United States 

Department of 

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 103

October 2010

Kenneth Hanson

The Food Assistance National

Input-Output Multiplier (FANIOM)

Model and Stimulus Effects

of SNAP
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A Report from the Economic Research Service

Abstract

USDA’s Economic Research Service uses the Food Assistance National Input-Output 

Multiplier (FANIOM) model to represent and measure linkages between USDA’s 

domestic food assistance programs, agriculture, and the U.S. economy. This report 

describes the data sources and the underlying assumptions and structure of the FANIOM 

model and illustrates its use to estimate the multiplier effects from benefits issued under 

the Supplemental Nutrition Assistance Program (SNAP, formerly the Food Stamp 

Program). During an economic downturn, an increase in SNAP benefits provides a fiscal 

stimulus to the economy through a multiplier process. The report also examines the 

different types of multipliers for different economic variables that are estimated by inputoutput multiplier and macroeconomic models and considers alternative estimates of the 

jobs impact. FANIOM’s GDP multiplier of 1.79 for SNAP benefits is comparable with 

multipliers from some macroeconomic models. 

Keywords: Automatic Stabilizer, fiscal stimulus, multipliers, jobs impact, Input-Output 

Multiplier Model, Social Accounting Matrix (SAM) multiplier model, Supplemental 

Nutrition Assistance Program (SNAP), Food Stamp Program. 

Acknowledgments

The author thanks Elise Golan, John Kort, Mark Prell, and Stephen Vogel from USDA, 

Economic Research Service, Randall W. Jackson from the Regional Research Institute 

at West Virginia University, and Geoffrey J.D. Hewings from the Department of Urban 

and Regional Planning at the University of Illinois at Urbana-Champaign for helpful 

comments. Thanks also to ERS editor Dale Simms and ERS designer Vic Phillips.

Kenneth Hanson khanson@ers.usda.gov

The Food Assistance National

Input-Output Multiplier (FANIOM)

Model and Stimulus Effects

of SNAP

Economic

Research

Report

Number 103

October 2010
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

An Input-Output Multiplier (IOM) Model . . . . . . . . . . . . . . . . . . . . . . . . 2

Food Assistance National Input-Output 

 Multiplier (FANIOM) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Exogenous Shock to an IOM Model From Government Expenditures . . 7

Multiplier Effects From an IOM Model . . . . . . . . . . . . . . . . . . . . . . . . . 11

Multipliers for Three Economic Variables: Production, 

 GDP, and Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Types of Multipliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Multiplier Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Distribution of Multiplier Effects Among Industries . . . . . . . . . . . . . . 18

Comparing and Reconciling Multipliers With Macroeconomic

 and IOM Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Comparison of Multipliers From Alternative 

 Macroeconomic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Reconciling IOM Jobs Impact Estimates With CEA Estimates . . . . . . 21

Limitations of an IOM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Linear Structure of the IOM Model and Fixed Prices . . . . . . . . . . . . . 25

Use of Average Coefficients From Base-Year Data . . . . . . . . . . . . . . 25

Budget Deficits, Interest Rate Effects, and Monetary Policy . . . . . . . 26

Household Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Appendix 1: Employment Data by Industry . . . . . . . . . . . . . . . . . . . . . 37

Appendix 2: Reallocating Proprietors’ Income From 

 Capital to Labor Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Appendix 3: Food Assistance National Input-Output 

 Multiplier (FANIOM) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

 

Recommended citation format for this publication: 

Hanson, Kenneth. The Food Assistance National Input-Output 

Multiplier (FANIOM) Model and Stimulus Effects of SNAP. ERR-103. 

U.S. Dept. of Agriculture, Econ. Res. Serv. October 2010.
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Economic Research Service/USDA

Summary 

USDA’s Economic Research Service uses the Food Assistance National 

Input-Output Multiplier (FANIOM) model to represent and measure linkages between USDA’s domestic food assistance programs, agriculture, and 

the U.S. economy. This report describes the data sources and the underlying 

assumptions and structure of the FANIOM model and illustrates its use to 

estimate the multiplier effects from benefits issued under the Supplemental 

Nutrition Assistance Program (SNAP, formerly the Food Stamp Program). 

What Is the Issue?

An increase in SNAP benefits provides a fiscal stimulus to the economy 

during an economic downturn. When resources are underemployed, the 

increase in SNAP benefits starts a multiplier process in which inter-industry 

transactions and induced consumption effects lead to an economic impact 

that is greater than the initial stimulus. An input-output multiplier (IOM) 

model, such as FANIOM, tracks and measures this multiplier process. 

IOM and macroeconomic models have been used for assessing the multiplier effects from government expenditures authorized under the American 

Recovery and Reinvestment Act of 2009 (ARRA), a Federal response to the 

recession that began in 2008. There is potential for confusion and misinterpretation of reported multiplier effects from different models. This report 

clarifies differences in model assumptions and multipliers. It examines the 

different types of multipliers for different economic variables that are estimated by IOM and macroeconomic models, and considers alternative estimates of the jobs impact. 

What Did the Study Find?

FANIOM provides a framework for calculating different types of multipliers 

for different variables at the national level. Multipliers are calculated for 

production, GDP, and employment, and they are adjusted to domestic market 

effects by netting out the share of new demand met by imports. A type I 

multiplier includes the direct and indirect effects from a fiscal stimulus, while 

a type II multiplier also includes the induced effects from the labor income 

and the type III multiplier also includes the induced effects from capital 

income. 

The type III GDP multiplier is the appropriate multiplier for assessing the 

impact of government expenditures on economic activity (GDP and production) during an economic downturn. The type I employment multiplier (with 

import adjustment) is the appropriate multiplier for assessing the jobs impact 

from government expenditures. The jobs impacts from the FANIOM model 

for the type II and type III multipliers are consistent with other input-output 

multiplier models, but higher than estimates from macroeconomic models 

and from empirical analysis of data on the quarter-to-quarter change in 

employment relative to a change in GDP. 
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The FANIOM analysis of SNAP benefits as a fiscal stimulus finds that: 

•	An increase of $1 billion in SNAP expenditures is estimated to increase

economic activity (GDP) by $1.79 billion. In other words, every $5 in

new SNAP benefits generates as much as $9 of economic activity. This

multiplier estimate replaces a similar but older estimate of $1.84 billion

reported in Hanson and Golan (2002). 

•	The jobs impact estimates from FANIOM range from 8,900 to 17,900

full-time-equivalent jobs plus self-employed for a $1-billion increase in

SNAP benefits. The preferred jobs impact estimates are the 8,900 fulltime equivalent jobs plus self-employed or the 9,800 full-time and parttime jobs plus self-employed from $1 billion of SNAP benefits (type I

multiplier). 

•	Imports reduce the impact of the multiplier effects on the domestic

economy by about 12 percent.

How Was the Study Conducted?

At the core of the FANIOM model are data from the U.S. Bureau of 

Economic Analysis (BEA), Benchmark Input-Output Accounts for 2002. 

Data from BEA National Income and Product Accounts are used to specify 

the induced effects from household income (labor and capital). Employment 

data from the U.S. Bureau of Labor Statistics, U.S. Bureau of Economic 

Analysis, and U.S. Department of Agriculture are used in estimating the jobs 

impact. The GAMS software was used to calculate the FANIOM multipliers. 
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Introduction

USDA’s Economic Research Service uses the Food Assistance National 

Input-Output Multiplier (FANIOM) model to represent and measure linkages between USDA’s domestic food assistance programs, agriculture, and 

the U.S. economy. This report describes the data sources and the underlying 

assumptions and structure of the FANIOM model and illustrates its use to 

estimate the multiplier effects from benefits issued under the Supplemental 

Nutrition Assistance Program (SNAP, formerly the Food Stamp Program). 

The report also examines the different types of multipliers for different 

economic variables that are estimated by input-output multiplier (IOM) 

and macroeconomic models and considers alternative estimates of the jobs 

impact. 

An increase in SNAP benefits provides a fiscal stimulus to the economy 

during an economic downturn. When resources are underemployed, the 

increase in SNAP benefits starts a multiplier process in which inter-industry 

transactions and induced consumption effects lead to an economic impact 

that is greater than the initial stimulus. An IOM model, such as FANIOM, 

tracks and measures this multiplier process. 

IOM and macroeconomic models have been used for assessing the multiplier effects from government expenditures authorized under the American 

Recovery and Reinvestment Act of 2009 (ARRA), a Federal response to 

the 2008 recession. There is potential for confusion and misinterpretation 

of reported multiplier effects from different models. Confusion can occur in 

regard to different types of multipliers and multipliers for different economic 

variables. Furthermore, different assumptions underlying IOM models and 

macroeconomic models can lead to multiplier effects that can be equivalent 

or widely different. The comparison and interpretation of model results can 

be difficult. This report clarifies these differences in model assumptions and 

multipliers. 

Chapter 2 describes the FANIOM model and how it can be used to analyze 

the multiplier effects from an increase in SNAP benefits (government expenditure). Chapter 3 describes the different economic variables for which 

multipliers are calculated, describes the different types of multipliers, and 

calculates them for SNAP benefits. Chapter 4 compares the multiplier effects

from an IOM model with those from several macroeconomic models and 

discusses some issues in reconciling the jobs impact estimates between these 

two types of models. Chapter 5 describes the conditions associated with an 

economic downturn that enable government expenditures to work as a fiscal 

stimulus, and examines the limitations in using an IOM model for analyzing 

the multiplier effects from government expenditures as a fiscal stimulus. 
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An Input-Output Multiplier (IOM) Model

Different economic models can be used for multiplier analysis of government 

expenditures (see box, “Historical Digression on the Roots of Multiplier 

Models”). While this report emphasizes the use of an IOM model, it extends 

the model to be equivalent to a social accounting matrix (SAM) multiplier 

model and compares the multiplier effects from an IOM model with those 

from macroeconomic models. 

Food Assistance National Input-Output 

Multiplier (FANIOM) Model 

USDA-ERS developed the Food Assistance National Input-Output Multiplier 

(FANIOM) model to assess the economywide and sector effects of U.S. 

domestic food assistance programs. While the FANIOM model described in 

this report is tailored to analyze the multiplier effects of SNAP benefits at the 

national level, it can also be used to analyze the effects of other exogenous 

changes at the national level.1 

The FANIOM model is based on two primary sources of data: 2002 

Benchmark Input-Output Accounts and National Income and Product 

Accounts (NIPA). The U.S. Bureau of Economic Analysis (BEA) provides 

both sets of data. Annual NIPA data are used in the model to specify the 

income flows from industry to households and from households to consumer 

expenditures, which involve specification of various tax and savings rates 

that are not included in the input-output accounts. Merging NIPA data 

with the input-output accounts creates a Social Accounting Matrix (SAM) 

and allows the IOM model to calculate the induced effects and estimate 

the equivalent of a SAM multiplier. Calculation of the induced effects is 

discussed in context of the multiplier types. 

The FANIOM model also involves data on employment (or jobs) by industry. 

Various measures of employment are included in the model’s database. 

