manufacturing 

industries 67-73 and 76-105, and is representative of the types of processed food industry firms the food hub 

purchases processed food products from for resale. It excludes the animal food and beverage manufacturing 

industries.

Margins—the value of the retail, wholesale, and transportation services provided in delivering commodities from 

producers’ establishments to purchasers. 

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.

• Direct effects—changes in the industries to which a final demand change is made

• Indirect effects—changes in backward linked industry purchases as they respond to the new demands of 

the directly affected industries

• Induced effects—changes in spending from households as income increases or decreases due to the 

changes in production

• 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.
iv

• Type II multiplier—captures the direct, indirect, and induced effects. Whereby, the household sector in 

endogenous to incorporate the household spending effects. 

• Type SAM multiplier—uses all information about the institutions selected to be included in the predictive 

model. The SAM has an input-output model at its core, but because the SAM distinguishes household 

purchasing patterns by income group, the multipliers based on the SAM reflect the ripple-effects 

throughout the economy with somewhat greater precision than do those based in an I-O model.

Production Function—this functions 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 income (i.e., self-employment 

income, including salary, benefits and total contributions to Social Security/Medicare), other property type income 

(e.g., dividends, interest, rent, corporate profits, and capital depreciation), and taxes on production and imports 

less subsidies (i.e., all business taxes and fees paid to governments including sales and excise taxes). 
1

A Practitioner’s Guide to Conducting an Economic Impact Assessment of Regional Food 

Hubs using IMPLAN: A Systematic Approach

1. 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 community economic impacts. Yet 

prior to Schmit, Jablonski, and Kay (2016), there had been no rigorous economic impact assessments of food hubs, 

nor had a replicable framework been proposed for their assessment. This practitioner’s guide is complementary to 

Jablonski, Schmit, and Kay (2016) and provides an update to the process described there within. The guide has also 

been updated from its original version (Schmit, Jablonski, and Kay, 2015) to reflect improvements in the 

methodology that (i) better accounts for defining institutional demands of new industries created (i.e., a Food Hub 

Farm industry in our example), and (ii) improves allocation decisions amongst intermediate input purchases (via 

gross absorption coefficients) and value added components. The guide 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.

The process proposed for creating a new industry requires editing model parameters from within and outside (via 

Microsoft ACCESS) the IMPLAN software. The process proposed for creating new industries, or disaggregating 

existing industries into separate segments, is relevant to any modeling exercise in IMPLAN where this is approach 

is necessary to the research objectives. Any users wanting to follow the recommended procedures outlined in the

Practitioner’s Guide for modifying the ACCESS data in IMPLAN must obtain an amended license agreement by 

IMPLAN.

This guide is intended for individuals who are familiar with IMPLAN software and data. In addition to the data 

available for purchase from IMPLAN, the proposed approach requires primary financial data collection from the

food hub, the farm and non-farm product suppliers to the food hub, and customers of the food hub. While timeconsuming and costly to collect, the information can be used to better assess the impacts of food hub activities with 

data particular to a given hub (or hubs) and the suppliers they procure goods from for resale. In addition, using 

more informed estimates of purchasing and sales activities, the value of the inter-industry linkages within the local 

economy resulting from expanded final demand for food hub goods and services can be estimated with greater 

precision. We have included sample tables containing the type of data you will need to make the necessary 

modifications within your models.

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 funding used to develop or expand an existing hub, the economic impact 

assessments approach is an arguably more useful and accurate approach as it better provides for an assessment of 

the net changes in economic activity.1

 

1 See Watson, et al. (2007) for more information, as well as at http://support.implan.com.
2

2. DEFINING THE STUDY AREA

The first step in conducting economic impact assessments is to define the appropriate study area; i.e., the 

boundaries of the local economy you are investigating. 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 effects 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. A useful section in IMPLAN Professional, Version 2.0 (IMPLAN 2004) discusses 

how best to define a project area (Ch. 9, p. 107).

When defining the study area, it is useful to consider the 

availability of secondary data for the region of interest. The 

methodology described in this guide uses secondary data 

available from IMPLAN, which is available by zip code, 

Congressional district, county, and state levels, as well as the 

United States as a whole. It is also easy to construct tailored 

regional models using multiple counties or states within the 

IMPLAN software. Other input-output models and/or regionspecific multipliers are available (e.g., RIMS II or REMI). However, 

IMPLAN software and data is widely used within industry, 

government, and academia. Though data are available at a subcounty level in IMPLAN, we advise using the concept of functional 

economic market areas (FEMAs) to better guide decisions about 

the selection of a study area. A FEMA 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. It is 

unlikely that a sub-county or even county area (particularly if 

rural) is likely to fit this definition, nor provide for any truly useful 

information when utilized in isolation in assessing impact. 

One should also consider the use of the assessment when 

determining an appropriate study 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 your area by 

state boundaries may make sense. Or, if the food hub is funded, 

in part, by a selection of counties, then a multiple-county region 

may be more appropriate. 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 that encompasses all of the 

participating farms may be best. However, you should also keep 

in mind the residential locations of the farm and food hub 

employees, as their spending patterns are important to your study 

results.

Defining the appropriate study area is ultimately the choice of 

the researcher, yet it 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 and, therefore, allowing the 

reader to have a better understanding of the range of potential 

impacts See, for example, Gunter and McFadden (2012).

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. They

are bound together by a relatively contained 

and cohesive network of trade links—the buying 

and selling of raw materials, industrial and 

consumer goods and services, and labor. In the 

United States, most county boundaries are not 

reflective of contemporary economies and, 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 
3

3. DEFINING FOOD HUB TRANSACTIONS

The second step in an impact assessment is to define the industry (or industries) of interest and its (their) linkages 

with other industries. This is not a straightforward exercise in the case of food hub assessments as a food hub 

industry and its transactions with other industries does not exist. Accordingly, additional information must be 

collected to construct a food hub industry or, alternatively and as described here, to describe the nature of all of 

food hub’s transactions and apply analysis-by-partsin IMPLAN. We recommend data collection from three sources: 

(i) the food hub(s) that you are analyzing, (ii) the farms (and potentially local processors) that sell product(s) to the 

food hub(s) for resale, and (iii) the customers that purchase product from the food hub(s). 

3.1 Data Collection - Food Hub

The approach outlined here can be applied to analyze the impact of a single food hub or multiple food hubs. You 

will need to collect data to understand the expenditure pattern(s) of the food hub(s) you are evaluating; i.e., to 

understand the input requirements (how much and where from) of the food hub for every dollar of its sales. In 

addition, you will need to collect sales information that describes the industries and/or institutions the food hub 

sells to, along with the amount of those sales that accrue within the defined study area.

To begin, ask the food hub(s) for information generally contained in their profit and loss (P&L) statement or similar

financial statements. This may require a confidentiality agreement that describes use and disclosure procedures. A 

P&L statement summarizes the revenues and expenses incurred during a specific period. For the purpose of this 

assessment, it is most useful to use the most recent year of operation. 

Sufficient financial detail is needed to be able to reasonably assign (or map) expenditures and sales categories to 

corresponding IMPLAN sectors. Keep in mind that as a general rule, the bigger the category, the more affect it will 

have on the results, and the more important it is to work to get it right. Currently, IMPLAN divides the economy 

into 536 industries, which do not always translate neatly to the ways that businesses categorize expenditures and 

sales. A NAICS/IMPLAN bridge table is available from IMPLAN that can assist in these mapping efforts.2 In Table 1, 

we provide an example for some common expenditure categories. Knowing what types of sellers the food hub buys 

products or services from is also important. For example, are fuel purchases from a retail (industry code 402) or 

wholesale (industry code 395) provider?

You will also need to know the percentage of expenditures for each category that was procured within your local 

study area (i.e., the local purchase percentage or LPP in IMPLAN language). This type of information is not generally 

included in typical financial statements, nor is it often easy to tabulate. You will need to work with the food hub 

operator to determine the LPPs. Oftentimes, it is sufficient to get a “reasonable” estimate. Numerous categories 

may be ‘all’ or ‘nothing’ (i.e., LPPs of 100% or 0%, respectively). A helpful format to record these items is illustrated 

in Table 1. Note, some expenditure categories from the food hub may map to more than one IMPLAN sector

depending on the specificity of the categories, or more than one food hub category may map to the same IMPLAN 

sector. Best judgement should be applied by the researcher in how to allocate across IMPLAN sectors or to 

aggregate those sectors in IMPLAN.

Purchases from retailers or wholesalers require margining, as only the margin (i.e., sales less cost of goods sold) is 

included in these industries within IMPLAN. How you margin depends on whether the items purchased are known 

(e.g., gasoline) or unknown (e.g., supplies) so that one can or cannot identify the specific production sector where 

these products originated. IMPLAN provides helpful guidance documents on this process when setting up activities 

and events within the IMPLAN software.3 However, for our purposes in creating the Food Hub Farm sector in 

IMPLAN, we must compute the margin and LPP components explicitly to define the sector’s production function.4

 

2 Go to support.implan.com and look under Resources > Downloads > 536 Sectoring Scheme > NAICS to IMPLAN 536 Bridge.

3 Go to support.implan.com and search for Margining: When the Item Being Purchased is Known and Margining: When the Item 

Being Purchased is Unknown.

4 Margins can be found in the CommonMarginstable when opening your model in ACCESS, and within menu screens in IMPLAN 

(Event Options > Edit Event Properties > Margins). IMPLAN’s SAM model values for LPPs can be found within menu screens at 

Event Options > Edit Event Properties > Local Purchase Percentage.
4

The computations are also necessary to apply analysis-by-parts procedure to estimate food hub impacts. The margin 

calculations in Table 2 demonstrate the approach. For fuel purchases ($75,000), we are told that 75% of those 

purchases were local (i.e., from local retail gasoline stations). Applying margins separates the cost for the fuel 

produced (sector 156) and the retail (402), wholesale (395), and relevant transportation (408-413) margins. We do 

not know, however, how much of the component purchases other than retail were local (i.e., the amount fuel 

purchases by retailers from local wholesalers, or the amount of fuel purchases by wholesalers from local refineries), 

but IMPLAN can estimate that for you for your study area. If fuel purchases were from a wholesaler, a similar 

procedure would dictate, albeit with margins apportioned between production, wholesale, and transportation only.
5

Table 1. Sample Food Hub Business Expenditure Profile and Mapping to IMPLAN Categories (2014 data year).