These include the number of full-time plus part-time jobs (FTPT-jobs), 

full-time equivalent jobs (FTE-jobs), production workers (prod-jobs), and 

self-employed (self-employed). (See appendix 1 for details about the sources 

of employment data.) Combinations of these employment measures can be 

used. For analysis related to agriculture, it is important to include the selfemployed since they make up a large share of the total labor force in the 

industry and they can adjust their hours of work on the farm. The jobs impact 

measures in this report are the FTE-jobs plus self-employed, and the FTPTjobs plus self-employed. 

The Benchmark Input-Output Accounts used by the FANIOM model are 

annual data prepared at 5-year intervals, based on data from the Economic 

Census (Stewart et al., 2007). The most recent benchmark account is 

for 2002, with 426 industries producing 428 commodities (or goods and

services), with many industries and commodities defined at the 6-digit 

NAICS (North American Industry Classification System) level. The number 

of commodities closely corresponds to the number of industries (groups of 

firms that produce similar commodities). The term “sector” is sometimes 

used as a proxy for commodities and industries, and the term “goods and 

1Hanson and Oliveira (2009) used the 

model to examine the impact of WIC 

on agriculture, Hanson (2003) used an 

earlier version of the model to examine 

the impact of the school meal programs 

on agriculture, and Hanson and Golan 

(2002) used an earlier version of the 

model to assess the multiplier effects of 

food stamps.
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The description and derivation of the multiplier as an economic process has 

its roots in the development of several economic models. First, in response 

to the Great Depression (1929-1933), Kahn (1931) and Keynes (1936) 

developed the aggregate/macroeconomic multiplier to explain how government interventions during a recession can stimulate the economy. An extensive early literature discussing the aggregate multiplier process includes 

Samuelson’s (1940) “Theory of Pump-Priming” and Machlup’s (1939)

discussion of its temporal dimension. Following the Great Depression, 

measurement of national income and the effect of fiscal policy on it were 

of keen interest (Clark, 1938; Samuelson, 1942; Hansen, 1951). 

An aggregate multiplier process is embedded in a macroeconomic forecasting model to some extent, depending on the underlying assumptions 

about agent behavior and market adjustment to disequilibrium between 

supply and demand. The first macro-econometric models were Keynesian 

and were influenced by this early literature on income determination and 

the multiplier process. These models were used to analyze fiscal stimulus 

packages during the 1960s. During the 1970s, they continued to be used but 

were criticized for the treatment of market adjustment processes and the 

bounded rationality of the agents in the model. In response, new generations 

of macro-econometric models arose where agents are forward looking, and 

markets adjust quickly and fully to disequilibrium despite potential market 

imperfections (Diebold, 1998; Mankiw, 2006; Woodford, 2009). Under the 

new paradigms, the multiplier effects from fiscal policy are dampened if not 

negated. The recession that started in 2008 has brought back an interest in 

multiplier effects from a fiscal stimulus. 

A second model of the multiplier process is based on the input-output 

accounts developed by Leontief (1936, 1986). An input-output multiplier 

model includes the direct and indirect effects of a change in demand on 

industry production. It can also include the induced effects from the additional expenditures generated by the increased income to households. Moore 

(1955) and Moore and Petersen (1955) are two early applications of inputoutput multiplier analysis, looking at the impact of a change in industry 

demand on a regional economy. Miernyk (1965) published what has become 

a classic on input-output analysis that introduced the terminology of Type 

I and Type II multipliers, and which treats the induced effects of the inputoutput multiplier as equivalent to the aggregate multiplier. Miller and Blair 

(1985) published a classic textbook on input-output nalysis, which discusses 

the different types of multipliers, as does Hewings (1985). 

The Social Accounting Matrix (SAM) multiplier is a third model of the 

multiplier process. A SAM expands upon the input-output accounts by 

fully integrating a nation’s National Income and Product Accounts with 

the input-output accounts, which involves accounting for taxes and savings 

and other inter-institutional income flows. Early development of the SAM 

multiplier is found in the work of Pyatt and Round (1979) and Defourny 

and Thorbecke (1984), and recent summaries in Pyatt (2001) and Robinson

(2006). IMPLAN (2010) provides a data-software package that applies a 

SAM-type multiplier similar to one developed in this report. Holland and 

Wyeth (1993) discuss moving from an input-output type II to a SAM-type 

II multiplier. 

Historical Digression on the Roots of Multiplier Models
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services” is used interchangeably with the term “commodities.” (For more 

information about the structure and content of the input-output accounts, see 

box, “Commodity and Income Flows in the Input-Output Accounts.”)

Annual input-output accounts through 2008 have been prepared by the U.S. 

Bureau of Economic Analysis (2010a). They reduce the detailed farm and 

food processing sectors of the benchmark accounts to one sector each, which 

limits their usefulness for studying the economic impacts on food and farm 

sectors. Therefore, the FANIOM model makes use of less recent 2002 data to 

achieve the industrial detail for studying the effect of Federal food assistance 

programs. 

The U.S. Bureau of Labor Statistics (2009b) has developed annual inputoutput accounts through 2008 based on the 2002 benchmark accounts. The 

accounts are used to project the employment requirements for 202 industries 

10 years into the future.2 For analysis related to farm and food issues, this set 

of accounts disaggregates food processing reasonably well (though less than 

the benchmark accounts), but it only disaggregates agriculture into crop and 

livestock sectors. Methods exist to disaggregate more recent but more aggregated input-output accounts using older but more disaggregated accounts 

(Jackson and Comer, 1993). Future work may pursue this data development 

to clarify whether the new data base is worthwhile in the sense of more 

accurate impact estimates that are significantly different at the national level. 

Using such a method to create a more recent disaggregated input-output 

account would add to the cost of developing and updating the model. 

More recent detailed input-output accounts and models for multiplier 

analysis have been developed for 2008 by the Minnesota IMPLAN Group, 

Inc. (2010, noted subsequently as IMPLAN) and by the U.S. Bureau of 

Economic Analysis (2010b, noted subsequently as RIMS II) for 2007/08 

using various procedures and data to update the 2002 benchmark inputoutput accounts. Both IMPLAN and RIMS II are designed to be used at the 

State or county level, so multipliers may be more strongly affected by the 

year of data as businesses come and go from a region. Use of IMPLAN and 

RIMS II entail a monetary cost. With IMPLAN, a user purchases the data 

and software to conduct the multiplier analysis independently. With RIMS 

II, a user purchases specific multipliers. Though the analysis in this report 

could be done with IMPLAN, USDA-ERS has chosen to develop a national 

IOM model that is easy to maintain (low cost), does not require significant 

data updating, can be easily used for other types of multiplier analysis at the 

national level, and can serve as a teaching tool. 

FANIOM, like other IOM models, is a system of linear simultaneous equations. Model parameters are specified as average coefficients from annual 

data for 2002. Derivation of the multipliers is an exercise of comparative 

statics; given an exogenous change, the model determines the new levels 

of economic activity consistent with that change. The process by which 

the economy adjusts to the new equilibrium level of economic activity 

is not modeled (see box, “Timeframe for Multiplier Process to Work”). 

The comparative static solution to an IOM model is traditionally found by 

matrix inversion of the system of linear simultaneous equations (Miller and 

Blair, 1985). Rather than using matrix inversion to calculate multipliers, 

the FANIOM model is solved as a system of simultaneous equations using 

2These accounts include a “domestic employment requirement table” to 

estimate the jobs impact for a change 

in final demand, such as exports or 

personal consumption expenditures, 

which this report compares to FANIOM 

estimates.
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The input-output accounts describe the flow of commodities from the industries that produce them to the industries that use them as inputs in production 

and to final demand. The inter-industry commodity flows are an essential 

part of the multiplier process. They are the basis from which an increase 

in production from an exogenous change in demand for a commodity gets 

passed on to other industries as demand for inputs. Final demand consists 

of a number of components: personal consumption expenditures (PCE), 

government purchases, private fixed investment as business equipment and 

structures and as residential construction, inventory change, and exports 

to the rest of the world. Imports are treated as a negative component of 

final demand since they are purchased as intermediate inputs by domestic 

industries and by the other components of final demand (except exports). 

All users of a commodity, be it industry or final demand, purchase the 

same share of the domestically produced commodity and imports of that 

commodity. The import share by commodity has an impact on the multiplier 

effects. A greater import share results in a smaller multiplier effect 

on domestic production. 

Corresponding to the commodity flows from industry to industry but in the 

opposite direction are the income payments for the purchase of the commodities, which are the intermediate cost of production. To fully specify the cost 

of production by industry, the input-output accounts also include industry 

payments to factors of production in the form of employee compensation 

(labor income) and operating surplus (capital income). Labor income is a 

gross measure of labor income to hired workers that includes the employer 

and employee contributions for social insurance (social security and 

Medicare). Capital income is one value for each industry in the input-output 

accounts, but in NIPA it includes interest payments, dividends, rent, proprietors’ income, retained earnings, profit tax, and depreciation. For consistency with including the self-employed as part of the employment measure, 

proprietors’ income is reallocated from capital income to labor income. 

Appendix 2 discusses how proprietors’ income is reallocated. Interest, 

dividends, and rent are treated as the capital income that households receive 

as a return to ownership of financial assets and property. Excise and sales 

taxes plus import tariffs less subsidies to industry are treated as an additional 

component of factor payments to derive industry value added as the sum 

of factor payments. Government revenue from these taxes will increase as 

expenditures on commodities increase. Due to issues about the treatment of 

excise and sales taxes in the input-output accounts, estimating the increase 

in government revenue generated from these taxes in the multiplier process 

is unreliable.

Another feature of the input-output accounts is that all commodity 

purchases, as intermediate or final demand, are recorded in the accounts at 

producer prices. But the purchase of a commodity may also involve retail 

trade, wholesale trade, and transportation margins for the service industries 

that deliver the commodity from the producer to the purchaser. The trade 

and transportation margins by commodity are maintained separately in the 

accounts, and can be used to calculate commodity expenditures in purchaser 

prices. In deriving the multiplier effects for a change in consumer expenditures it is important to specify the change in expenditures for commodities in 

purchaser prices (value at the retail outlet) and convert that into a change in 

expenditures for commodities in producer prices (value at the factory gate), 

plus a change in the expenditures on trade and transportation services.

Commodity and Income Flows in the Input-Output Accounts
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The multiplier effects on economic activity occur over time. There is no 

definitive analysis about how long it takes for the full multiplier effects to 

occur. Still, it is possible to provide some guidance on the timeframe for the 

economic effects from the multiplier process to occur. The initial increase in 

expenditures by SNAP recipients has a direct effect on the economic activity 

of the producers of the goods and services purchased, retail establishments, 

and the wholesale and transportation systems. These effects will occur 

quickly, particularly for SNAP benefits as recipients spend them during 

the month that they receive them. The producer of the goods and services 

may not respond as quickly to the direct effect by increasing production if 

inventories are plentiful. Consequently, the short-term effectiveness of a 

fiscal stimulus will depend on inventory levels. If inventories are low, the 

producer will increase production during the current or next month following 

the expenditures and will order new inputs. The new input orders will 

stimulate production by the industries that make them, generating the next 

round of the multiplier process and the first round of indirect effects. The 

new input orders are likely to occur during the same quarter as the initial 

expenditure. 