Food hub expenditure category IMPLAN category Expenditure

Local 

Purchase 

Percentage

Expense 

Share

Food purchases for resale -nonfarm Manufactured food, constructed by authorsa $ 250,000 20 0.250

Food purchases for resale - farm Food Hub Farm, constructed by authorsa $ 150,000 90 0.150

Fuel purchases from retailers Petroleum refineries (156) – apply margins b $ 75,000 75 0.075

Lease trucks Automotive equipment rental and leasing (442) $ 30,000 100 0.030

Insurance Insurance carriers (437) $ 25,000 100 0.025

Building repairs and maintenance Main. and repair of nonresidential structures (62) $ 20,000 100 0.020

Utilities Electric power transmission and distribution (49) $ 7,000 100 0.007

Business supplies from wholesalers Wholesale trade (395) – apply margins c $ 3,000 50 0.003

Total intermediate input purchases $ 560,000 0.560

Wages and benefits Employee compensation (Total employees = 4) $ 260,000 100 0.260

Taxes (all) Tax on production and imports d $ 30,000 70 0.030

Depreciation (noncash) Other property type income (capital consumption allowance) e $ 25,000 na 0.025

Interest payments Other property type income (net interest) f $ 10,000 100 0.010

Net income to owner(s) Proprietor income g $ 115,000 100 0.115

Total Value Added $ 440,000 0.440 a The food hub is assumed to purchase food products from food hub farms; e.g., fresh fruits and vegetables, as well as manufactured food products; e.g., processed meat 

products, jellies, flour. Additional explanation is included in the text. b As these are retail purchases, margins are applied. Since the purchased items are known (i.e., fuel), we margin back to the producing sector and account for local 

purchasing percentages (LPP). See Table 2 for details.

c As these are wholesale purchases, margins are applied. Since the purchased items are not specific enough to be attributed to a particular producing sector, we only 

consider the average margin value for wholesale trade for impact. See Table 2 for details.

d All business taxes and fees paid to governments, including sales and excise taxes, net of subsidies.

e Depreciation is a noncash expenditure; however, it is usually a tax-deductible expense used by firms in determining profits. It should be allocated to other property type 

income (as a capital consumption allowance, usually modified to a market value basis). OPTI is not considered in estimating impacts.

f Interest payments are not included in intermediate input spending, rather net interest (business interest payments less business interest receipts) is included in other 

property type income. We assume no interest receipts. g The remaining balance after computing total outputs (sales) less intermediate input expenditures and the other allocations to value added.
6

Table 2. Margin Calculations for Food Hub Expenditures.

a

IMPLAN category Margin Expenditure

Local Purchase 

Percentage Expense share

Fuel purchases from retailers ($75,000) b

Petroleum refineries (156) 0.6284 $ 47,130 0.86 0.047130

Wholesale trade (395) 0.1894 $ 14,205 94.74 0.014205

Retail gasoline stations (402) 0.1667 $ 12,503 75.00 0.012503

Rail transportation (409) 0.0010 $ 75 45.30 0.000075

Water transportation (410) 0.0021 $ 158 71.93 0.000158

Truck transportation (411) 0.0076 $ 570 51.43 0.000570

Pipeline transportation (413) 0.0048 $ 360 17.06 0.000360

Business supplies from wholesalers ($3,000) c

Wholesale trade (395) 0.1730 $ 519 50.00 0.000519

Miscellaneous manufacturing (394) 0.8270 $ 2,481 0.00 0.002481 a Margins and local purchase percentages (LPPs) taken from IMPLAN default estimates, except for the LPPs known from the 

primary data. Expense shares calculated based on $1,000,000 output for the food hub. b Since the specific products purchased from the retailer are known, we margin back to the producing industry.

c Since the specific products purchased from the wholesaler are unknown, we use the average wholesale trade margin in 

IMPLAN and the LPP from the primary data. The remaining allocation ($2,481) is lumped into a general industry and assigned 

a zero LPP. This will not affect the impact results (all leakage), but is needed to complete the production function.

The more detailed the information you know about the products being purchased, the more you are able to 

accurately assess impact. For the fuel purchase example, 75% of the retail margin is considered in local impact, as 

well as 95% of the wholesale trade margin ($14,205) and portions of the transportation and production sectors to 

the degree they occur locally. If all we knew was $75,000 of purchases at retail gasoline stations, that could include 

fuel, but also hot dogs, air fresheners, funnels, beer, coffee, etc. Unless we know how much was in the various 

components, all we can reasonably count towards impact is average margin value for retail gasoline stations, or 

0.1160 per IMPLAN multiplied by the LPP. Depending on the level of purchases and the extent they are local, this 

may or may not be of significance to your overall assessment. The unknown business supplies purchased from 

wholesalers ($3,000) highlights this second case. Are the supplies purchased pencils, staples, toner, or shipping 

containers? Since we do not know, the best we can do is apply the average wholesale trade margin for impact. In 

our case 0.1730, or $519, less the amount procured from outside the study area (50%), leaving $260 to include for 

impact. While this may seem extreme, it may be entirely realistic given the study area and, more to the point in our 

example, the total purchases by the hub to wholesalers represented only 0.3% of total hub outlays, so very little 

impact from these purchases was expected to begin with. 

To complete the outlays by the food hub, one must consider expenditures and allocations to various value added 

components; i.e., employee compensation (EC), proprietor income (PI), other property type income (OPTI), and 

taxes on production and imports (TOPI). EC (including benefits paid to employees) and TOPI are commonly included 

in a firm’s financial statements and can be assigned directly. All business taxes and fees paid to governments, 

including sales and excise taxes net of subsidies are recorded under TOPI. Table 1 identifies the number of jobs 

supported by the food hub (full- or part-time), which will become useful when computing jobs impacts. Payments 

to contractors or consultants who are not employees of the firm are not included in EC, but as intermediate input 

expenditures to a relevant service category (e.g., management consulting services, code 454). 

Depreciation is a noncash expenditure (no impact); however, it is usually a tax-deductible expense used by firms in 

determining profits subject to income taxes. Accordingly, if depreciation is included in the financial statements, it 

should be allocated to OPTI, as a capital consumption allowance, usually modified to a market value basis. 

Dividends, rental income, and corporate profits are also be allocated to OPTI. Finally, interest payments (on debt) 

are not included in intermediate input spending, but rather as net interest (business interest payments less business 

interest receipts) and included in OPTI. OPTI is generally not considered in estimating impact.
7

In our example, the remaining balance of fundsfrom total output (sales) is allocated to PI; i.e., payments to the selfemployed owners of the food hub. Depending on the business structure of the food hub, different allocations to 

value added may be appropriate, e.g., (corporate) profits distributed to shareholders would be in OPTI.

The sales side is similarly constructed (Table 3), now looking forward along the supply chain. To avoid doublecounting as goods move along the supply chain, sales to wholesale and retail trade sectors are margined to reflect 

only the value of the services provided by these sectors in delivering commodities from producers’ establishments 

to purchasers. The values of the commodities (in producer prices) are apportioned to one or more deliveries to final 

demand, depending on the location and allocation of final deliveries; e.g., to households, exports, etc. (Schmit and 

Boisvert 2014). Since we do not know exactly what products are sold to the retail and wholesale firms (i.e., farm or 

processed food products), we weight the relevant margins in proportion to the amounts purchased by the food 

hub. In our example, this results in weighted retail food and wholesale margins of 0.2885 and 0.1004, respectively.5

Exports are computed as total sales less local sales. 

For the hub’s retail sales, the retail margin is $72,128 (0.2885 x 250,000); with 100% of these sales to local retailers, 

this is the margined and final sales to retailers. The balance of the $250,000 is allocated to households, or $177,872 

(all local, no exports) as the producer value of sales attributed to final household demand. In the case where product 

moves from wholesalers and then to retailers, margins must be applied in both the wholesale and retail sectors. 

The wholesale margin here is $20,078 (0.1004 x 200,000); however, 50% goes to local wholesalers ($10,039) and 

50% to non-local wholesalers ($10,039) as exports. The retail margin of these originating sales is $57,703 (0.2885 x 

200,000), assigning 50% to local retailers ($28,851) and 50% ($28,851) to nonlocal retailers (export).

The portion of sales allocated to households after subtracting wholesale and retail margins is $300,092 ($177,872 

from retail and $122,220 from wholesale), where $239,982 is attributed local household demand ($177,872 from 

retail and $61,110 from wholesale) and the balance ($61,110) goes to exports. Summing the individual export 

components yields total exports of $187,500 ($10,039 non-local wholesale margin + $28,851 retail margin of nonlocal wholesale sales + $61,110 household portion of non-local wholesalers + $87,500 nonlocal manufactured food 

sales).

Table 3. Summary of Food Hub Sales, including Margining and Exports.1

Industry/Institution Code

Percent

Local

Gross

Sales

Margined

Sales

Final

Sales

Retail trade - Food2 400 100 $ 250,000 $ 129,831 $ 100,979

Wholesale trade2 395 50 $ 200,000 $ 20,078 $ 10,039

Manufactured Food Mfood 50 $ 175,000 $ 175,000 $ 87,500

Full service restaurants 501 100 $ 150,000 $ 150,000 $ 150,000

Elementary and secondary schools 472 100 $ 225,000 $ 225,000 $ 225,000

Households Hhld 100 $ 300,092 $ 238,982

Export $ 187,500

Total $1,000,000 $1,000,000 $1,000,000 1 To avoid double counting, sales to wholesale and retail sectors are margined to reflect only the value of the services provided 

by these sectors in delivering commodities from producers’ establishments to purchasers. The values of the commodities (in 

producer prices) are apportioned to one or more deliveries to final demand, depending on the location and allocation of final 

deliveries (e.g., to households, exports, etc.). 

2 Margins for farm and manufactured food products are, respectively, retail: 0.3027 and 0.2800; wholesale: 0.1152 and 0.0915 

(2014 model year, New York State). Final retail and wholesale margins are weighted based on the relative purchase amounts 

from these sectors by the food hub (i.e., 0.2885 and 0.1004, respectively).

 

5 Individually from IMPLAN, the retail trade food margin for farm products was 0.3027 and the retail trade margin for 

manufactured food products was 0.2880. Similarly, the wholesale trade margin for farm products was 0.1152 and the wholesale 

trade margin for manufactured food products was 0.0915.
8

3.2 Data Collection - Food Hub Farm

Understanding how farms that sell product 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, 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 likely 

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 business types. As such, assuming that the food hub’s 

insurance company has a similar expenditure profile to that of the relevant insurance industry 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 

different proportions of labor or mechanized equipment than their larger counterparts, thus having a different 

impact on the labor force. Accordingly, we recommend that any food hub assessment involve primary data 

collection from the farms supplying the hub food. Again, as above, the larger the purchases and the more the 

policy/intervention is intended to support that sector, the more important it is to get it right.

Primary data collected from Food Hub Farms are used to come up with an average (representative) farm profile to 

scale up (i.e., by the total number of Food Hub Farms in the study area) and create the Food Hub Farm sector 

(extracted from the aggregate Farm sector). You will need to collect sales and expenditure information from the 

farms selling to the food hub(s) analogous to financial information collected from the food hub itself. Farm 

employees are counted based on the number of paid, full-time, part-time, or temporary positions. Unpaid labor is

excluded. Direct face-to-face contact with the farms is likely the most efficient way of obtaining the information, 

given the complexity of information requested and the wariness of farmers to provide such information in 

traditional telephone, online, or mail surveys. Assurances to the farmers that farm financial information will only 

be presented in aggregate form, with no individual farm data identifiable, is important to communicate. 

Table 4 provides a simple example of an average Food Hub Farm expenditure profile (you will likely have more 

categories). Value added allocations follow similarly to the example for the food hub, and purchases from retail and 

wholesale firms are margined (Table 5). 

Table 6 illustrates the sales side for the Food Hub Farms. Margining sales and the computation of exports follows 

the same procedure as that for the food hub. The only difference here concerns Food Hub Farm sales to the food 

hub. Since we are not creating food hub sector in our model, we cannot allocate these sales to that sector. Instead, 

farm product sales to the food hub are allocated across industries and institutions in the same proportion as are 

the sales allocations for the food hub itself (i.e., based on the last column of Table 3).
9

Table 4. Sample Average Food Hub Farm Expenditure Profile and Mapping to IMPLAN Categories (2014 data year).a

Farm expenditure category IMPLAN category Expenditure

Local 

Purchase 

Percentage 

Expense 

Share

Purchases from other food hub farms Food Hub Farm, constructed by authorsb $ 10,000 100 0.100

Fuel purchases from retailers Petroleum refineries (156) – apply margins c $ 10,000 100 0.100

Ag chemical purchases from retailers Pest. and other ag chemical mfg (172) – apply margins d $ 5,000 100 0.050

Farm supplies from wholesalers Wholesale trade (395) – apply margins e $ 2,000 50 0.020

Contracted freight costs Truck transportation (411) $ 5,000 75 0.050

Automotive and machinery repair Automotive repair and maintenance (504) $ 5,000 100 0.050

Utilities Electric power transmission and distribution (49) $ 3,000 100 0.030

Insurance Insurance carriers (437) $ 2,000 100 0.020

Total intermediate input purchases $ 42,000 0.420

Wages and benefits Employee compensation (Total employees = 3) $ 25,000 100 0.250

Taxes (all) Tax on production and imports f $ 3,000 75 0.030

Depreciation Other property type income (capital consumption allowance) g $ 5,000 na 0.050

Interest payments Other property type income (net interest) h $ 5,000 100 0.050

Net income to owner(s) Proprietor income i $ 20,000 100 0.200

Total Value Added $ 58,000 0.580 a We assume 50 farms in the Food Hub Farm sector, with an average of 3 employees per farm. The total number of jobs for the sector is 150 (50 x 3). Average per farm 

expenditures are scaled similarly to create sector totals, such that total outputs (sales) equals total outlays = 5,000,000 (50 x (42,000 + 58,000)).

b We assume purchases from other farms are only from other Food Hub Farms, rather than to other non-food hub farms or a combination of both. c As these are retail purchases, margins are applied. Since the purchased items are known (i.e., fuel), we margin back to the producing sector and account for local 

purchasing percentages (LPP). See Table 5 for details.

d As these are retail purchases, margins are applied. Since the purchased items are known (i.e., ag chemicals), we margin back to the producing sector and account for 

LPPs. See Table 5 for details.

e As these are wholesale purchases, margins are applied. Since the purchased items are not specific enough to be attributed to a particular producing sector, we only 

consider the average margin for the wholesale sector for impact. See Table 5 for details.

f All business taxes and fees paid to governments, including sales and excise taxes, net of subsidies.

g Depreciation is a noncash expenditure; however, it is usually a tax-deductible expense used in determining profits. It should be allocated to other property type income 

(as a capital consumption allowance, usually modified to a market value basis) and not considered in estimating impacts.

h Interest payments are included as net interest (interest payments less interest receipts) in OPTI. We assume no interest receipts. i The remaining balance after computing total outputs (sales) less intermediate input expenditures and the other allocations to value added.
10

Table 5. Margin Calculations for Food Hub Farms.

a

IMPLAN category Margin Expenditure

Local Purchase 

Percentage

Expense 

share

Fuel purchases from retailers ($10,000 average per farm) b

Petroleum refineries (156) 0.6284 $ 6,284 0.86 0.06284

Wholesale trade (395) 0.1894 $ 1,894 94.74 0.01894

Retail gasoline stations (402) 0.1667 $ 1,667 100.00 0.01667

Rail transportation (409) 0.0010 $ 10 45.30 0.00010

Water transportation (410) 0.0021 $ 21 71.93 0.00021

Truck transportation (411) 0.0076 $ 76 51.43 0.00076

Pipeline transportation (413) 0.0048 $ 48 17.06 0.00048

Ag chemical purchases from retailers ($5,000 average per farm) b

Pesticide & other ag chemical manufacturing (172) 0.5756 $ 2,878 47.42 0.02878

Wholesale trade (395) 0.1224 $ 612 94.74 0.00612

Retail building mat, garden eq., & supplies (399) 0.2822 $ 1,411 100.00 0.01411

Air transportation (408) 0.0082 $ 41 58.43 0.00041

Rail transportation (409) 0.0010 $ 5 45.30 0.00005

Truck transportation (411) 0.0106 $ 53 51.43 0.00053

Farm supplies from wholesalers ($2,000 average per farm) c

Wholesale trade (395) 0.1730 $ 346 50.00 0.00346

Miscellaneous manufacturing (394) 0.8270 $ 1,654 0.00 0.01654 a Margins and local purchase percentages (LPPs) taken from IMPLAN default estimates, except for the LPPs known from the 

primary data. Expense shares calculated based on $100,000 in average output per Food Hub Farm. b Since the specific products purchased from the retailer are known, we margin back to the producing industry. c Since the specific products purchased from the wholesaler are unknown, we use the average wholesale trade margin in IMPLAN 

and the LPP from the primary data. The remaining allocation ($1,654) is lumped into a general industry and assigned a zero LPP. 

This will not affect the impact results (all leakage), but is needed to complete the production function.

Table 6. Summary of Average Food Hub Farm Sales, including Margining and Exports.1

Industry/Institution Code

Percent

Local

Gross

Sales

Margined

Sales

Final

Sales

Final Sales

FH allocated

Food Hub Farms2 FHF 100 $ 10,000 $ 10,000 $ 10,000 $ 10,000

Retail trade - Food3 400 100 $ 15,000 $ 9,686 $ 8,657 $ 8,960

Wholesale trade3 395 80 $ 17,000 $ 1,958 $ 1,567 $ 1,597

Manufactured food Mfood 50 $ 20,000 $ 20,000 $ 10,000 $ 10,263

Full service restaurants 501 100 $ 10,000 $ 10,000 $ 10,000 $ 10,450

Households Hhld 100 $ 25,000 $ 45,355 $ 43,376 $ 44,093

Food Hub4 100 $ 3,000 $ 3,000 $ 3,000

Elem/Secondary schools 472 $ 675

Export $ 13,400 $ 13,963

 Total $ 100,000 $ 100,000 $ 100,000 $ 100,000 1 To avoid double counting, sales to wholesale and retail sectors are margined to reflect only the value of the services 

provided by these sectors in delivering commodities from producers’ establishments to purchasers. The values of the 

commodities (in producer prices) are apportioned to one or more deliveries to final demand, depending on the location 

and allocation of final deliveries (e.g., to households, exports, etc.). 

2 Assumes all sales to Other Farms are to other Food Hub Farms. 3 Retail and wholesale margins for farm products are 0.3027 and 0.1152, respectively (2014 model year, New York State). 4 Since there is no food hub sector in our model, the sales to the food hub are distributed across industries in proportion 

to where the food hub sells its products (Table 3).
11

3.3 Data Collection – Food Hub Customers

The final data needed are from food hub customers (or potential customers) and serve two important purposes. 

First, the information 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 for the food hub sector can grow? Second, it can be used to estimate 

demand-side opportunity costs. 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 likely occur in 

concert with reduced purchases in other sectors. Including this demand-side opportunity cost component in an 

impact assessment is very important. In our case study of Regional Access (Jablonski, Schmit, and Kay, 2016), we 

found that excluding opportunity costs (i.e., reduced purchases by customers from other wholesale food providers) 

overestimated total impacts by over 10%. The sectors in which opportunity costs are reflected may differ depending 

on situation and/or study area. For example, additional purchases from food hubs may increase purchases in the 

retail grocery sector if customers are now more likely to cook at home (i.e., a negative opportunity cost).We 

recommend designing a short survey for food hub customers (or potential customers) that address the following 

questions:

• Is your business interested in purchasing additional items from the food hub if the food hub expanded its 

operations in some way (e.g., carried additional items/quantities, offered different delivery options, etc.)? 

(yes/no/unsure)

• If yes: 

o Quantify the dollar amount of additional purchases from the food hub on a weekly basis.

o Based on additional purchases from the food hub, would you purchase fewer items from other local 

businesses? (yes/no/unsure)

o What percentage of the additional purchases from the food hub would displace purchases from other local 

sources?

Based on the responses from customers, you can calculate the percentage of customers indicating that they are 

interested in purchasing additional items from the food hub, and the average dollar amount of these additional 

purchases on an annual basis. The total dollar amount of additional final demand for food hub goods and services

follows by multiplying these two numbers together. However, this additional final demand does not come without 

a cost if some positive percentage of customers indicated that the additional food hub purchases result in fewer 

purchases from other firms. Accordingly, you can estimate the average decrease in purchases from other firms as a 

portion of the additional demand for food hub goods and services. Accordingly, the opportunity cost associated 

with $1 of additional demand for food hub products can be computed by multiplying the percentage of customers 

and the average decrease in offsetting purchases.
12

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 your IMPLAN model. 

4.1 Creating the Model and Aggregation Scheme

To begin, select the data file or files within IMPLAN based on the region defined by the study (i.e., county, multicounty, or state, and year). Once you have selected your data file(s), go to File > User Preferences > Analysis 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. At this time, you should also click Advanced Modeling

in the User Preferences window.

Next, create an aggregation scheme. It is preferable to leave the model as disaggregated as possible, to minimize 

aggregation bias.6 That said, for some key sectors it may be difficult to avoid some level of aggregation. As one can 

see from above, translating financial statement categoriesto appropriate IMPLAN industry sector(s) can be complex 

and requires informed judgments by the modeler. For our example, we create two aggregated sectors to reflect the 

primary food expenditure categories for food hubs; i.e., from farms and from food processors. Specifically, we 

aggregate sectors 1-4, 6, and 10-14 and name it the Farm sector. For processed food products, we aggregate sectors 

67, 71-73, and 76-105 and name it the Manufactured Food sector.7 Aggregating your model should be defined by

your research objectives and data availability, noting that model results can always be aggregated after estimating 

impacts, if desired. Keep in mind that you will need one industry that does not have any output in your aggregated 

model; i.e., look under Explore > Study Area Data for industries with zero output (we use Tobacco farming). This 

industry will be reassigned as your Food Hub Farm sector. This does not imply that there is no demand for that 

industry’s commodities within the study area, but only that there is no industry in the study area that produces 

them. After aggregating, reconstruct your model (i.e., Options > Construct > Multipliers).

4.2 Creating a Food Hub Farm sector

To create the Food Hub Farm sector, begin by saving a copy of your model following aggregation with a new name. 

For example, our original aggregated model is PGUIDE NYS 2014 Aggregated, and we saved a copy of it as PGUIDE 

NYS 2014 Aggregated Food Hub Farm. Now open the model you just saved with the new name. 