Also occurring during this first quarter in response to the direct effects and 

initial indirect effects is an increase in labor income for the directly affected 

industries and their input suppliers. A first round of induced effects on 

economic activity is generated from the additional labor income. These 

occur as households receive their paychecks, which will happen during the 

first quarter. Less clear is when the induced effects from capital income 

occur, but they are more likely to occur later than the induced effects from 

labor income, as households receive capital income less quickly and less 

often than wages. 

The direct effects and initial rounds of indirect and induced effects will arguably occur quickly and most probably during the first quarter of the initial 

expenditures. The subsequent rounds of indirect and induced effects take 

place sequentially over time. Though empirical evidence does not exist, to 

put some bounds on the timeframe it seems reasonable to argue that each 

round of effects will take an additional quarter. Thus, within a year, four 

rounds of indirect and induced effects will likely occur. So what percent of 

the multiplier process is accounted for by the direct effects and four rounds 

of indirect and induced effects? One response to this question is from the 

input-output method of taking a power series approximation to the inverse 

of the (I-A) matrix (Miller and Blair, 1985, pp. 22-24; Hewings, 1985, p.

14). Each round of induced economic effects from the multiplier process

is equivalent to adding an additional term in the power series. A feature of 

a power series is that the impact of each additional term is reduced exponentially. “In many applications it has been found that after about 7 rounds 

of indirect effects the impact is insignificantly different from zero. So, it is 

possible to capture most of the effects associated with a given final demand 

by using the first few terms in the power series.” By four rounds in the first 

year, it is reasonable to claim that 75 percent of the multiplier effects will 

have been accounted for. 

Timeframe for Multiplier Process To Work
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an optimizing algorithm in the GAMS software (Brooke et al., 1992). For a 

linear IOM model, the solution will be robust to the solution method, be it 

matrix inversion or optimization. A benefit of using GAMS is that the IOM 

model is specified as a system of algebraic equations, which is a flexible 

means of developing modifications to a traditional IOM model, particularly 

related to the induced effects. Other authors have preferred to use alternative 

matrix decompositions to achieve the same end (Pyatt and Round, 1979). 

Appendix 3 provides a technical description of the FANIOM model. 

FANIOM is a partial-equilibrium, static model of the U.S. economy. By 

its nature, the model is unable to capture all economywide impacts of any 

program, such as opportunity costs of the government expenditures or the 

implications of the revenue sources. The report does discuss how macroeconomic models have addressed stimuli programs and their potential implications on future interest rates, inflation, and household expectations 

and behavior. 

Exogenous Shock to an IOM Model 

From Government Expenditures

Translating government expenditures as a fiscal stimulus into an exogenous 

shock to the IOM model is a critical step in using an IOM model to estimate 

the multiplier effects. How to do this is specific to the type of expenditure 

or the type of project/program that is funded. For instance, investment into 

infrastructure is an increase in government demand for construction activity; 

extension of unemployment insurance benefits is a transfer to households; 

and general aid to States can be used in many ways such as funding education, primarily teacher salaries. 

When government expenditures go directly to households as transfers, 

wages, or tax rebates, there is the issue of how much is spent, how much is 

saved (or used to pay off debt), and how much is taxed. The higher a household’s income, the more likely some of it will be saved and taxed, which 

reduces the multiplier effects from the expenditures. This is a reason to 

carefully translate government expenditures into an exogenous change in 

final demand.

This report focuses on government expenditures as SNAP benefits to lowincome households. SNAP is the Nation’s largest domestic nutrition assistance program for low-income Americans. In fiscal year (FY) 2009, the 

program served 33.7 million Americans in an average month and issued 

$50.4 billion of SNAP benefits over the year, including $4.3 billion from

ARRA legislation. As a means-tested entitlement program, SNAP automatically responds to changing economic conditions, providing assistance to 

more households during an economic downturn or recession and to fewer 

households during an economic expansion (figs. 1 and 2). While SNAP is 

an automatic fiscal stimulus, SNAP can also serve as a discretionary fiscal 

stimulus, meaning that Congress can change the program in any given 

year as economic conditions warrant. For example, as part of the government economic stimulus package of 2009 (ARRA), Congress temporarily 

increased the maximum benefit amounts to recipients by 13.6 percent (of 

2009 levels). Increasing benefits to SNAP recipients provides a sudden 

stimulus because SNAP recipients spend the benefits quickly and fully. An 
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estimate of the expected increase in SNAP benefits in response to the 2008 

recession—based on Congressional Budget Office (CBO) projections—is 

presented in the box, “SNAP Response to the 2008 Recession.” 

SNAP recipients use the benefits quickly and fully, with no effect on the 

savings or taxes of the recipients. The issue is in translating the increase in 

benefits into a change in consumer expenditures on goods and services. As 

stipulated by program rules, recipients spend all the benefits on food at home, 

1976 78 80 82 84 86 88 90 92 94 96 98 2000 02 04 06 08 10

0

2.0

4.0

6.0

8.0

10.0

12.0

0

5

10

15

20

25

30

35

40

Unemployment rate, percent

FSP caseload, million persons

Figure 1

Unemployment rate, percent

Fiscal year

FSP caseload, million persons

Source: U.S. Department of Agriculture, Food and Nutrition Service; U.S. Bureau of Labor Statistics, Current Population Survey;

and Congressional Budget Office.

Annual SNAP caseload and unemployment rate, 1976-2008, with CBO projections for 2009-11

1976 78 80 82 84 86 88 90 92 94 96 98 2000 02 04 06 08 10

0

2.0

4.0

6.0

8.0

10.0

12.0

0

10

20

30

40

50

60

Unemployment rate, percent

FSP benefits $ billion

FSP benefits $ billion (2008)

Figure 2

Unemployment rate, percent

Fiscal year

FSP benefits, $ billion

Source: U.S. Department of Agriculture, Food and Nutrition Service; U.S. Bureau of Labor Statistics, Current Population Survey;

and Congressional Budget Office.

Annual SNAP benefits and unemployment rate, 1976-2008, with CBO projections for 2009-11
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Congressional Budget Office (CBO 2009a,b,d) estimates for the expected 

increase in SNAP benefits due to the recession that started in January 2008 are 

presented in the table below. The benefit amounts are reported in “current” 

dollars (not adjusted for inflation) for the year that the benefits are issued. 

There are two components: (1) the additional benefits from an increase in 

caseloads and (2) the additional benefits from a 13.6-percent increase in the 

maximum benefit amount from the American Recovery and Reinvestment 

Act of 2009 (ARRA). The combined increase in benefits issued range from a 

high of $15.4 billion in 2009 and $14.7 billion in 2010 to a low of $4.2 billion

in 2008 and 2012 (excluding 2013 when the average monthly caseload is 

expected to fall).

As part of the 2009 fiscal stimulus package, ARRA included a 13.6-percent 

increase in the SNAP maximum benefit amounts based on the June 2008 

Thrifty Food Plan cost for a four-person reference family. The maximum 

benefit is the amount of SNAP benefits received by recipients who have no 

“net income” (calculated as a family’s gross income less deductions). The 

maximum benefit varies by household size (and whether a family resides in 

Alaska or Hawaii). For example, the maximum benefit for a 4-person household (in the 48 States and DC) increases by $80 from $588 to $668 in 2009 as 

a result of the ARRA. With legislation passed in February 2009, States were 

able to implement the adjustment to households’ benefits starting in April 

2009 (USDA-FNS, 2009). ARRA stipulated that the “adjusted” maximum 

benefit amounts remain in effect until the June cost of the Thrifty Food Plan 

(TFP), which rises with food price inflation, exceeds this adjusted maximum 

benefit amount. The cost of the June TFP is the usual basis for setting the 

SNAP maximum benefits for an upcoming fiscal year. 

According to CBO (2009d) cost projections for ARRA, the additional SNAP 

benefits issued during FY 2009 are estimated to be $4.812 billion. The estimate is for one-half of the fiscal year (April through September), reflecting the 

timing of when the additional benefits are issued. For FY 2010, the additional 

benefits are estimated at $6.1 billion, decreasing to $4.4 billion for FY 2011,

$3.1 billion for FY 2012, $1.6 billion in FY 2013, and zero thereafter. The

estimated additional benefits from the ARRA get smaller as the benefits with 

the adjusted maximum benefit is compared to the benefits that would be issued 

without passage of ARRA, given expected food price inflation. 

SNAP Response to the 2008 Recession

Estimated additional SNAP benefits issued following the 2008 

recession, 2008-13 

2008 2009 2010 2011 2012 2013 Sum

$ billion

Estimated additional 

SNAP benefits 4.238 15.417 14.660 8.194 4.273 1.639 48.421

From caseload 

increase 4.238 10.605 8.602 3.832 1.158 0.000 28.435

From SNAP 

benefit adjustment 0.000 4.812 6.058 4.362 3.115 1.639 19.986

Source: CBO (2009a,b,d) cost estimates. 
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but empirical research finds that recipients shift some cash income that was 

being spent on food into nonfood expenditures upon receiving the benefits. 

Consequently, food expenditures increase by only a percentage of the total 

increase in benefits, while nonfood expenditures increase by the remaining 

amount. 

This report assumes that food expenditures increase by 26 percent of the 

increase in SNAP benefits. Fraker (1990) and Fox et al. (2004) reviewed a

number of studies that estimated the effects of SNAP benefits on food expenditures by households and the shifting of cash into nonfood expenditures. 

Estimates ranged from 0.17 to 0.47, indicating that a $1 increase in SNAP

benefits would lead to additional food expenditures of between $0.17 and

$0.47. Estimates based on data after 1977 changes in the SNAP purchase

requirement range from 0.23 to 0.35. Levedahl (1995) estimates a marginal 

propensity to consume food from SNAP benefits of 0.26, while KramerLeBlanc et al. (1997) estimate a value of 0.35, and Breunig and Dasgupta 

(2005) estimate a value of 0.30. These estimates are considered more 

relevant to current program circumstances. 

The increase in food-at-home expenditures is distributed among specific food 

items using average food expenditure shares from the personal consumption expenditure (PCE) data in the input-output accounts. Similarly, nonfood 

expenditures are distributed among the nonfood goods and services in the 

PCE data according to average shares of nonfood expenditures. The average 

expenditure shares are calculated in purchaser prices, at retail, and then 

converted to expenditures for goods and services at producer prices and for 

trade and transportation services. These average shares for food and nonfood 

expenditures are used to approximate what should be marginal expenditure 

shares for an increase in expenditures from a change in SNAP benefits. This 

use of average shares as marginal shares is typical of an IOM model but 

could be a source of model misspecification. Households’ average use of 

income may differ from how they spend an increase in income. Marginal 

expenditure shares specified from econometrically estimated income elasticities could be used to modify the FANIOM model. This would be a useful 

extension of the model. 
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Multiplier Effects from an InputOutput Multiplier (IOM) Model

There are a number of different types of multipliers that can be derived from 

an IOM model and each type can be calculated for a number of economic 

variables. This chapter clarifies the differences among these multipliers by 

describing what they are and how they are calculated. Using the FANIOM 

model, these multipliers are calculated for SNAP benefits. 

Multipliers for Three Economic Variables: 

Production, GDP, and Employment

A “multiplier” is a ratio between changes in two economic variables. A 

multiplier expresses the change in one economic variable that is endogenous—i.e., determined within the framework of the model—as result of a 

change in a second economic variable that is exogenous—i.e., determined 

outside of the model. This study considers three endogenous variables of 

economic activity: production, gross domestic product (GDP or value 

added), and employment (jobs).3

Production is a measure of economic activity that corresponds to the cash 

receipts or revenue from the sale of goods and services. It is a gross measure 

of economic activity in that it includes inter-industry transactions. Relative 

to the value of production, GDP is net of inter-industry expenses, or the 

purchase of inputs from other industries. An IOM model embraces both 

measures of economic activity since the input-output accounts include 

inter-industry transactions. Macroeconomic models focus on GDP to 

measure economic activity. Comparing multipliers from these two modeling 

approaches can be confusing if it is unclear whether a production or GDP 

multiplier is being reported. It is important to make the distinction clear since 

the production multiplier is close to twice the magnitude of the GDP multiplier. Following Miernyk (1965), the U.S. Bureau of Economic Analysis uses 

the term “total requirements” as the direct plus indirect production activity 

generated by a change in final demand (Horowitz and Planting, 2006). This 

report makes the distinction between these two multipliers by referring to a 

production multiplier and a GDP multiplier. 

The FANIOM model is specified with data for 2002. Most applications will 

pertain to events in a more recent year. That is, the exogenous shock will be 

in dollar value for the more recent year. To the extent that the structure of the 

IOM model remains the same over time as specified by the data underlying 

the model, the production and GDP multipliers from 2002 will be applicable to the dollar value of the exogenous change in a more recent year. The 

assumption of an unchanging structure is unlikely to be fully true, but, practically, the change in an IOM model structure over 5 to 10 years is minimal. 

To demonstrate, a Type I production multiplier (as described below) has 

been calculated from three benchmark input-output accounts for 1992, 1997, 

and 2002 for an exogenous change in household expenditures from $1 billion

of SNAP benefits. The multipliers ranged from 1.84 for 1992 (Hanson and

Golan, 2002) to 1.92 for 1997 (unpublished), with an intermediate value of 

1.88 for 2002 (in this report). Similarly, Stern (1975) estimated a multiplier 

for an exogenous change in final demand from a set of government transfers 

3Multipliers for other endogenous 

economic variables such as household 

income can be calculated. This report 

focuses on these three variables since 

they are most commonly discussed in 

context of the 2008 recession.
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to households using the 1972 benchmark input-output accounts and found a 

value of 1.87. The evidence suggests that production and GDP multipliers 

based on 2002 data will work reasonably well for an application that pertains 

to economic activity in 2008 through 2012. 

The employment multiplier (jobs impact) is a demand for labor by industry 

to carry out the new production activity. The new demand can be met by 

employing new workers, having existing employees work more hours, or 

not laying off existing employees and/or not reducing hours of work. These 

are the created and saved jobs. The model cannot distinguish among the 

means by which the jobs impact occurs; it provides a general estimate of the 

demand for additional labor. 

Calculation of employment multipliers starts with data on average industry 

jobs-production ratios for each employment measure. The IOM model estimates the change in production by industry from the multiplier process. The 

change in industry jobs is estimated for each industry as the product of the 

industry jobs-production ratio and the change in industry production. 

The employment multiplier is calculated by the model as the number of jobs 

per billion dollars of SNAP benefits (or other form of government expenditures) in 2002 dollars, the year of the data for model specification. But it is 

preferable to report the jobs impact in terms of the year for which the study 

is being conducted, such as 2008. Unlike the production or GDP multipliers, 

the magnitude of the employment multiplier is sensitive to the number of 

years between the year for which the model is specified (2002) and the 

year in which the results are reported (2008). Adjusting the employment 

multiplier to a more recent year depends on the rate of inflation and labor 

productivity. 

Labor productivity tends to increase over time so the amount of labor necessary to produce a given amount of output tends to fall. To adjust for labor 

productivity, the employment-output ratios of 2002 are reduced by a labor 

productivity adjustment factor of 0.873 using the U.S. Bureau of Labor 

Statistics (2009a) major sector productivity index (output per hour) for the 

business sector. Given an increase in the price of commodities, the dollar 

value for a quantity of output in a more recent year is larger than the dollar 

value in an earlier year. To reflect this change in the dollar value of a quantity 

of output, the employment-output ratios (number of jobs per unit of output) 

are reduced by an inflation adjustment factor of 0.868 using the implicit price 

deflator for the labor productivity measure (U.S. Bureau of Labor Statistics, 

2009a). Combined, these two adjustments result in an overall employment 

adjustment factor of 0.758 that reduces the employment impact per billion 

dollars of output in 2002 dollars to an employment impact per billion dollars 

of output in 2008 dollars.4 

Types of Multipliers

The multiplier effects depend on more than simply which pair of endogenous 

and exogenous variables is considered. For any given pair, there are several 

“types” of multipliers that depend on how other variables in the model are 

treated—specifically, which variables are held constant or unchanging in the 

calculation of a multiplier and which variables are allowed to vary (Miernyk, 

4The 0.758 employment adjustment factor is very close to a value of 

0.754 derived from the BLS (2009b)

Employment Requirement Table, for 

2002 and 2008, for an exogenous change 

in personal consumption expenditures.
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1965; Hewings, 1985; Miller and Blair, 1985). This study distinguishes three 

types of multipliers (type I, II, and III), which have their roots in alternative 

methods of analyzing the multiplier process (see box, “Historical Digression 

on the Roots of Multiplier Models,” on page 3). A further distinction is 

whether the multipliers are adjusted to domestic economic effects by netting 

out the share of goods and services that are imported into the U.S. market 

(import adjustment). Each type of multiplier is calculated for the three endogenous variables of economic activity considered in this study.

A type I multiplier consists of two components: the “direct” and “indirect” 

effects due to an exogenous change in final demand. For an increase in SNAP 

benefits, the direct effects are the share of expenditures made by SNAP recipients that go to domestic producers. Given the structure of the input-output 

accounts, the direct effects are distributed among the producers of the goods 

and services being purchased, the retailer, and the wholesale and transportation systems. These industries increase production to supply the domestic 

share of the new demand for goods and services. An increase in imports 

completes the direct effects, but these are not a fiscal stimulus to the domestic 

economy. Imports are a leakage in the multiplier process for the domestic 

economy, but they do provide a stimulus to the rest of the world. The direct 

effects from SNAP benefits tend to occur completely in the month of receipt, 

a quick and full response to the fiscal stimulus by the government. 

The indirect effects are the inter-industry demand for inputs to production 

that arise in response to the direct effects from the new demand for goods 

and services. An IOM model hinges on the input-output accounts that record 

the inter-industry use of goods and services in the production of other goods 

and services. It is this set of complex interactions among industries that 

provides the basis for calculating the indirect effects for the type I multiplier. The indirect economic activities are distributed over time, with some 

occurring sooner than others. Most indirect effects will occur within the 

year, for they involve the refilling of inputs used in producing the goods and 

services purchased by food stamp recipients. For instance, the baker who 

sells more loaves of bread will order more flour from the miller, who will 

process more wheat to fill the order. All stages of the new production activity 

incur new demand for such basic inputs as energy and labor, as well as the 

need for transportation services. Given the heightened demand for food with 

SNAP benefits, a significant share of the new demand for inputs into food 

processing is for farm products. 

A type II multiplier expands the type I multiplier with the induced effects 

from labor income (net of taxes and savings). The jobs created or saved 

through the direct and indirect effects of the type I multiplier process have a 

corresponding increase in labor income to households. The households that 

receive the income spend some of it, devote some to income tax, and put 

some into savings. The portion of labor income that is spent on goods and 

services further stimulates the economy. The first round of induced effects 

from labor income leads to additional induced and indirect effects, which 

compound the multiplier process. 

To account for the induced effects of the type II multiplier, first calculate the 

additional number of jobs created or saved. The jobs impact is calculated 

using the industry jobs-production ratio and the change in production. The 
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number of FTE-jobs plus self-employed is the employment measure used in 

calculating the jobs impact for this report. 

Second, calculate the labor income corresponding to the change in employment. Labor income includes proprietors’ income as a return to selfemployed labor, which is included in the jobs impact. A typical method to 

calculate the change in labor income is to multiply the ratio of labor income 

to industry production by the change in industry production from the type 

1 multiplier process, just as the jobs impact is calculated. This approach is 

consistent with using industry labor income to calculate an average wage for 

industry employment (FTE-jobs plus self-employed) and multiplying this 

wage by the change in industry employment (FTE-jobs plus self-employed). 

Finally, to complete the calculations of the induced effects for the type II 

multiplier, calculate the portion of additional labor income received by 

households that is spent on goods and services. Using National Income 

and Product Accounts (NIPA) data for 2002, subtract social insurance 

taxes (11 percent) to arrive at net labor income to households, then subtract 

income taxes (12 percent) and the portion of earned income that is saved 

(2.5 percent). The remaining labor income is spent on goods and services. 

The amount spent is distributed among the goods and services consumed by 

households in proportion to the personal consumption expenditures in the 

input-output accounts. 

A type III multiplier expands the type II multiplier by including the induced 

effects from the capital income households receive, net of taxes and savings. 

In addition to the labor income (which includes proprietors’ income), households also receive income from the ownership of capital and property in the 

form of dividends, interest, and rent. These sources of capital income are 

components of industry gross operating surplus in the input-output accounts. 

NIPA data for 2002 are used to estimate that households receive 47 percent

of industry capital income (net of proprietors’ income), with the remainder 

consisting of other forms of capital income that do not go to households, such 

as retained earnings, depreciation, and profit tax. 

The capital income received by households and spent on goods and services 

is calculated in a manner similar to the treatment of labor income. The capital 

income received by households from the multiplier process is calculated by 

multiplying the change in industry production by the historical average ratio 

of industry capital income received by households to industry production. 

The portion of capital income received by households that is spent on goods 

and services is net of household income tax and savings. The same income 

tax rate and savings rates are used for both sources of income (capital and 

labor). Including the induced effects from capital income in an IOM model 

makes the type III multiplier equivalent to a SAM multiplier. 

It is important to adjust the multipliers for the share of goods and services 

that are imported so that the multipliers are for the domestic U.S. economy 

only. Imports will fulfill a share of the new demand for commodities that 

arise from the exogenous change and the multiplier process. It is assumed 

that the share of new demand fulfilled by imports equals the import share 

of domestic commodity demand in the benchmark input-output accounts. 

The accounts assume that all users (industries and households) of a specific 
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commodity purchase the same ratio of imports to domestic supplies, though 

the ratio varies by commodity. Throughout this report, multipliers will 

include this import adjustment unless noted otherwise.