4.2.1 Customize Study Area Data

The Food Hub Farm sector is extracted from the Farm sector based on the Food Hub Farm financial information; 

i.e., Tables 4-6. The process will involve opening and editing the model in IMPLAN and in Microsoft ACCESS through 

an iterative process. You will use the Customize menu items in IMPLAN; i.e., Study Area Data, Industry Production, 

Commodity Production, and Trade Flows. The order of customization is extremely important! For example, if you 

need to go back and edit Industry Production during the process, customizing Commodity Production and Trade 

Flows must be completed again. 

You begin by adding the Food Hub Farm sector’s employment, output, and value added (Part 1, steps 1.1-1.8) and 

then reducing the corresponding components from the Farm sector by those same amounts (Part 1, steps 1.9-1.15). 

The Farm sector will now represent the Other Farm sector as the Food Hub Farm components have been removed. 

In this way, the size of the total economy remains unchanged. The detailed calculations for our example are included 

in the companion document to this guide (PGUIDE companion.xlsx, Study Area Data worksheet). Detailed steps 

follow in Part 1 and are illustrated for our example in Figures 1 and 2.

 

6 Aggregation bias occurs from a loss of detail when aggregating sectors before generating the multipliers. In so doing, the 

production function of the new aggregated industry is the weighted average of the individual industry’s production functions, 

such that the largest industries have the largest influence on the aggregated industry. That said, the nature of your data and/or 

research questions might make aggregation preferred and more manageable when modifications to the model are necessary.

7 Individual industries excluded for the Farm and Manufactured Food sectors, include those for which there is very little or no 

activity in the study area (New York State), or for which the food hub is not expected to procure products from.
13

Part 1: Customize Study Area Data

1.1 Select Customize > Study Area Data.

1.2 Highlight Tobacco farming from the Industry List and click Rename Industry, enter Food Hub Farm.

1.3 Make sure that Lock (along Intermediate Expenditures row) is unchecked.

1.4 Enter total Employment; i.e., employment listed in Table 4 times the number of Food Hub Farms.

1.5 Under Edit Options, select Edit totals then update per worker values.

1.6 Enter total Output and each Value Added component (i.e., Employee Compensation, Proprietor Income, Other 

Property Type Income, and Tax on Production and Imports); i.e., scale numbers up from Tables 4 and 5 by the 

number of Food Hub Farms (in our case, 50)

1.7 Click Update and double-check that your numbers have not changed (it sometimes happens). The number 

generated in Intermediate Expenditures should equal the total intermediate input purchases from Tables 4 

and 5 times the number of Food Hub Farms, perhaps with a small amount of rounding. If any numbers change 

from those entered, re-enter the correct numbers and click Update until it is correct.

1.8 Click Save (see Figure 1 for illustration).

1.9 While still on this screen, select Farm from the Industry List and click Rename Industry, enter Other Farm.

1.10 Now reduce the total Employment by the same amount you included in the Food Hub Farm industry.

1.11 Under Edit Options, select Edit totals then update per worker values.

1.12 Reduce Output and each Value Added component by the amount included in the Food Hub Farm industry.

1.13 Click Update, double-check that your numbers have not changed (it sometimes happens), perhaps with a 

small amount of rounding. Re-enter your numbers as necessary and click Update until it’s correct.

1.14 Click Save. Click Close. (see Figure 2 for illustration).

1.15 Reconstruct model (Options > Construct > Multipliers). Confirm changes in Explore > Study Area Data.
14

Figure 1. Creating the Food Hub Farm Sector.
15

Figure 2. Creating the Other Farm Sector.
16

4.2.2 Customize Industry Production

Total output and value added outlays were defined in Part 1 and, by computation, total intermediate input 

purchases. Defining the production function for Food Hub Farm involves the individual input expenditure shares 

from Tables 4 and 5, called Gross Absorption Coefficients (GACs) in IMPLAN. GACs do not consider where the 

purchases originated, that is taken care of when customizing trade flows later. Since this step technically involves 

removing intermediate input purchases from the original Farm sector and allocating them to the Food Hub Farm

sector, the GACs for the Other Farm sector will likely change (at this stage they are the same, relative to each other, 

as the original Farm sector). However, if the Food Hub Farm sector is a small proportion of the Farm sector, this is 

of little consequence to impacts and can be reasonably ignored. In our example, the Food Hub Farm sector is less 

than 0.1% of the original Farm sector (by output), so we ignore this technical detail. However, if the Other Farm

sector purchases commodities from the Food Hub Farm sector (in our example it does not), the GACs for Other 

Farm and Food Hub Farm commodities will need to be modified in the Other Farm sector.

Even though Tobacco farming did not have a production function in our model originally (since there was no 

output), now that we have, in essence, added output to Tobacco farming via Part 1, IMPLAN automatically imports 

the default production for Tobacco farming.8 This is NOT the production function we want! Accordingly, using 

Tables 4 and 5, individual GACs are entered that sum up to the Total Absorption Value (TAV) for Food Hub Farm

(0.420, Table 4). The detailed steps follow in the first section of Part 2 (2.1-2.10). 

Based on the sales of Food Hub Farms (Table 6), the GACs for every local industry that purchases Food Hub Farm

commodities must also be edited to reallocate the original GAC for Farm commodities into separate GACs for Other 

Farm and Food Hub Farm commodities. The detailed steps follow in the second section of Part 2 (2.11-2.14), and 

refer to the edited GACs derived in Table 7. 

Finally, you need to reallocate the GACs for every sector that purchase commodities produced by nonlocal firms in 

the same industry you are using to create the Food Hub Farm industry. In our example, sectors 111 (Tobacco 

product manufacturing), 456 (Scientific research and development services), and 12001 (State/Local Govt 

NonEducation) purchase Tobacco (3007). Since nearly all of these purchases are nonlocal (i.e., our example shows 

a small amount of Institutional Commodity Sales that are part of Total Commodity Supply), they will not affect local 

impacts. It is useful, however, to reallocate purchases so that the Gross Commodity Demand for Food Hub Farm

commodities (3007) excludes the purchases of the original commodity. Instiutional purchases are adjusted later 

(within ACCESS). Keep the TAV and VAC at their original values for these industries and reallocate the GAC from the 

original (Tobacco) commodity to another purchased input. For Tobacco product manufacturing (111), we remove 

the GAC for Tobacco (3007) and add it to Tobacco products(3111). For Scientific research & development services

(456), we remove the GAC for Tobacco (3007) and add it to Other Farm (3001). The detailed steps follow in the 

third section of Part 2 (2.14-2.18). Following all production function (GAC) adjustments, reconstruct your model.

Table 7. Editing Gross Absorption Coefficients (GACs) for Industries Purchasing Food Hub Farm Commodity.

A B C

A x B

D

Table 6x50

E

C - D

F

D/A

G

E/A

Industry Code

Industry

Output

($ Mill)

GAC

Farm

Farm

Purchase

($ Mill)

Food Hub 

Farm

Purchase

($ Mill)

Other 

Farm

Purchase

($ Mill)

GAC 

Food 

Hub 

Farm

GAC 

Other 

Farm

Food Hub Farms FHF $ 5 $ 0.0 $ 0.500 $ 0.0 0.100000

Retail trade - Food 400 $ 14,294 0.002173 $ 31.1 $ 0.445 $ 30.6 0.000031 0.002142

Wholesale trade 395 $101,178 0.000021 $ 2.1 $ 0.080 $ 2.0 0.000001 0.000020

Manufactured food Mfood $ 25,990 0.311313 $ 8,091.1 $ 0.513 $ 8,090.6 0.000020 0.311293

Full serv restaurants 501 $ 17,725 0.001312 $ 23.2 $ 0.523 $ 22.7 0.000029 0.001283

El/2nd schools 472 $ 7,268 0.000019 $ 0.14 $ 0.034 $ 0.10 0.000005 0.000014

Sources: IMPLAN default data, primary data collected. Number of Food Hub Farms assumed to be 50.

 

8 GACs for any production sector are the same across any region for a given model year since they are based on national data.
17

Part 2: Customize Industry Production

2.1 Select Customize > Industry Production.

2.2 Highlight Other Farm in the industry list and select Options > Library > Export. Leave the name for the production function 

as suggested (1 Other Farm) and click OK. Since many commodities Food Hub Farms purchase are likely similar to Other 

Farms (albeit in different proportions), it is useful to start with the Other Farm production function when composing the 

one for Food Hub Farm. Since production functions in IMPLAN are national, there will be many, very small GACs. 

2.3 Highlight Food Hub Farm in the industry list. The TAV and VAC follow from Part 1, but the GACs are not (in our case, they 

reflect Tobacco). Click Options > Library > Import, select the production function you exported in 2.2 and click Import. 

When asked to confirm the replacement, click Yes. The relative values of the GACs are identical to that for Other Farm, 

balanced such that their sum is equal to the TAV for Food Hub Farm. Note, while we can change the names of the 

industries (Part 1), we cannot do the same for the names of the commodities they produce 

2.4 For the Food Hub Farm industry, add Tobacco (3007), really the Food Hub Farm, and enter the GAC (expense share, Table 

4, within-sector purchases). If this commodity was not purchased by the Farm sector originally (in our case, farms did not 

buy tobacco), you must add the commodity. Click on Options > Add a Commodity and select 3007 Tobacco farming. Enter 

the GAC and click Ok. Make sure the Fixed box is checked. 

2.5 Edit the GACs for the remaining commodities Food Hub Farm purchases (Tables 4 and 5), except for one. If the 

commodities are already listed, you can edit them directly, otherwise add them as above. For purchases from retailers or 

wholesalers enter the GAC for each margin component separately (Table 5). Combine expense shares for commodities 

that show up more than once; e.g., truck transportation in our example. You can list each commodity only once.

2.6 For any commodities that Food Hub Farm does not purchase, enter a zero and make sure it is set as Fixed. We have found 

that IMPLAN may crash when you remove them at this point, so we suggest entering zeros for now.

2.7 Go to the final non-zero commodity yet to edit (i.e., pick one from Table 4 or 5) and make sure Fixed is NOT checked. Click 

Balance. The GAC will be computed and should equal what you have in your calculations, with possibly some rounding 

error. Click Save. This is the production function for Food Hub Farm, albeit with many zero GACs.

2.8 Highlight all commodity rows with zero GACs and select Options > Remove a commodity. This is your production function 

for Food Hub Farm, now cleaned of all zero GACs. Click Save. See Figure 3 for illustration.

2.9 Do NOT reconstruct yet. We want to change other industry production functions first.

2.10 Multiply total output for each industry Food Hub Farm sells to (column A, Table 7) by that industry’s GAC for Farm

commodities (column B, Table 7) to compute total purchases of Farm commodities (column C, Table 7). This is useful to 

do in a spreadsheet, see companion workbook for our example.

2.11 Subtract Food Hub Farm purchases for each industry (column D, Table 7) from total Farm purchases (column C, Table 7) 

to compute the (net) purchases of Other Farm commodities (column E, Table 7).

2.12 Compute the Food Hub Farm and the Other Farm commodity’s GACs for each industry; i.e., divide purchases by total 

output (columns F and G, Table 7). The Food Hub Farm GACs in our example are all very small, except for the withinsector purchases, but this make sense give the relatively small size of our Food Hub Farm sector and the relatively large 

sizes of the corresponding industries purchasing from it.