Multiplier Estimates

Figure 3 compares the production and GDP multipliers from $1 billion of

SNAP benefits for the different types of multipliers. The type I multiplier 

without import adjustment is a starting point for comparing the relative 

impact of additional multiplier components. The type I GDP multiplier 

without import adjustment is 1.0, such that a $1-billion change in final

demand generates an equivalent change in GDP, while the type I production multiplier without the import adjustment is 1.88. The GDP multiplier is 

53 percent of the production multiplier, reflecting an average 55.6 percent 

ratio of GDP to production in the 2002 benchmark input-output accounts. 

This relationship between GDP and production multipliers holds for each 

type of multiplier. The 1.88 production multiplier using the 2002 benchmark 

input-output accounts is similar to the value of 1.84 reported in Hanson and

Golan (2002) using the 1992 benchmark input-output accounts. Including 

the import adjustment with the type I multipliers (second pair of columns in 

figure 3) lowers both the production and GDP multipliers by 12 percent. The 

GDP multiplier is less than one, since some of the new demand for goods and 

services is met by imports and the income (GDP or factor returns) generated 

from the production of those imports goes to foreign producers.

The type II multiplier adds the induced effects from labor income to the type 

I multiplier. In figure 3, the type II multipliers include the import adjustment. 

The production multiplier is 2.67, and the GDP type II multiplier is 1.45. The

induced effects from labor income increase the multiplier effects from the 

fiscal stimulus by 62 percent. The type III multipliers add the induced effects 

Type I without

 import adj.

Type I Type II Type III

0

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1.88

1

1.66

0.89

2.67

1.45

3.31

1.79

Production multiplier

GDP multiplier

Figure 3

IO multiplier value

Source: ERS calculations from input-output multiplier model.

Production and GDP multipliers from a $1-billion 

increase in SNAP benefits
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of capital income to the type II multipliers. The type III multipliers are 3.31 

for production and 1.79 for GDP, a 24- percent increase relative to the type II

multipliers. 

Figure 4 compares the jobs impact (employment multipliers) per $1 billion of

SNAP benefits for the type I, II, and III multipliers. The jobs impact is calculated in two ways, as FTE-jobs plus self-employed and as FTPT-jobs plus 

self-employed. These jobs impacts use average employment-to-output ratios 

by industry and the change to industry production in their calculation. The $1

billion of SNAP benefits generates a jobs impact of 9,800 FTE-jobs plus selfemployed from the direct and indirect effects of a type I multiplier without 

import adjustment. The import adjustment reduces the jobs impact by 10 

percent to 8,900 FTE-jobs plus self-employed. Adding the induced effects 

from labor income (type II multiplier) increases the employment effects to 

14,400 FTE-jobs plus self-employed, a 62-percent increase over the type I

multiplier. Adding the induced effects for capital income (type III multiplier) 

increases the jobs impact by another 24 percent to 17,900 FTE-jobs plus

self-employed. The jobs impact as FTPT-jobs plus self-employed is about 

10 percent larger than the FTE-jobs plus self-employed for each type of 

multiplier. 

Table 1 summarizes the multiplier effects on production, GDP, and jobs 

for three types of multipliers and two employment measures. Different 

types of multipliers are used for different situations (see box, “What Type 

of Multiplier To Use”). Of particular importance for analyzing the fiscal 

stimulus from government expenditures is the type III GDP multiplier. The 

value of 1.79 is comparable with the GDP multipliers generated by several 

macroeconomic models. 

Type I without

 import adj

Type I Type II Type III

0

5,000

10,000

15,000

20,000

25,000

9,80010,700

8,900 9,800

14,400

15,900

17,900

19,800

FTE jobs + self-employed

FTPT jobs + self-employed

Figure 4

Jobs

Source: ERS calculations from input-output multiplier model.

Jobs generated per $1 billion (2008 dollars) of SNAP benefits
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Both the U.S. Department of Agriculture and U.S. Department of Commerce 

use type I multipliers for the analysis of export impacts on the U.S. economy 

(Schluter and Edmondson, 1994; Davis, 1996; Edmondson, 2008; and

Tschetter, 2010). The type I multiplier with import adjustment is suited 

to determine the number of jobs associated with the production activities 

underlying U.S. exports. Tschetter (2010)—using the employment requirements table prepared by the U.S. Bureau of Labor Statistics (2009b)—estimated that 6,076 jobs (FTPT-jobs plus self-employed) were generated per $1

billion of U.S. exports in 2008, given the composition of exports that year. 

The same employment requirements table can be used with PCE (Personal 

Consumption Expenditures) data on household expenditures to find that, on 

average, 9,645 FTPT-jobs plus self-employed were generated per $1 billion

of household expenditures in 2008. This number of jobs compares well with 

the jobs impact with the type I multiplier (with import adjustment) reported 

in table 1 (9,800 FTPT-jobs plus self-employed) that is calculated by the 

FANIOM model for a similar scenario. 

The type II multiplier, with the induced effects from labor income, is typically used for regional analysis of the economic impact from an exogenous 

change in economic activity such as a military base closure (U.S. GAO, 

2005) or a production plant moving into or out of a region (U.S. Federal 

Reserve Bank of Dallas, 2004). Both IMPLAN (2010) and RIMS II (U.S.

Bureau of Economic Analysis, 2010) are IOM models designed for this type 

of analysis. A common feature of these applications is the long-term change 

in economic activity for a region. The induced effects from labor income are 

relevant since the exogenous change will affect the employment opportunity 

in the region and hence the earnings spent in the region. For a type II multiplier, IMPLAN estimates a jobs impact of about 17,000 FTPT-jobs plus selfemployed for $1 billion of household expenditures in 2008, slightly higher

than the 15,900 FTPT-jobs plus self-employed from the FANIOM model. 

Traditionally the induced effects from capital income have not been included 

in an IOM model. However, including the induced effects from capital 

income makes the type III multiplier in an IOM model equivalent to a SAM 

multiplier and comparable with a macroeconomic multiplier. Sullivan et al. 

(2004) provide an example of using a SAM multiplier, while Zandi (2008a)

provides an example of a GDP multiplier from a macroeconomic model. 

A jobs impact measured as the number of FTE-jobs plus self-employed 

and calculated using an industry average jobs-to-GDP (or production) ratio 

times the change in GDP (or production) is appropriate for assessing the 

jobs impact from a new business starting or old business closing since this 

will affect all jobs in that business. Such a jobs impact is also appropriate 

for calculating the number of jobs it takes to support the level of exports or 

household expenditures in a particular year. But this method of estimating 

the jobs impact may not be appropriate for a change in exports or household 

expenditures. How employment changes in response to a change in GDP 

(or production) may be less than what would be calculated using an average 

jobs-to-GDP (or production) ratio.

What Type of Multiplier To Use?
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Distribution of Multiplier Effects 

Among Industries 

SNAP benefits increase household food expenditures and allow recipients 

to shift some cash income from the purchase of food to the purchase of 

other goods and services. The new demand for food and nonfood goods and 

services, along with inter-industry linkages, has an impact on production, 

GDP, and employment for a number of industries, including agriculture, 

food processing, retail stores, wholesale-transportation, energy, and various 

other manufacturing and service industries. The induced effects on household 

expenditures from labor and capital income compound the multiplier effect 

across industries, while the import share reduces the impact on domestic 

producers. 

The shift of cash income from food to nonfood expenditures as households 

receive more SNAP benefits has a significant impact on how the multiplier 

effects are distributed over industries. Even though recipients spend all 

SNAP benefits on food, the receipt of SNAP benefits allows them to shift 

some of their previous cash expenditures on food to alternative uses. As a 

consensus estimate from the literature, this report assumes that, on average 

over all SNAP recipients, every dollar of SNAP benefits generates an additional 26 cents of food expenditures, with the rest spent on nonfood goods 

and services. Expenditures on food and nonfood goods and services are 

assumed to be in proportion to average expenditure shares in the personal 

consumption expenditures of the 2002 Benchmark Input-Output Accounts. 

Consider the case of a $1-billion increase in retail food expenditures where

the type I multiplier (without import adjustment) is used to estimate the 

impact of the food expenditures on agriculture. Out of the $1 billion in

food expenditures, 26.2 percent goes to retailers, 11.7 percent goes to the 

wholesale-transportation system, 56.5 percent goes to food processors, and 

5.6 percent goes directly to agriculture. Some foods such as fresh fruits 

and vegetables, tree nuts, and eggs bypass the processing industries and go 

directly from the farm through the wholesale-transportation system into retail 

outlets. Food processors purchase the bulk of agricultural commodities for 

processing into the foods we eat.5 Another source of indirect effects of food 

expenditures on agriculture is the use of agricultural commodities in the 

production of other agricultural commodities, such as feed grains for animal 

and dairy production. Given the direct and indirect effects, the $1 billion of

retail food expenditures generates $267 million of agricultural production,

5The processing of agricultural 

commodities into the foods that we eat 

involves a complex network of industries. One set of industries purchases 

agricultural commodities and processes 

them into basic food products, such as 

flour from wheat, while another set of 

industries uses the basic foods to make 

more highly processed foods such as 

bread and the many prepared foods that 

we eat.

Table 1

Production, GDP, and job multipliers from a $1-billion increase in 

SNAP benefits

Production GDP FTE-jobs FTPT-jobs

Type I without import adjustment 1.88 1.00 9,800 10,700

Type I 1.66 0.89 8,900 9,800

Type II 2.67 1.45 14,400 15,900

Type III 3.31 1.79 17,900 19,800

Source: ERS calculations.
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$87 million of agricultural GDP or value added, and close to 3,000 agricultural jobs (FTE-jobs plus self-employed).

Using the type I multiplier model adjusted for imports, the impact of $1

billion in food expenditures on domestic agricultural production, GDP, and 

employment is reduced by about 16 percent. The $73.4-million increase in

agricultural GDP with the import adjustment is distributed between livestock 

(44 percent) and crop production (56 percent). The GDP impact on livestock

is distributed among dairy (14.2 percent), poultry (7.8 percent), and cattle

plus other animals (22 percent). The GDP impact on crops is distributed 

among grains (12.4 percent), fruits and vegetables (30.8 percent), and other

crops (12.8 percent). 

Now consider a $1-billion increase in SNAP benefits, which will increase

retail food expenditures by $260 million (26 percent) and expenditures on

nonfood goods and services by $740 million due to the shift of cash income

from food to nonfood. Using the type I multiplier with import adjustment, 

agriculture receives $68 million in cash receipts from the sales of agricultural

commodities (production). The additional sales lead to $23.5 million of agricultural value added or GDP, and close to 765 agricultural jobs (FTE-jobs 

plus self-employed). The increase in agricultural GDP is distributed between 

livestock (38 percent) and crop production (62 percent). The GDP impact on 

livestock is distributed among dairy (11.4 percent), poultry (6.1 percent), and

cattle plus other animals (20.8 percent). The GDP impact on crops is distributed among grains (11.3 percent), fruits and vegetables (23.2 percent), and 

other crops (27.2 percent).

Finally, consider the case of a $1-billion increase in SNAP benefits with the

type III multiplier with import adjustment. Given the direct, indirect, and 

induced effects from labor and capital income, the $1 billion of SNAP benefits generates $92.6 million of agricultural production, $32.3 million of agricultural GDP or value added, and close to 1,000 agricultural jobs (FTE-jobs 

plus self-employed).6 The increase in agricultural GDP is distributed between 

livestock (38 percent) and crop production (62 percent). The GDP impact on 

livestock is distributed among dairy (11.4 percent), poultry (6.0 percent), and

cattle plus other animals (20.6 percent). The GDP impact on crops is distributed among grains (11.4 percent), fruits and vegetables (22.3 percent), and

other crops (28.3 percent). Most of the increase in GDP goes to the service 

industries (67.4 percent), while agriculture receives 1.8 percent; food processors, 2.8 percent; energy sectors, 3.2 percent; nonfood manufacturing, 7.6 

percent; retail trade, 9.8 percent; and wholesale-transportation, 7.4 percent.

6With the type II multiplier (including import adjustment), the $1 billion

of SNAP benefits generates $83.1 million of agricultural production, $28.9

million of agricultural GDP, and 938 

agricultural jobs (FTE-jobs plus selfemployed). The distribution of these 

impacts among agricultural sectors is 

identical to the distribution for the type 

III multiplier.
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Comparing and Reconciling Multipliers 

With Macroeconomic and IOM Models

This section first compares the multiplier effects from an IOM model with 

those from several macro-economic models. It then discusses how to reconcile the jobs impact from an IOM model with those derived using the method 

recommended by the U.S. Executive Office of the President, Council of 

Economic Advisors (2009; CEA in subsequent citations). 

Comparison of Multipliers from 

Alternative Macroeconomic Models

There is considerable debate on the appropriate macroeconomic model for 

analyzing the multiplier effects from a countercyclical fiscal policy. The 

effect of fiscal policy on real economic activity (real GDP) is sensitive to 

model assumptions regarding household behavior (myopic vs. forwardlooking), market adjustment to disturbances, and monetary policy. Several 

studies present multipliers from alternative macroeconomic models that 

have contributed to the debate on expected multiplier effects of government 

expenditures funded by the ARRA during the 2008 recession. 

Romer and Bernstein (2009) estimate GDP multipliers for an increase in 

government purchases and a decrease in taxes using the Federal Reserve 

FRB/US model and a model from a leading private forecaster. They assume 

an accommodative monetary policy in which “the federal funds rate remains 

constant rather than increasing in response to the fiscal expansion, on the 

grounds that the funds rate is likely to be at or near its lower bound of zero 

for the foreseeable future” (p. 12). Their analysis finds that an increase in 

government purchases results in a GDP multiplier of 1.56, and a tax cut 

results in a multiplier of 1. They state that these multipliers “represent a 

consensus among economists and professional forecasters” (p. 3). 

Zandi (2008b) used a macroeconomic model to analyze the GDP multiplier

from various spending and tax proposals considered for the 2009 stimulus 

package. The model is specified so monetary policy is accommodating, and 

government borrowing has little or no crowding out effects (Zandi, 2008a;

2009). Simulations of the model result in GDP multipliers of 1.73 for SNAP 

benefits, 1.63 for unemployment insurance benefits, 1.38 for general aid to 

States, and 1.59 for infrastructure spending. A weighted average of these 

GDP multipliers is 1.50 using weights from CBO (2009a) budget estimates 

for ARRA expenditures. The weighted-average multiplier for government 

expenditures is close to the Romer and Bernstein GDP multiplier of 1.56 for 

government purchases, and Zandi’s GDP multiplier of 1.73 for SNAP

benefits corresponds to the 1.79 GDP multiplier (type III) from the 

FANIOM model. 

Though these two studies dominated discussions among Congress and the 

Obama Administration about the expected impact of ARRA expenditures, 

other analyses illustrate the range of GDP multiplier effects generated by 

macroeconomic models (see table 2 and appendix of CBO, 2010). For 

instance, Cogan et al. (2009) contend that the GDP multiplier is less than 

1 while Hall (2009) suggests that it can rise to 1.72. Hall’s analysis illus-
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trates how interest rates near the zero bound allow a fiscal stimulus to occur 

without crowding out private sector investment. Cogan’s analysis illustrates 

that multiplier effects are significantly reduced by model assumptions that 

households are forward-looking with perfect foresight and that unemployment as a labor market adjustment problem does not exist. 

Reconciling FANIOM Jobs Impact 

Estimates with CEA Estimates 

Romer and Bernstein (2009) is the basis for the Council of Economic 

Advisors (CEA)-recommended method for estimating the jobs impact 

from government expenditures funded through the American Recovery and 

Reinvestment Act of 2009 (CEA, 2009). 

There are four steps in the CEA method to estimate the jobs impact from 

government expenditures: 

1. Start with a GDP multiplier of 1.56 for all types of government 

expenditures, including spending on goods and services, transfers 

to States, and transfer payments to households. The multiplier is 

derived from simulation experiments with several macroeconomic 

models.

2. Assume that a 1-percent increase in GDP will increase employment by 

1 million jobs. CEA states that this is a “conservative rule of thumb” 

that allows for higher productivity as a means by which GDP rises in 

response to a fiscal stimulus (Romer and Bernstein, 2009, p. 3).

3.	 Calculate that a $100-billion increase in government spending

creates 1,085,355 job-years. Derive the jobs impact by applying a 

1.56 GDP multiplier to the $100-billion increase in spending to get

a $156 billion increase in GDP, which is about 1.085355 percent

of GDP in 2008 ($14,373 billion, prior to revisions). At 1 million

jobs per 1-percent increase in GDP, the 1.085355-percent increase 

in GDP results in the 1,085,355 jobs from a $100 billion increase in

spending. 

4.	 Divide the $100-billion increase in government spending by the

1,085,355 jobs to get the CEA rule of thumb that a $92,136 increase

in government spending creates 1 job. 

The CEA-estimated jobs impact, based on macroeconomic analysis, is 

less than the jobs impact from the FANIOM model based on input-output 

analysis. By the CEA method, $1 billion of SNAP benefits generates 10,854

jobs (divide 1,085,355 jobs by $100 billion). With the FANIOM type III

multiplier, the jobs impact from $1 billion of SNAP benefits is 17,900

FTE-jobs plus self-employed (table 1). The jobs impact from the FANIOM 

model is 65 percent larger than the CEA jobs impact. It is not clear what job 

measure CEA uses (FTE-jobs or FTPT-jobs) and whether it includes the selfemployed, but the more comprehensive jobs measure is the FTE-jobs plus 

self-employed. The FANIOM type II jobs impact is 33 percent (FTE jobs) 

larger than the CEA estimate, while the type I jobs impact is 18 percent 
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(FTE jobs) smaller than the CEA jobs impact estimate. The type III multiplier is most similar to the multiplier process in a macroeconomic model 

where consumption depends on income derived from all sources, labor, and 

capital. The difference in the jobs impact from these two types of models is 

significant enough that it needs some explanation and discussion as to how 

the estimates might be reconciled. 

The difference in the jobs impact estimates is from differences in (1) the 

magnitude of the GDP multiplier and (2) the jobs-to-GDP (production) ratio. 

First, the 1.79 type III GDP multiplier is 15 percent more than the CEA 1.56 

multiplier. The CEA multiplier was derived for government expenditures 

in general, whereas the FANIOM multiplier is for SNAP benefits, which 

are likely to be larger since SNAP recipients tend to spend all the benefits 

quickly. Multiplier estimates for other types of government expenditures 

from Zandi (2008a) support a larger multiplier for SNAP. Increasing the

CEA multiplier from 1.56 to 1.79 would increase the CEA jobs impact to 

12,500 jobs per $1 billion of SNAP benefits, a 15-percent increase. This

reduces the gap between jobs-impact estimates from 65 percent to 43 percent.

Differences in household saving and tax rates between models could also 

contribute to the difference in multipliers, but these are difficult to check 

without detailed information about the macroeconomic model (which is not 

readily available). Features of a macroeconomic model that do not affect an 

IOM model could also reduce the GDP multiplier effects relative to the IOM 

multipliers. These offsets include price and interest rate effects. But, given 

the assumptions about underemployed resources and accommodating monetary policy to hold interest rates low, the multipliers from Zandi and Romer/

Bernstein are unlikely to be reduced by the offsets. 

A second reason for the difference in estimated jobs impact is that the 

change in number of jobs corresponding to a change in GDP is smaller in 

the CEA analysis than with the FANIOM model.7 The CEA analysis starts 

with the assumption that 1 million jobs are generated from a 1-percent 

increase in GDP. The FANIOM type III multiplier results in 1.44 million

more jobs (FTE-jobs plus self-employed) from a 1-percent increase in GDP, 

a 44-percent larger jobs impact. Though CEA does not document how they

arrived at 1 million jobs per 1-percent increase in GDP, empirical analysis 

of the historical relationship between changes in the number of jobs relative 

to a change in GDP undoubtedly underlies this assumed value. CBO (2009c, 

2009d) reports a similar ratio between changes in GDP and jobs. They use 

an empirical estimate of “Okun’s Law” (Knotek, 2007) to calculate a change 

in the unemployment rate given a macro forecast on GDP, and then derive a 

change to employment from the change in the unemployment rate. 

To approximate the ratio of change in the number of jobs to a 1-percent 

change in GDP, figure 5 displays the quarter-to-quarter change in the 

number of employees (BLS, Current Employment Statistics, nonfarm payroll 

employees in thousands) as a ratio to the percent change in real GDP from 

1979 to 2009. An average of these ratios is one estimate of the change in 

jobs as a ratio to the change in GDP. For the first quarter of 1979 (1979-1) 

through 2009-1, the average ratio is 710,000 jobs per 1-percent change 

in GDP.8 If the quarters 2000-3 to 2003-2 are excluded, the average ratio 

becomes 851,000 jobs per 1-percent change in GDP. The 2000-3 to 2003-2 

time period includes the 2001 recession and a period of unusually slow job 

7IOM models use an average jobsto-production ratio to calculate the jobs 

impact from a production multiplier. 

The IOM model also calculates the 

change in GDP so it is possible to 

calculate a ratio of the change in jobs 

to a change in GDP. A difference in the 

employment measure for the number of 

jobs could contribute to the difference 

in the jobs impact, but it is not clear 

since the CEA does not define how employment is measured. The difference is 

likely to be 10 percent or less.

8Extreme values for three quarters 

are excluded (1990-3, 2007-1, and 

2007-4). The employment measure

does not include agricultural labor and 

self-employed, so it undercounts the 

change somewhat.
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recovery following the recession (Groshen and Potter, 2003). Over a shorter 

and more recent timeframe of 1992-1 to 2009-1, the average ratio is 973,000 

jobs per 1-percent change in GDP when excluding the 2000-3 to 2003-2 

period, or 703,000 jobs when including it.