2.13 For each industry in Table 7, go to Customize > Industry Production> Edit Industry Production, edit the Farm commodity 

(really Other Farm) and Food Hub Farm commodity (in our case Tobacco farming) GACs to reflect the values from 2.12

(Table 7). To minimize rounding error, enter in nine or ten places to the right of the decimal point (even though only six 

places will show on the screen). Make sure Fixed is checked for each and click Balance. Then click Save. The edited 

production function for Retail trade- Food (400) from our example is shown in Figure 4.

2.14 Reallocate the original zero-production sector commodity GAC (in our case, Tobacco) to another commodity for every 

industry that purchases it (i.e., for our case that actually purchases tobacco). 

2.15 Select the industry from the list (Edit Industry Production). For Tobacco product manufacturing (111) in our example, the 

GACs are 0.028354 and 0.077018 for Tobacco (3007) and Tobacco products (3111), respectively. Enter 0 for 3007 and 

0.105372 for 3111. Make sure Fixed is checked for each. Click Balance. Click Save. Highlight the commodity row for 

Tobacco (3007), click Options > Remove a commodity. Click Balance. Click Save.

2.16 For Scientific research and development services (456) in our example, the GACs are 0.000002 and 0.000872 for 

Tobacco (3007) and Farm (3001), respectively. Enter 0 for 3007 and 0.000874 for 3001. Make sure Fixed is checked for 

each. Click Balance. Click Save. Highlight the commodity row for Tobacco (3007), click Options > Remove a commodity. 

Click Balance. Click Save. Repeat for each relevant industry and then click Close.

2.17 Reconstruct model (Options > Construct > Multipliers).
18

Figure 3. Edited Food Hub Farm Production Function.
19

Figure 4. Edited Retail – Food and Beverage Stores Production Function.
20

4.2.3 Customize Commodity Production

Customizing commodities produced by the Food Hub Farm sector is the next step in creating the sector. 

Commodities are produced by industries, some of which produce multiple commodities (generally a primary 

commodity and one or more byproducts). Commodity codes generally follow the same structure as for the industry 

codes, albeit preceded by the number 3. Commodities produced by each industry (based on national data), in 

proportion to the value of total output, are shown under Customize > Commodity Production. The commodities 

produced by the Other Farm sector in our example are illustrated in Figure 5, representing the same commodities 

produced originally by the Farm sector. The Other Farm sector produces mostly (other) farm commodities (3001, 

98.5%), along with small amounts of forestry products (3015, 0.4%), support activities for agriculture and forestry 

(3019, 0.9%), and other amusement and recreation (3496, 0.2%).

For our Food Hub Farm sector, the commodities at this stage will reflect those produced by Tobacco farming, as 

this was the original empty sector that we are populating with the Food Hub Farm information. 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 small 

percentage of the total commodities sold, this will not have any material effect on the impact results, and is useful 

in editing trade flows that follow next. In our case, the primary commodity will be 3007 (Tobacco), even though we 

know it by now as the Food Hub Farm commodity. The detailed steps follow in Part 3 below (3.1-3.4), with the 

result illustrated in Figure 6. There are no other industries that need their commodities modified. Once you 

complete this part, reconstruct your model. 

Part 3: Customize Commodity Production

3.1 Under Customize > Commodity Production, select Food Hub Farm from the industry list.

3.2 Highlight the Tobacco farming commodity (really the Food Hub Farm commodity) coefficient and enter 1. Make 

sure the Fixed box is checked and click Balance. The other commodity coefficients will go to zero. Click Save.

3.3 Highlight all commodity rows with zero commodity coefficients and select Options > Remove a commodity. Now 

only the Food Hub Farm commodity (Tobacco farming) is listed. Click Balance. Click Save. Click Close. See Figure 

6 for an illustration.

3.4 Reconstruct model (Options > Construct > Multipliers).
21

Figure 5. Commodities Produced by the Other Farm sector.
22

Figure 6. Commodities Produced by the Food Hub Farm sector (Tobacco farming = Food Hub Farm).
23

4.2.4 Customize Trade Flows

The next step within IMPLAN is to customize trade flows to reflect the percentage of commodities (intermediate 

inputs) purchased by the Food Hub Farm from within the local study area. Specifically, you will edit the regional 

purchase coefficients (RPCs) for each commodity that the Food Hub Farm sector purchases based on the LPPs in 

Tables 4 and 5. By their very definition, RPCs are the most important parameters for assessing impact, but are often 

the most difficult parameters to collect. Absent collecting these in your data efforts, you are restricted to using 

IMPLAN’s estimates or using your own judgement. It may be useful to evaluate the sensitivity of your results to 

changes in various RPCs. RPCs associated with the largest purchases will have the most impact on your results.

By default, RPCs are the same for all sectors (industries and institutions) that purchase that commodity. For our 

example, all sectors purchasing Farm (really Other Farm) commodities procure 35.4% of them locally (go to 

Customize > Trade Flows > Industry/Institution RPC, illustrated in Figure 7). In our case, Other Farm commodities 

are purchased by Other Farms (1), food manufacturers (67), households (10001-10009), governments (12001), and 

more. Similarly, the demands for the Food Hub Farm commodity (shown as Tobacco farming), prior to customizing

trade flows, are shown in Figure 8. Here, the average RPC is 7.4%, and is a combination of the original RPC for 

tobacco (near zero) and some adjustments made earlier with commodities produced and demanded. In short, it is 

NOT correct and needs adjustment. Each industry’s demand for the Food Hub Farm commodity are shown under 

Industry Intermediate Demand for Commodities and reflect the level of purchases from Tables 6 and 7. Since these 

purchases represent the actual sales by the Food Hub Farm industry in the study area, RPCs should equal one. The 

detailed steps for editing these trade flows follow in the first section of Part 4 (4.1-4.2), with the result illustrated 

in Figure 9.

Household purchases (or any other institutional purchases) need to be edited within ACCESS since institutional 

demands cannot be changed in IMPLAN. We will do this later, but notice in our example, that there are no purchases 

from households (Figure 8). This is because households did not purchase any tobacco in our original model; 

however, households do purchase the Food Hub Farm commodity. The institutional purchases that do show up 

here are actually purchases of tobacco by state/local Govt NonEducation (12001) and must be removed in ACCESS.

While in Customize > Trade Flows > Industry/Institution RPC, change the RPCs for each commodity that the Food 

Hub Farm industry purchases (i.e., based on the LPPs in Tables 4 and 5). For commodities listed more than once in 

Tables 4 and 5 due to margining, compute a weighted average of the LPPs based on expenditures and enter those 

RPCs. Figure 10 provides an illustration of the Retail Gasoline commodity (3402). After completing this step, close 

the model in IMPLAN. Detailed steps follow in the second section of Part 4 (4.3-4.5).

The next set of changes for the model are made within Microsoft ACCESS. As discussed, IMPLAN does not allow you 

to change commodity demands by institutions within the software. With the model open in ACCESS, open the 

Regional Commodity Balances table and copy the data for the Food Hub Farm (3007) and Other Farm (3001) 

commodities to EXCEL. Do the same for the original model aggregated in IMPLAN but without model customization. 

Comparing across these models allows one to comprehensively evaluate the model values before and after creation 

of the Food Hub Farm industry. All changes in ACCESS are only made to your aggregated food hub farm model. The 

edits in ACCESS update variables that pertain to the institutional commodity sales and demands for Food Hub Farm. 

However, entering these changes necessarily affects levels of other variables for Food Hub Farm that depend on 

these values. Furthermore, adding household demand (or any institutional demand) for Food Hub Farm requires 

subtracting those same amounts from Other Farm since those values still reflect the demands for Farm

commodities. It also means removing the institutional demands that remain for the tobacco commodity under Food 

Hub Farm. After editing, copy the data back into ACCESS. Table 8 provides an illustration for our example and the 

companion workbook includes detailed computations and comments. Detailed steps follow in the third section of 

Part 4 (4.6-4.9).9

 

9 The changes to the Other Farm commodity are relatively minor, but technically correct, since our Food Hub Farm sector is 

relatively small. That said, ignoring them would not likely change the impact results all that much. The point here is to document 

the correct process, as there may be instances for researchers where changes to sectors may be more consequential; e.g., 

splitting the fruit sector in New York into still sizable apple fruit and non-apple fruit sectors.
24

Part 4: Customize Trade Flows

4.1 Under Customize > Trade Flows > Edit Trade Flows > Industry/Institution RPC, select 3007 Tobacco Farming (really the 

Food Hub Farm commodity) from the dropdown list.

4.2 Under Industry Intermediate Demand for Commodities, set the RPC for all industry sectors to one. Click Save. The Gross 

Commodity Demands reflect only local purchases from the Food Hub Farm industry (Table 6, Final Sales FH Allocated 

column). See Figure 9 for illustration. Do not reconstruct yet.

4.3 Select each commodity from the dropdown list that the Food Hub Farm sector purchases and adjust the RPC based on 

the LPPs in Tables 4 and 5. For commodities listed more than once (due to margining), compute a weighted average LPP, 

based on expenditures, and enter that number as the RPC. After editing each commodity RPC, click Save. See Figure 10

for an illustration of the Retail Gasoline commodity (3402).

4.4 Reconstruct model (Options > Construct > Multipliers). 

4.5 Close the model in IMPLAN. Close IMPLAN.

4.6 Open the aggregated food hub farm model in Microsoft ACCESS (PGUIDE NYS 2014 Aggregated Food Hub Farm.impdb). 

Open the Regional Commodity Balances table and copy the data for the Other Farm (3001) and Food Hub Farm (3007) 

commodities into an EXCEL spreadsheet. 

4.7 Open the original aggregated model without model customization in Microsoft ACCESS; i.e., the IMPLAN before creating 

the Food Hub Farm industry (PGUIDE NYS 2014 Aggregated.impdb). Open the Regional Commodity Balances table and 

copy the data for the Other Farm (3001) and Tobacco (3007) commodities into the same EXCEL spreadsheet as in 4.6.

4.8 In EXCEL, compute the updated variables values for the aggregated food hub farm model, beginning with institutional 

commodity sales (zero in our example) and demands (household sales in our example, Table 6, Final Sales FH Allocated) 

for Food Hub Farm. Updating these values will affect the levels of other variables for Food Hub Farm that depend on 

them (note formulas in the companion workbook). Adding household demand (or any institutional demand) for Food 

Hub Farm also requires subtracting those same amounts from Other Farm since those values still reflect the demands 

for the Farm commodity. Finally, remove any institutional demands that remain for tobacco that are still included in 

Food Hub Farm. Once complete, save the changes in EXCEL.

4.9 Copy all updated variable values from EXCEL back into the Regional Commodity Balancestable in ACCESS and click Save. 

Detailed computations for our example are contained in the companion workbook to this guide (PGUIDE companion.xlsx, 

Commodity Balances worksheet) and are summarized in Table 8.

4.10 While still in ACCESS, open the Regional Institution Demand table and copy the data for the Other Farm (3001) and Food 

Hub Farm (3007) commodities into an EXCEL spreadsheet. 