An analysis of quarterly data suggests that the historical relationship between 

change in jobs and change in GDP is comparable to or somewhat less than 

1 million jobs per 1-percent change in GDP. This supports the CEA jobs 

impact relative to the larger jobs impact with the type III multiplier from the 

FANIOM model. The empirical analysis calls into question the use of an 

average jobs-to-production ratio to calculate the jobs impact from a change in 

production, as conducted in the FANIOM and other IOM models. One explanation for a smaller jobs impact is that an increase in production activity by 

existing businesses will increase the number of production-workers only 

since nonproduction workers such as managers and support staff like accountants are already working and will not be affected by the increased production activity (see appendix 1 for a definition of production-workers). This is 

a different situation than a new business hiring all types of new workers, and 

can explain how a change in jobs to a change in GDP (production) is smaller 

than an average jobs-to-GDP (production) ratio.9

Exploratory analysis with the FANIOM model modifies the jobs impact 

estimate by allowing only production workers plus the self-employed to 

respond, which leads to smaller induced effects from earnings by a smaller 

number of new workers, and smaller type II and III multipliers. The increase 

in production workers is calculated, as with other employment measures, 

by multiplying the change in industry production by the industry ratio of 

production workers plus self-employed to production. The increase in labor 

9Tschetter (2010) uses an average 

jobs-to-production ratio to calculate 

the number of jobs that support U.S. 

exports and distinguishes the estimate 

from what would be the number of jobs 

associated with a change in exports.

1979−I

1979−I

V

1980−III

1981−II

1982−I

1982−I

V

1983−III

1984−II

1985−I

1985−I

V

1986−III

1987−II

1988−I

1988−I

V

1989−III

1990−II

1991−I

1991−I

V

1992−III

1993−II

1994−I

1994−I

V

1995−III

1996−II

1997−I

1997−I

V

1998−III

1999−II

2000−I

2000−I

V

2001−III

2002−II

2003−I

2003−I

V

2004−III

2005−II

2006−I

2006−I

V

2007−III

2008−II

2009−I

−6,000

−4,000

−2,000

0

2,000

4,000

6,000

Figure 5

Jobs per 1-percent change in GDP

Source: ERS calculations from BEA-NIPA data on GDP and BLS data on CES employment.

Quarterly change in employment per 1-percent change in real GDP, 1979-2009
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income is calculated by multiplying the increase in production workers plus 

self-employed by an average industry wage, which is set by dividing total 

industry labor income (including proprietors’ income) by total industry 

employment (FTE-jobs plus self-employed). 

The type II production and GDP multipliers are 2.36 and 1.12 when only 

production workers plus self-employed adjust, down from 2.67 and 1.45

when all jobs adjust (see table 1, FTE plus self-employed). The type III 

production and GDP multipliers are 2.84 and 1.34 respectively, down from

3.31 and 1.79 when all jobs adjust. For the type II multiplier, the jobs impact 

is 10,400 production workers plus self-employed per $1-billion increase in

SNAP benefits, while the jobs impact for the type III multiplier is 12,500 

production workers plus self-employed per $1 billion of SNAP benefits. This

jobs impact is only 15 percent larger than the CEA jobs impact estimate of 

10,854 jobs per $1 billion of government expenditures, while the jobs impact

from the type II multiplier is essentially equal. But now the type III GDP 

multiplier is about 15 percent less than the CEA multiplier (table 2). 

Table 2 summarizes the estimated GDP and jobs impacts for $1 billion of

SNAP benefits and for the CBO (2009a,b,d)-estimated increase in SNAP 

benefits in 2009 (see table in box, “SNAP Response to the 2008 Recession”). 

The total increase in SNAP benefits is derived both from the increase in caseloads and from the 13.6-percent increase in the maximum benefit amount. 

The first two columns in table 2 are the GDP and jobs impact from the 

FANIOM model with the type III multiplier and full jobs adjustment. The 

fifth and sixth columns are the GDP and jobs impact using the CEA method. 

Relative to the CEA method, the type III multiplier from the FANIOM 

model has a 15 percent larger GDP multiplier and a 65 percent larger jobs 

impact. The third and fourth columns are the GDP and jobs impact from the 

FANIOM model when only production workers plus self-employed adjust. 

In this case the jobs impact from the FANIOM model is 15 percent greater 

than the CEA jobs impact, but consistent with a CEA jobs impact if the 

GDP multiplier were 15 percent larger (increased from 1.56 to 1.79). From 

the perspective of consistent results, the GDP multiplier from the FANIOM 

model falls to 1.34 when only production workers plus self-employed adjust.

Table 2

Economic effects from the estimated increase in SNAP benefits for 2009

FANIOM FANIOM

CEA

FTE jobs + selfemployment impactProd-jobs + selfemployment impact

GDP, $ bil # jobs GDP, $ bil # jobs GDP, $ bil # jobs

From $1 billion of SNAP benefits 1.79 17,900 1.34 12,500 1.56 10,854

From total increase in SNAP benefits 27.642 275,958 20.658 192,708 24.050 167,332

From caseload increase 19.014 189,823 14.210 132,558 16.543 115,103

From maximum benefit adjustment 8.628 86,135 6.448 60,150 7.507 52,229

Source: ERS calculations.
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Limitations of an IOM Model

The underlying assumptions of an IOM model impose limitations on using 

the model to analyze the economic effects from a fiscal stimulus. Limitations 

include those due to the IOM model’s linear structure and use of average 

coefficients. Also, an IOM model does not allow for price effects, interest 

rate effects, or several other economic adjustments that could arise in 

response to a fiscal stimulus and reduce the multiplier effects relative to 

those from an IOM model. These economic adjustments to a fiscal stimulus 

are designed into macroeconomic models. Depending on economic circumstances and specification of these adjustments in a macroeconomic model, 

these models can result in multipliers that are as large as the GDP type III 

multiplier from an IOM model (offsetting factors have little to no effect 

on the multiplier), or they can result in multipliers that are zero because 

economic adjustments fully offset the multiplier effects from an IOM model. 

Linear Structure of the IOM Model and Fixed Prices

The linear structure of an IOM model results in the same multiplier for any 

magnitude of exogenous change to a specific component of final demand. So, 

for instance, the linear structure of the model allows the 1.79 type III GDP 

multiplier calculated from a $1-billion change in SNAP benefits to be used to

estimate the GDP impact from a change in SNAP benefits of any magnitude. 

Of course, this cannot be true for all magnitudes of change in SNAP benefits, 

for at some magnitude of change the underlying assumptions of the model 

will be violated. For instance, an IOM model assumes that the exogenous 

change in demand affects the demand and supply of goods and services, and 

not prices. For this to occur, the resources for increasing production—namely 

labor and production capacity— must be available. If resource constraints 

exist, then the increase in demand will cause prices to increase, reducing the 

multiplier effects on production, GDP, and employment. 

Key economic conditions of an economic downturn include high unemployment, low utilization of production capacity, and low inventory levels. Under 

these conditions, an increased demand for goods and services from government expenditures will stimulate production rather than simply causing pressure on prices or a rundown of inventories. They are the economic conditions 

for which an IOM model is appropriate for calculating the multiplier effects 

of government expenditures. Excess capacity and high unemployment during 

the 2008 recession suggest that the economy can absorb the new demand 

from the fiscal stimulus package with little or no price effects. Inventory 

reduction since mid-2008 suggests that new demand for goods will stimulate 

new production activity. 

Use of Average Coefficients 

From Base-Year Data

The parameters of the linear IOM model are calibrated with data from the 

input-output accounts, NIPA, and employment measures. The coefficients are 

average values from data in a base year, but they are used to determine the 

change to an endogenous variable in response to a change in the exogenous 

variable, which is a marginal change. Ideally, marginal coefficients should be 
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used in the model, but in most cases either the average coefficient is a good 

approximation to a marginal coefficient or there is not enough information 

to reliably estimate a marginal coefficient. Still, there are situations where 

the distinction between an average and marginal coefficient is important and 

it may be preferable to replace an average coefficient in the model with a 

marginal coefficient. An example is the use of average jobs-to-production 

ratios in estimating the jobs impact from an increase in economic activity 

(production or GDP). When an IOM model is used to assess the jobs impact 

from a business startup, the business can be expected to hire an industry 

average number of jobs per unit of production. If a business is already in 

operation and receiving additional orders, a marginal jobs coefficient may be 

more appropriate. It may not be necessary to hire as many new workers as 

are used on average, since occupations such as managers and support staff 

may not take on additional work. One way to approximate a marginal jobs 

coefficient is to allow an expansion of production workers only as production 

increases.

Household consumption coefficients as well as tax and savings rates are other 

examples where average coefficients could be improved upon with marginal 

coefficients if available. Average consumption coefficients are the share of 

total household expenditures spent on each good and service in the personal 

consumption expenditures data of the input-output accounts. Average tax and 

savings rates are calculated from NIPA data on how much households save 

or pay as tax during the year relative to average household income during the 

year. For an increase in income or SNAP benefits, the change to consumption, savings, and taxes may be more or less than the average. Specification 

of marginal consumption, tax, and savings rates from empirical studies of 

household behavior is worth pursuing, but beyond the scope 

of this report.10

Budget Deficits, Interest 

Rate Effects, and Monetary Policy

Government borrowing to finance fiscal stimulus expenditures may compete 

with private sector borrowing on financial markets. This can put upward 

pressure on interest rates, which can reduce business investment and interestsensitive household purchases such as homes and durable goods. This 

financial market effect from deficit spending is referred to as crowding out, 

and may partially offset the multiplier effect of the fiscal stimulus. At issue 

is how much interest rates will rise in response to government borrowing, 

and how much business investment and household expenditures will fall in 

response to the rise in interest rates (Blinder and Solow, 1973; IMF, 2002). 

The CBO (2005) summarized the literature to find that a “sustained increase 

in the federal deficit amounting to 1 percent of GDP raises interest rates 

by roughly 20 to 60 basis points, with the weight of the evidence around 

30 basis points,” concluding that, “Overall, the effects of federal deficits 

on interest rates are small” (p. 4). At the same time, there is a need for a

commitment to medium-term fiscal sustainability to accompany any shortterm fiscal stimulus (IMF, 2008). If the fiscal stimulus is seen by markets as 

compromising fiscal sustainability, it can lead to rising real interest rates. 

10A related model extension is to 

disaggregate households into different 

types of households, where expenditure 

patterns and the distribution of labor 

and capital income from the induced 

effects may differ by household type.
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As for the impact of interest rates on investment, empirical evidence suggests 

that business investment is determined more by expected output growth 

than by user cost of capital (Chirinko, 1993; 1999). Furthermore, when the 

economy is in recession, interest rates are low, so an increase in interest 

rates will not be the most significant factor in the business investment decision. Household demand for housing is highly sensitive to interest rates, and 

normally it would fall due to higher interest rates. Zandi (2008a) suggests

that, during the 2008 recession, the interest rate effect on housing demand 

was muted given other housing market problems. 

Overall, the empirical evidence suggests that crowding out during a recession 

is not significant. Still, as the government’s accumulated debt rises, it can and 

will eventually raise interest rates. It is important that the government maintain a longrun plan for debt management.