4.11 In EXCEL, compute the updated values for the relevant demands, RPCs, and Foreign Import Proportions for both 

commodities. Demand additions in Food Hub Farm (in our example, household demand) must be subtracted from the 

respective demands in Other Farm. We allocate total household demand (Table 6) evenly across all nine household 

categories. Also, remove the institutional demands for tobacco that are still reflected in Food Hub Farm. RPCs and 

Foreign import Proportions for both commodities were already computed in the revised Regional Commodity Balances

table. Enter them again here. Once complete, save the changes in EXCEL.

4.12 Copy all of the updated variable values from EXCEL back into the Regional Institution Demand table in ACCESS and click 

Save. Detailed computations for our example are contained in the companion workbook to this guide (PGUIDE 

companion.xlsx, Institution Demand worksheet) and are summarized in Table 9.

4.13 Close the model in ACCESS. Close ACCESS.

4.14 Reopen the model in IMPLAN (PGUIDE NYS 2014 Aggregated Food Hub Farm.impdb).

4.15 Under Customize > Trade Flows > Edit Trade Flows > Industry/Institution RPC, select 3007 Tobacco Farming (really the 

Food Hub Farm commodity) from the dropdown list. The industry demands and the RPCs for them still hold from the 

previous edits. The institutional demands added and RPCs edited in ACCESS (for households in our case) and any 

deletions (the state government purchases of tobacco in our example) are now reflected correctly. The summary values 

at the bottom of the screen are correct and will match your calculations made earlier (Regional Commodity Balances). 

Click Save. No additional changes are necessary. Figure 11 provides an illustration.

4.16 Under Customize > Trade Flows > Edit Trade Flows > Industry/Institution RPC, select 3001 Farm (really the Other Farm

commodity) from the dropdown list. The demands (industry and institutional) are already updated based on the 

corrections made in ACCESS, as well as the updated RPCs for institutions. The RPCs for industry demands, however, still 

need updating. The level of the RPCs should be the same as for the institutional demands, and follow from the average 

RPC you computed for Commodity Balances. Once you have updated the RPCs for the industry demands, all the summary 

numbers on the bottom of the screen should match your calculations made in EXCEL and copied into ACCESS (Regional 

Commodity Balances). Click Save. Figure 12 provides an illustration.

4.17 Reconstruct model (Options > Construct > Multipliers).
25

Figure 7. RPCs and Demands for Other Farm Commodity.
26

Figure 8. RPCs and Demands for Food Hub Farm Commodity, prior to Customizing Trade Flows.
27

Figure 9. Corrected RPCs by Industries for the Food Hub Farm Commodity.
28

Figure 10. Corrected RPCs by Food Hub Farm Industry for Retail Gasoline.
29

Table 8. Computations for Other Farm and Food Hub Farm Regional Commodity Balances.

Exported from ACCESS Edited & Saved in 

ACCESS

Initial Aggregated 

Model

Aggregated Food Hub 

Farm Model

Aggregated Food Hub 

Farm Model

Label IMPLAN Variable Name Farm Tobacco Other 

Farm

Food Hub

Farm

Other

Farm

Food Hub

Farm

Commodity Code  3001 3007 3001 3007 3001 3007

A IndustryCommodityProduction 6560.1433 0.0000 6555.2210 5.0000 6555.2210 5.0000

B InstitutionalCommoditySales 259.9622 2.7038 259.9622 2.7038 259.9622 0.0000

C=A+B TotalCommoditySupply 6820.1055 2.7038 6815.1832 7.7038 6815.1832 5.0000

D ForeignExports 447.8479 1.2446 447.8479 1.2446 447.8479 0.0000

E ForeignTransshipments 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

F=C-D-E NetCommoditySupply 6372.2576 1.4592 6367.3353 6.4592 6367.3353 5.0000

G IntermediateCommodityDemand 9851.3814 69.5508 9849.3839 2.0972 9849.3839 2.0972

H InstitutionalCommodityDemand 4023.1766 0.2695 4023.1766 0.2695 4020.9719 2.2047

I=G+H TotalGrossCommodityDemand 13874.5580 69.8202 13872.5605 2.3667 13870.3558 4.3019

J ForeignImportProportion 0.1273 0.5198 0.1273 0.5198 0.1273 0.0000

K=1-J ForeignRPC 0.8727 0.4802 0.8727 0.4802 0.8727 1.0000

L=GxJ ForeignIntermediateImports 1253.7806 36.1506 1253.5264 1.0901 1253.5264 0.0000

M=HxJ ForeignInstitutionImports 512.0278 0.1401 512.0278 0.1401 511.7472 0.0000

N=L+M ForeignImports 1765.8084 36.2907 1765.5541 1.2301 1765.2735 0.0000

O=G-L DomesticIntermediateDemand 8597.6008 33.4001 8595.8575 1.0071 8595.8575 2.0972

P=H-M DomesticInstitutionDemand 3511.1488 0.1294 3511.1488 0.1294 3509.2248 2.2047

Q=O+P TotalDomesticCommodityDemand 12108.7497 33.5295 12107.0063 1.1366 12105.0823 4.3019

R LocalSupplyToDomesticDemands 4915.1280 0.1760 4915.1280 0.1760 4910.8262 4.3019

S=R/Q DomesticRPC 0.4059 0.0052 0.4060 0.1548 0.4057 1.0000

T=SxO LocalSupplyToDomesticIntermediate 3489.8986 0.1753 3489.6934 0.1559 3487.1933 2.0972

U=SxP LocalSupplyToDomesticInstitutional 1425.2294 0.0007 1425.4346 0.0200 1423.6329 2.2047

V=T+U TotalLocalSupply 4915.1280 0.1760 4915.1280 0.1760 4910.8262 4.3019

W=O-T DomesticIntermediateImports 5107.7022 33.2248 5106.1641 0.8512 5108.6642 0.0000

X=P-U DomesticInstitutionImports 2085.9194 0.1287 2085.7142 0.1094 2085.5919 0.0000

Y=W+X TotalDomesticImports 7193.6216 33.3536 7191.8783 0.9606 7194.2561 0.0000

Z=W+L IntermediateImports 6361.4828 69.3755 6359.6905 1.9413 6362.1905 0.0000

AA=X+M InstitutionalImports 2597.9472 0.2688 2597.7420 0.2495 2597.3391 0.0000

AB=Z+AA TotalImports 8959.4300 69.6443 8957.4324 2.1907 8959.5296 0.0000

AC=R/I AverageRPC 0.3543 0.0025 0.3543 0.0744 0.3541 1.0000

AD=C TotalCommodityOutput 6820.1055 2.7038 6815.1832 7.7038 6815.1832 5.0000

AE=R/C AverageRSC 0.7207 0.0651 0.7212 0.0228 0.7206 0.8604

AF=F-R DomesticExports 1457.1296 1.2832 1452.2072 6.2832 1456.5091 0.6981

AG=F/I DomesticSupply/DemandRatio 0.4593 0.0209 0.4590 1.0000 0.4591 1.0000

AH=R TotalDomesticCommodityOutput 4915.1280 0.1760 4915.1280 0.1760 4910.8262 4.3019

AI=I/F Demand/SupplyRatio 1.0000 1.0000 1.0000 0.3664 1.0000 0.8604

AJ=D/(D+AF) ForeignExportProportion 0.2351 0.4924 0.2357 0.1653 0.2352 0.0000

AK=N/AB ForeignImportToTotalImport 0.1971 0.5211 0.1971 0.5615 0.1970 0.0000

Notes: All dollar variables reported in millions of dollars. Cells shaded in yellow denote model changes made and saved in ACCESS

prior to reopening the model in IMPLAN. Detailed notes on calculations are included in the EXCEL workbook as a companion to 

this written guide.
30

Updating the individual institutional sector demands follows next. To do so, copy the data for Other Farm (3001)

and Food Hub Farm (3007) commodities from the Regional Institution Demand table in ACCESS to EXCEL. Edit the 

relevant demands, RPCs, and Foreign Import Proportions for both commodities, noting that demand additions in 

Food Hub Farm must be subtracted from the respective demands in Other Farm. Lacking additional detail for 

household demand for the Food Hub Farm commodity, we allocate total household demand (Table 6) evenly across 

all nine household categories. We also remove the institutional demands for tobacco still reflected in Food Hub 

Farm (similar to above). The RPCs and Foreign import Proportions for Food Hub Farm are one and zero, respectively, 

and follow from the calculations made for the Regional Commodity Balances table done previously. After editing, 

copy the updated values back into ACCESS. Table 9 provides an illustration for our example and the companion 

workbook includes detailed computations and comments. Detailed steps follow in the fourth section of Part 4 (4.10-

4.13). At this point, save the model in ACCESS and close ACCESS.

The final set of updates to get the trade flows correct are done back in IMPLAN. At this point, the industry demands 

for the Food Hub Farm commodity (3007), as well as the RPCS for them still hold from the previous edits completed 

in IMPLAN. In addition, the institutional demands (households) and RPCs reflect the changes made in ACCESS. 

Accordingly, the summary values of supplies, demands, imports, and exports are correct and follow from the 

previous updates. No additional changes are necessary for the Food Hub Farm commodity (see Figure 11). However, 

one final set of changes are required for the Other Farm commodity (3001). While the demands (industry and 

institutional) are now updated based on the corrections in ACCESS, only the updated RPCs for institutions are 

reflected. The RPCs for the industry demands should match the RPCs for the institutional demands and reflect the 

average RPC you calculated earlier; i.e., when updating Regional Commodity Balances. These are updated within 

IMPLAN (see Figure 12). Detailed steps follow in the last section of Part 4 (4.14-4.17). 

At this point, the development of your model is complete and you are ready to estimate impacts! 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 for the Food Hub Farm and Other Hub Farm industries. In our example, the output, 

employment, and labor income multipliers are 1.775 versus 1.516, 1.271 versus 1.412, and 1.640 versus 1.611, 

respectively.
31

Table 9. Other Farm and Food Hub Farm Regional Institutional Demand. 

Exported from ACCESS Edited and saved in ACCESS

Type 

Code

Commodity

Code

Gross

Institution

Demand RPC

Foreign

Import

Proportion

Type

Code

Commodity

Code

Gross 

Institution 

Demand RPC

Foreign 

Import 

Proportion

10003 3001 207.1683 0.3543 0.1971 10003 3001 206.9233 0.3541 0.1273

10001 3001 127.2605 0.3543 0.1971 10001 3001 127.0155 0.3541 0.1273

11003 3001 0.0000 0.3543 0.1971 11003 3001 0.0000 0.3541 0.1273

11002 3001 0.1072 0.3543 0.1971 11002 3001 0.1072 0.3541 0.1273

14002 3001 83.7236 0.3543 0.1971 14002 3001 83.7236 0.3541 0.1273

14001 3001 0.0000 0.3543 0.1971 14001 3001 0.0000 0.3541 0.1273

12002 3001 21.8814 0.3543 0.1971 12002 3001 21.8814 0.3541 0.1273

10007 3001 509.8656 0.3543 0.1971 10007 3001 509.6206 0.3541 0.1273

10004 3001 215.7681 0.3543 0.1971 10004 3001 215.5232 0.3541 0.1273

10002 3001 83.7189 0.3543 0.1971 10002 3001 83.4740 0.3541 0.1273

12003 3001 0.0000 0.3543 0.1971 12003 3001 0.0000 0.3541 0.1273

10005 3001 324.8025 0.3543 0.1971 10005 3001 324.5575 0.3541 0.1273

10006 3001 573.0961 0.3543 0.1971 10006 3001 572.8511 0.3541 0.1273

11001 3001 0.0000 0.3543 0.1971 11001 3001 0.0000 0.3541 0.1273

12001 3001 204.1395 0.3543 0.1971 12001 3001 204.1395 0.3541 0.1273

10009 3001 928.6417 0.3543 0.1971 10009 3001 928.3968 0.3541 0.1273

10008 3001 743.0031 0.3543 0.1971 10008 3001 742.7582 0.3541 0.1273

10001 3007 0.0000 0.0744 0.5615 10001 3007 0.2450 1.0000 0.0000

10007 3007 0.0000 0.0744 0.5615 10007 3007 0.2450 1.0000 0.0000

12003 3007 0.0000 0.0744 0.5615 12003 3007 0.0000 0.0000 0.0000

14001 3007 0.0000 0.0744 0.5615 14001 3007 0.0000 0.0000 0.0000

10004 3007 0.0000 0.0744 0.5615 10004 3007 0.2450 1.0000 0.0000

11002 3007 0.0000 0.0744 0.5615 11002 3007 0.0000 0.0000 0.0000

11003 3007 0.0000 0.0744 0.5615 11003 3007 0.0000 0.0000 0.0000

10009 3007 0.0000 0.0744 0.5615 10009 3007 0.2450 1.0000 0.0000

11001 3007 0.0000 0.0744 0.5615 11001 3007 0.0000 0.0000 0.0000

10008 3007 0.0000 0.0744 0.5615 10008 3007 0.2450 1.0000 0.0000

10003 3007 0.0000 0.0744 0.5615 10003 3007 0.2450 1.0000 0.0000

12001 3007 0.2695 0.0744 0.5615 12001 3007 0.0000 0.0000 0.0000

12002 3007 0.0000 0.0744 0.5615 12002 3007 0.0000 0.0000 0.0000

10005 3007 0.0000 0.0744 0.5615 10005 3007 0.2450 1.0000 0.0000

10002 3007 0.0000 0.0744 0.5615 10002 3007 0.2450 1.0000 0.0000

14002 3007 0.0000 0.0744 0.5615 14002 3007 0.0000 0.0000 0.0000

10006 3007 0.0000 0.0744 0.5615 10006 3007 0.2450 1.0000 0.0000

Notes: Demands reported in millions of dollars. Cells shaded in yellow denote model changes made and saved in 

ACCESS prior to reopening the model in IMPLAN. Detailed notes on calculations are included in the EXCEL workbook 

as a companion to this written guide.
32

Figure 11. Final Demands, Supplies, and RPCs for the Food Hub Farm Commodity.
33

Figure 12. Final Demands, Supplies, and RPCs for the Other Farm Commodity.
34

5. 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 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) within IMPLAN. ABP

allows you to create a customized industry based on IMPLAN's pre-created spending patterns, based on your own 

budgetary spending pattern, or based on a combination of both. In our case, we utilize ABP to estimate impacts 

based on the spending pattern data collected from the food hub; i.e., from Tables 1 and 2.10

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 the impact estimates. 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 non-local purchases represent leakage from the local economy. In 

general, only a portion of value added outlays are regarded as generating impact. While spending on EC and PI

generate induced impacts in the local economy, outlays to OPTI and TOPI are generally excluded. Similarly, 

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 outlays. 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 (e.g., products from farms 

and food processors, insurance and utilities costs), $25 to EC, $10 to PI, and $5 on TOPI. The $100 represents the 

direct output effect. If one-half of the inputs ($30) are procured locally, the other $30 represent leakage. The $25 

and $10 value added components and included in assessing impact, but the $5 in tax payments are not 

5.1 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, let us 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.11 With ABP, this positive shock is allocated to the levels of intermediate 

input purchases and value added outlays necessary to support it, and follow from the expense shares and LPPs 

included in Tables 1 and 2. The direct effect in terms of output is the $250,000, while the corresponding direct 

effects for employment, labor income, and total value added (TVA) can be computed based on each of their 

requirements per dollar of output (sales). For our example, based on the food hub’s current production pattern 

(Table 1), 0.004 employees are required per $1,000 in output, while $0.375 of labor income and $0.440 of total 

value added are required per dollar of output. By simple computation then, the direct effects for employment, labor 

income, and TVA are 1.0, $93,750, and $110,000, respectively. 

Note that relatively large changes in final demand for food hub products are likely to change its production function 

profile; e.g., the food hub may increase automated for human labor operations, or change the levels and locations 

of where they purchase intermediate inputs. If this information is known, the new GACs, RPCs, and value added 

outlays should be used in estimating impact. In our case, we use the current spending patterns of the food hub in 

estimating impacts, even though our increase in final demand is relatively large.

5.2 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 their increase in 

purchases of food hub products. From the customer survey results, you should have generated an estimate of how 

much of the increase in demand for food hub purchases involves a shift of demand away from other sectors. For 

 

10 See IMPLAN’s Case Study: Analysis-by-Parts or The Basics of Analysis-by-Parts for more information at support.implan.com.

11 Recall, that total sales by the food hub in our example were $1,000,000 (Table 3).
35

our analysis, we assume that these offsets would occur in the Wholesale Trade (3395).12 Thus, in addition to the 

positive shock for increasing food hub goods and services, a simultaneous negative shock accounts for the offset in 

purchases from other sectors. Since these are wholesale purchases in our example, margins are applied. For ease 

of exposition, assume for our example that, on average, for every additional dollar of food hub products purchased, 

ten cents worth of purchases are decreased in wholesale commodities (i.e., a 10% offset). With an output (sales)

increase of $250,000 of food hub products, this implies a total negative shock of $25,000 that is margined between 

the Wholesale (3395) and produced (Other Farm, Manufactured Food) commodities.13

5.3 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 spending pattern of the food hub for intermediate inputs, value added outlays 

for the labor income components, and opportunity costs. In each of the activities, you define a set of events, and 

then define a scenario that includes your activities. Once the scenario is analyzed the results are displayed in 

IMPLAN. 

Intermediate input purchases are entered using the Industry Spending Pattern type of Activity and where the 

individual events within this activity include the individual commodity levels purchased, along with their LPPs 

(Tables 1 and 2). For commodities listed more than once in Tables 1 and 2 (due to margining), the expense shares 

are combined into one Event and a weighted average LPP assigned. The level of the Activity follows from the 

increase in final demand, $250,000 for our example. Detailed steps follow in the first section of Part 5 (5.1-5.2) and 

with the results from our example illustrated in Figure 13.

Outlays for EC and PI are represented with Labor Income Change activities. The values defined under Events are 

based on the food hub’s current production pattern (Table 1), where the amount of EC and PI per dollar of output 

($0.260 and $0.115, respectively, in our example) are multiplied by the increase in food hub products and services 

($250,000 in our example). LPPs assigned are based on the data collected from the food hub. Detailed steps follow 

in the second and third sections of Part 5 (5.3-5.6) with the results from our example illustrated in Figures 14 and 

15.

Opportunity costs are entered with an Industry Spending Pattern Activity, with the level set as the total opportunity 

cost value (-$25,000 in our case). Events are entered with coefficients that sum to one and reflecting the margins 

applied, if applicable. Since the detailed nature of these reduced purchases are generally unknown (i.e., local or 

non-local), LPPs can be assigned by IMPLAN’s SAM Model Value. Detailed steps follow in the fourth section of Part 

5 (5.7-5.9) with the results from our example illustrated in Figure 16.

The final steps involve Analyzing Scenarios for the Activities previously. A new Scenario is named and the relevant 

Activities selected (Figure 17). After clicking on Analyze Single Region (we do not consider multi-regional impact

models in this guide), the results are computed and a screen prompts you to review the results. Detailed steps 

follow in the last section of Part 5 (5.10-5.14) with the results from our example illustrated in Figure 18.

 

12 Based on Jablonski, Schmit and Kay (2016), business customers reported decreasing purchases from other food distributors, 

which are included in IMPLAN’s wholesale trade sector (395).

13 Supply-side opportunity costs may also be relevant based on the nature of the research objectives and study area conditions. 

For example, if the increase in demand for food hub products is met by a reallocation of acres by farmers to food hub food 

crops (e.g., tomatoes) and away from other crops (e.g., soybeans) the loss in soybean output should also be included.
36

Part 5: Analysis-by-Parts

5.1 First, enter the Industry Spending Pattern for your food hub, along with the level of the direct output effect. To do this, 

select Analyze > Setup Activities > New Activity > Industry Spending Pattern. Name the activity Intermediate Input 

Purchases. Set the Activity Level to the increase in demand for the food hub’s products and services; i.e., the direct 

output effect (in our example, $250,000). Click Save.

5.2 Select a New Event for each intermediate input commodity purchased. For commodities listed more than once in Tables 

1 and 2 (due to margining), combine the expense shares into one event and compute a weighted average LPP, based on 

the relative levels of the expenses. Select each commodity from the Sector column and enter the expense share under

Coefficient. The value in the Local Purchase Percentage column will be 100% by default, change as necessary based on 

the LPPs listed in Tables 1 and 2; i.e., select Event Options > Edit Event Properties > Local Purchase Percentage > User 

LPC. A screen will pop up labeled Custom Local Percent, enter the LPP, and click OK. Note that the food hub purchases

the Food Hub Farm commodity (3007, Tobacco farming, in our example). Once all commodity values are entered, the 

Sum of Event Values at the bottom of the screen will equal the total intermediate input expense shares from Table 1. 

See Figure 13 for an illustration.

5.3 Now define a new activity to represent the labor income change for EC. To do this, select New Activity >Labor Income 

Change. Name the activity Employee Compensation. Set the Activity Level to 1. Click Save.

5.4 Select New Event and select 5001 Employee Compensation. Enter the change in EC associated with the increase in 

demand for food hub products and services. Based on the food hub’s production pattern for our example (Table 1), 

$0.260 of EC are required per dollar of output. Multiplying this value by the increase in demand (sales) yields a Labor 

Income Value of $65,000 ($0.260 x $250,000). After entering this value, edit the LPP, if necessary. For our example, all 

employees are local. See Figure 14 for an illustration.

5.5 Now define a new activity to represent the labor income change for PI. To do this, select New Activity >Labor Income 

Change. Name the activity Proprietor Income. Set the Activity Level to 1. Click Save.

5.6 Select New Event and select 6001 Proprietor Income. Enter the change in PI associated with the increase in demand for 

food hub products and services. Based on the food hub’s production pattern for our example (Table 1), $0.115 of PI are 

required per dollar of output. Multiplying this value by the increase in demand (sales) yields a Labor Income Value of 

$28,500 ($0.115 x $250,000). After entering this value, edit the LPP, if necessary. For our example, all proprietors are 

local. See Figure 15 for an illustration.

5.7 Add activities for any opportunity costs. In our case, we assume a 10% offset in purchases from wholesalers. Since the 

reduced purchases are at the wholesale level, margins are applied. Select New Activity > Industry Spending Pattern, name 

the activity Opportunity Cost Wholesale Trade, and set the Activity Level equal to the opportunity costs (in our case,

minus $25,000). Click Save. 

5.8 Select New Event and select Wholesale Trade (3395). We know from earlier (footnote 6) that the weighted average 

wholesale trade margin for farm and processed food products in our example is 0.1004. Enter this value under 

Coefficient. Since we do not know the nature of these reduced purchases (i.e., local or non-local), select Event Options > 

Edit Event Properties > Local Purchase Percentage > Set to SAM Model Value to use the default value in IMPLAN.

5.9 To account for the margin values of the farm and processed food products associated with the reduced wholesale 

purchases, allocate the remaining opportunity costs to these commodities. In our case, the remaining margin ($22,490) 

is allocated between Other Farm ($8,434, 0.33736) and Manufactured Food ($14,056, 0.56224) based on the relative 

values of these products purchased by the food hub. Since we do not know the nature of these reduced purchases (i.e., 

local or non-local), select Event Options > Edit Event Properties > Local Purchase Percentage > Set to SAM Model Value. 

See Figure 16 for an illustration.

5.10 The final step involves Analyzing Scenarios for the Activities defined above. Following 5.9, click Next. 

5.11 You are now on the Analyze Scenarios screen. If this is the first Scenario you are creating, a screen will open asking for 

The Name of Your New Scenario and to enter the Scenario Level. Choose a name descriptive enough to understand the 

Activities you will include in it. For our example, we entered Food Hub $250K Output with Opp Cost. Set the Scenario 

Level to 1. Click Save.

5.12 The Activities you set up previously show on the left side of the screen under Available Acitivity(s). Select all Activities

that you would like to include in your Scenario. In our example, all four are selected. See Figure 17 for an illustration. If 

you would like to see impact results without opportunity costs, define a separate Scenario the excludes the Activity

associated with it.

5.13 Click Analyze Single Region (we do not consider multi-region models in this guide).

5.14 A Run Analysisscreen will pop up stating Analysis complete. Do you want to view the results? Click Yes. See Figure 18 for 

an illustration of the results screen for our example.
37

Figure 13. Industry Spending Pattern Activity for the Food Hub Increase in Final Demand.
38

Figure 14. Employee Compensation Activity for the Food Hub Increase in Final Demand.
39

Figure 15. Proprietor Income Activity for the Food Hub Increase in Final Demand.
40

Figure 16. Opportunity Cost Activity for the Food Hub Increase in Final Demand.
41

Figure 17. The Analyze Scenario Screen after Selecting Activities.
42

Figure 18. The Scenario Results Screen, Summary Results.
43

5.4 Interpreting Results

Congratulations! You have made it to the fun part - interpreting your results. IMPLAN displays the impact results in 

a number of ways; i.e., a summary report that covers the total economic impacts, detailed results by type of impact 

(i.e., employment, output, labor income, and combined and individual value added components), and a tax impact 

report. Under ABP, IMPLAN does not show any direct effects (see Figure 17 for our example) since you defined 

them through their allocations of outlays (i.e., industry spending and labor income components). In other words, 

by using ABPs, you have essentially provided the first round of indirect effects through the industry spending 

patterns and of induced effects through the labor income changes. 

The food hub for our example sells food products purchased for resale (i.e., Food Hub Farm and Manufactured 

Food commodities) like a traditional wholesale distributor, as well as services (e.g., freight/trucking services). In 

IMPLAN, service sector output is the value of the hired services and does not require margining. If the food hub was 

defined as two separate industries by type of output (i.e., one each for the wholesale and service components), this 

would require margining on the former, but not on the latter. By using ABPs, where the complete spending pattern 

is defined, we avoid this complication. However, since the value of the cost of goods sold are included in the food 

hub’s spending pattern (normally excluded from a wholesale margin) and, therefore, is included in the direct effect 

for output, this implies that the farm and processed food purchase components represent expanded production 

from the underlying production sectors. This is appropriate in the context of the analysis presented here.

5.4.1. Summary Results

The generative impacts of the increase in final demand for food hub products and services for our example are 

displayed in the top section of Table 10, along with the direct effects (computed earlier) and the implied multiplier 

for each component (total effect divided by direct effect).14 As illustrated, for every additional employee added to 

the food hub’s payrolls, an additional 1.46 jobs are generated in backward-linked industries (employment multiplier 

= 2.46). Here, the employment multiplier effects are relatively strong, particularly contributing from the linkages to 

the Food Hub Farm sector (see detailed results). Similarly, the initial $250,000 of direct final demand output 

generates a total of over $406,000 of output impact when all indirect and induced effects are considered, resulting 

in an implied output multiplier of 1.62.

It may be useful to compare your multiplier results with other industries in your study area. However, since the 

multipliers computed above include opportunity costs, direct comparisons are problematic. To compute the implied 

multipliers without opportunity costs, simply create a new scenario in IMPLAN that excludes the opportunity cost 

activity. The summary results for this scenario are shown in the bottom section of Table 10.

Table 10. Summary of Impact Results, with and without Opportunity Costs.

Impact Type Employment Labor Income Value Added Output

With Opportunity Costs

Direct Effect 1.00 $93,750 $110,000 $250,000

Indirect Effect 0.92 $27,151 $39,956 $73,638

Induced Effect 0.54 $30,625 $52,793 $82,565

Total Effect 2.46 $151,526 $202,749 $406,203

Implied Multiplier 2.46 1.62 1.84 1.62

Without Opportunity Costs

Direct Effect 1.00 $93,750 $110,000 $250,000

Indirect Effect 1.00 $30,168 $45,449 $84,498

Induced Effect 0.60 $31,389 $54,111 $84,627

Total Effect 2.60 $155,307 $209,560 $419,125

Implied Multiplier 2.60 1.66 1.91 1.68

 

14 Computations for our example are included in the companion workbook to this guide (PGUIDE companion.xlsx, Impact 

Summary worksheet).
44

5.4.2 Detailed Results

Given the objectives of your study, it may be useful to examine the industries primarily affected by the food hub 

demand expansion; i.e., through the distribution of indirect and induced effects. Figure 19 illustrates the first page 

of detailed results for output (considering opportunity costs) in our example, with the industry sectors sorted by 

total effect (indirect + induced). Not surprisingly, of the total indirect and induced effects ($156,203), the top two 

affected industries are the Food Hub Farm ($23,425) and Manufactured Food ($10,721) sectors, almost entirely 

from indirect effects and from where the food hub’s primary purchases accrue. Note that the positive indirect effect 

from the food hub demand expansion for Manufactured Food is moderated, in part, by the negative margin 

component effect from the portion of opportunity costs allocated to this sector.

The effects on the Insurance carrier sector (437) is the next largest, following from a relatively large food hub 

expenditure category, but also due to the addition of induced effects reflecting spending out of labor income 

changes by households for insurance products. Effects on the Owner-occupied dwellings sector (441) is the next 

largest, accruing entirely from induced effects and reflecting spending out of labor income changes by households 

for the costs of home ownership.15

The effects on Wholesale trade (395) follow next and are a combination of the positive impacts from the food hub 

demand expansion (indirect), the negative effects of the opportunity costs (indirect), and the related effects from 

spending out of the labor income changes (induced). The combined effect in our example is a positive $6,869. While 

not shown in Figure 19, some industry effects are negative (on net), reflecting the opportunity costs activity 

included in this example. Specifically, negative industry effects accrue on net to Other Farm (sector 1, -$1,652), 

Support activities for agriculture and food (sector 19, -$47), Other animal food manufacturing (sector 66, -$30), 

and Farm machinery and equipment manufacturing (sector 262, -$1). In other words, from the negative shock 

applied to the Other Farm sector initially (for the opportunity costs), and the backward linked sectors from which 

Other Farm purchases inputs. Given the relatively large number of industries in your disaggregate model (467 with 

positive output in our example), you will likely want to aggregate some sectors when summarizing your impact 

results, perhaps at the two- or three-digit NAICS level.16

 

15 Specifically, this sector represents the imputed rental activity for owner-occupied dwellings; i.e., what owner occupants 

would pay in rent if they rented rather than owned their homes. In other words, IMPLAN creates an industry out of owning a 

home, where its production function represents repair and maintenance of that home. Output for the sector is ownership 

and is purchased entirely from the household sector. More details are included in Special Sector Definitions at 

support.implan.com.

16 Detailed information for the two and three digit NAICS aggregation schemes for the 536 IMPLAN sectors is available at 

support.implan.com and included within the IMPLAN software (Options > Aggregation).
45

Figure 19. The Scenario Results Screen, Detail Results for Output.
3

6. SUMMARY

Congratulations! If you have made it this far, you have now completed an economic impact assessment 

for a food hub (real or planned) using the IMPLAN software, with a combination of default/secondary data 

from IMPLAN and your own primary data collection efforts. The guide provides you with a systematic 

approach to follow and includes revising your model from both within and outside (via ACCESS) the 

IMPLAN software. The guide is limited to a single region analysis, but includes advice on transforming food 

hub and farm financial data to appropriate IMPLAN sectors, where and how margining is applied, and 

detailed instructions along the way. The guide is also useful beyond the food hub focus to any researcher 

creating a new sector in IMPLAN or, as done here, separating an existing sector (e.g., Farm) into two 

sectors (e.g., Food Hub Farm and Other Farm). 

If you have additional clarifying questions for the example provided here, or for your own impact analysis, 

feel free to contact the authors. In addition, the IMPLAN Forums and other information at 

support.implan.com provide excellent resources and advice for modeling efforts with the software.

Good luck and happy modeling!



7. REFERENCES

IMPLAN, LLC. 2004. The IMPLAN Professional, Version 2.0. Third edition. 

Gunter, A., and D. Thilmany McFadden. 2012. “Economic Implications of Farm to School for a Rural, 

Colorado Community.” Rural Connections. May.

Jablonski, B.B.R., T.M. Schmit. 2016. “Assessing the Economic Impacts of Food Hubs on Regional 

Economies: A Framework that Includes Opportunity Cost.” Agricultural and Resource Economics Review

45(1):143-172.

Schmit, T.M. and R.N. Boisvert. 2014. “Agriculture-Based Economic Development in New York State: 

Assessing the Inter-industry Linkages in the Agricultural and Food System.” EB 2014-03, Charles H. Dyson 

School of Applied Economics and Management, Cornell University. March. 

http://publications.dyson.cornell.edu/outreach/extensionpdf/2014/Cornell-Dyson-eb1403.pdf.

Schmit, T.M., B.B.R. Jablonski, and D. Kay. 2015. “A Practitioner’s Guide to Conducting an Economic 

Impact Assessment of Regional Food Hubs using IMPLAN: A Step-by-step Approach.” USDA, Agricultural 

Marketing Service.

https://apps.ams.usda.gov/MarketingPublicationSearch/Reports/stelprdc5110439.pdf.

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.