Other features of the economy can eliminate or at least reduce crowding out 

of investment, as when the Federal Reserve uses an accommodative monetary policy that holds interest rates steady while the government borrows to 

finance deficit spending. This was done during the 2008 recession. Crowding 

out is also a non-issue when interest rates are near zero and the excess funds 

in the financial system are not being used by private businesses. Under 

this circumstance, government borrowing to finance deficit spending will 

not cause interest rates to increase enough to affect investment (Blinder, 

2006; Krugman, 2005; Feldstein, 2009). Zandi (2009) suggests that Federal

borrowing to finance the stimulus will not lead to excessively higher longterm rates while private bond issuance is depressed, as in the 2008 recession. 

Household Savings 

Government expenditures as a fiscal stimulus during an economic downturn 

are funded through government borrowing or deficit spending. At some point 

in the future, the borrowed funds need to be repaid. Higher income households may expect taxes to be raised in the future to reduce the deficit. And 

some of these households may increase savings and reduce spending when 

the government is trying to stimulate the economy with deficit spending, 

which will reduce the multiplier effects from the fiscal stimulus. These 

households are considered to be forward-looking with rational expectations 

and make current consumption-savings decisions in the context of permanent 

income, or the discounted present value of lifetime income. Other households, for reasons such as myopic expectations and liquidity constraints, 

make current consumption-savings decisions in the context of current disposable income. They do not reduce current consumption in anticipation of 

higher taxes, which allows the multiplier effects from the fiscal stimulus 

to occur.

How households form expectations and make consumption and savings 

decisions are important features of a macroeconomic model and significantly affect the multiplier from a fiscal stimulus (Bernheim, 1989). The 

bulk of empirical research finds that household consumption is more sensitive to current income than the permanent income hypothesis would imply 

(Elmendorf and Mankiw, 1999; Blinder, 2006; Gale and Orszag, 2004).

Other studies support the notion of forward-looking households that are 
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free of liquidity constraints (Taylor, 2009). Still, most empirical evidence 

suggests that the effect of deficit spending on savings and consumption will 

not significantly reduce the multiplier effects. 
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Conclusion 

The Food Assistance National Input-Output Multiplier (FANIOM) model 

can be used to provide a systematic exploration of the multiplier effects on 

economic activity from an exogenous change in final demand. Different types 

of multipliers (types I, II, and III) can be estimated for various economic 

variables (e.g., production, GDP, and jobs). This allows the modeler to 

use the multipliers that are appropriate to the issue being addressed and to 

compare them to ones calculated by other IOM models and macroeconomic 

models. 

SNAP benefits are a fiscal stimulus whose effects on the economy can be 

measured with the FANIOM model as multiplier effects. For SNAP benefits, 

the FANIOM GDP Type III multiplier of 1.79 is an appropriate update for 

the 1.84 multiplier reported in Hanson and Golan (2002), which is often cited

as $9.20 (or $9) of economic activity generated from $5 of SNAP benefits.

Though the two multipliers are different—one is a type III multiplier for 

GDP (1.79) and the other is a type I multiplier for production (1.84) —they

each express an overall impact on economic activity. The type III GDP 

multiplier is a comprehensive multiplier that accounts for the direct, indirect, 

and induced effects from labor and capital income, and is equivalent to a 

GDP multiplier from macroeconomic models. The Hanson and Golan (2002) 

multiplier of 1.84 should be replaced with the new FANIOM model result,

such that $1.80 of economic activity (GDP) is generated from $1 of SNAP

benefits (rounding off). Essentially, the multiplier estimates are the same, but 

based on an improved model that uses more recent data. 

The GDP multiplier of 1.79 is close to that estimated for SNAP benefits by 

Zandi (2008a) using a macroeconomic model (1.73). Though larger than the

1.56 multiplier estimated by Romer and Bernstein (2009) for government 

expenditures, the new GDP multiplier is not inconsistent with it when taking 

into account the multiplier effects from different types of government expenditures. A weighted average of multipliers from Zandi (2008a) is 1.50, where

the SNAP multiplier is 1.73 and the multipliers for other types of government 

expenditures are lower. The GDP multiplier from the FANIOM model reconciles well with the multipliers from these analyses using macroeconomic 

models.

Jobs impact estimates from the FANIOM model range from 9,000 to 18,000 

FTE-jobs plus self-employed per $1 billion of SNAP benefits, with the

range varying by type of multiplier. Ideally, the larger jobs impact (17,900 

in table 1) would be used in analysis that uses the corresponding 1.79 type 

III GDP multiplier. This jobs estimate of 17,900 corresponds to 1.44 million

jobs per 1-percent change in GDP, but empirical analysis of quarterly GDP 

and employment data suggests that between 700,000 and 1 million jobs are 

generated per 1-percent change in GDP. This range is consistent with the 

findings from several macroeconomic models that have been used to estimate the jobs impact from ARRA expenditures (U.S. EOP, CEA, 2010, p. 

26, table 8). Romer and Bernstein (2009) assume 1 million jobs are generated from a 1-percent change in GDP, which amounts to 10,850 jobs per $1

billion of government expenditures given their 1.56 GDP multiplier. The 

higher jobs impact from an IOM model is an issue that needs to be resolved 

if the type III multiplier is to be used to assess the jobs impact from ARRA 
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expenditures. Until that issue is resolved, it seems most appropriate to use the 

jobs impact from the type I multiplier: 9,000 FTE-jobs plus self-employed or 

10,000 FTPT-jobs plus self-employed per $1 billion of SNAP benefits. These

jobs impact estimates are more consistent with those estimated using the 

CEA-recommended method. 

In an IOM model such as FANIOM, the jobs impact is calculated by multiplying the average jobs-to-production ratio by the change in production that 

occurs in response to the fiscal stimulus. This method of calculating the jobs 

impact assumes that new workers are hired for all occupations in a business in proportion to average industry employment. Another possibility is 

that only additional production-workers are hired as production increases. 

Additional nonproduction workers such as managers and support staff (e.g., 

accountants) may not be hired as production increases. 

As an experiment, the FANIOM model was modified to allow only production-workers plus self-employed to adjust in response to a fiscal stimulus. 

In this case, the jobs impact for the type III multiplier is 12,500 jobs for 

$1 billion of SNAP benefits. This jobs impact estimate is only 15 percent

larger than the CEA estimate of 10,850 jobs. Given the smaller jobs impact, 

the induced effect from labor income is smaller, reducing the type III GDP 

multiplier to 1.34, which is about 15 percent less than the GDP multiplier

of 1.56 estimated by Romer and Bernstein (2009). This experiment shows 

promise and merits further exploration.
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References

Bernheim, B. Douglas. 1989. “A Neoclassical Perspective on Budget 

Deficits,” Journal of Economic Perspectives 3(2), Spring, 55-72.

Blinder, Alan S. 2006. “The Case Against the Case Against Discretionary 

Fiscal Policy,” in The Macroeconomics of Fiscal Policy, R. Kopcke, G. 

Tootell, and R. Triest (eds.), MIT Press. 

Blinder, Alan S., and Robert M. Solow. 1973. “Does Fiscal Policy Matter?” 

Journal of Public Economics, 2, 319-37.

Breunig, Robert, and Indraneel Dasgupta. 2005. “Do Intra-Household Effects 

Generate the Food Stamp Cash-Out Puzzle?” American Journal of 

Agricultural Economics 87(3), August, 552-68.

Brooke, Anthony, David Kendrick, and Alexander Meeraus. 1992. GAMS: A 

User’s Guide. San Francisco, CA: Scientific Press. 

Chirinko, Robert S. 1993. “Business Fixed Investment: A Critical Survey of 

Modeling Strategies, Empirical Results, and Policy Implications,” Journal 

of Economic Literature 31, 1875-1911. 

Chirinko, Robert S., Steven M. Fazzari, and Andrew P. Meyer. 1999. 

“How Responsive is Business Capital Formation to its User Cost? An 

Exploration with Micro Data,” Journal of Public Economics, 74, 53-80.

Clark, Colin. 1938. “Determination of the Multiplier from National Income 

Statistics,” Economic Journal 48(191), Sept., 435-48.

Cogan, John, Tobias Cwik, John Taylor, and Volker Wieland. 2009. “New 

Keynesian versus Old Keynesian Government Spending Multipliers.” 

NBER Working Paper 14782.

Congressional Budget Office (CBO). 2005. “Long-Term Economic Effects 

of Chronically Large Federal Deficits.” http://www.cbo.gov/ftpdocs/67xx/

doc6744/10-13-Long-TermEffects_Brief.pdf

Congressional Budget Office (CBO). 2009a. “The Budget and Economic 

Outlook: Fiscal Years 2009 to 2019.” http://www.cbo.gov/ftpdocs/99xx/

doc9957/01-07-Outlook.pdf

Congressional Budget Office (CBO). 2009b. “Supplemental Nutrition 

Assistance Program (SNAP) – formerly known as Food Stamps, CBO 

Baseline January 2009.” 605 Interested Parties.xls, 1/6/2009. http://www.

cbo.gov/budget/factsheets/2009/foodstamps.pdf

Congressional Budget Office (CBO). 2009c. “Estimated Macroeconomic 

Effects of the Inouye-Baucus Amendment.” http://cbo.gov/ftpdocs/96xx/

doc9619/Gregg.pdf
32

The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

Congressional Budget Office (CBO). 2009d. “Estimated Macroeconomic 

Impacts of the American Recovery and Reinvestment Act of 2009.” 

http://www.cbo.gov/ftpdocs/100xx/doc10008/03-02-Macro_Effects_of_

ARRA.pdf

Congressional Budget Office (CBO). 2010. “Estimated Impact of the 

American Recovery and Reinvestment Act on Employment and Economic 

Output from October 2009 through December 2009.” http://cbo.gov/

ftpdocs/110xx/doc11044/02-23-ARRA.pdf

Davis, Lester. 1996. “U.S. Jobs Supported by Goods and Services Exports, 

1983-94.” U.S. Department of Commerce, Economics and Statistics

Administration, Research Series OIMA-1-96. 

Defourny, Jacques, and Erik Thorbecke. 1984. “Structural Path Analysis

and Multiplier Decomposition within a Social Accounting Matrix 

Framework,” Economic Journal 94, March, 111-36.

Diebold, Francis X. 1998. “The Past, Present, and Future of Macroeconomic 

Forecasting,” Journal of Economic Perspectives 12(2), Spring, 175-92.

Edmondson, William. 2008. “U.S. Agricultural Trade Boosts Overall 

Economy.” U.S. Department of Agriculture, Economic Research Service, 

Outlook Report No. FAU-124. http://www.ers.usda.gov/Publications/

FAU/2008/04Apr/FAU124/

Elmendorf, Douglas W., and N. Gregory Mankiw. 1999. “Government 

Debt,” in Handbook of Macroeconomics, Vol. 1, J.B. Taylor and M. 

Woodford (eds.), NY: Elsevier.

Feldstein, Martin. 2009. “Rethinking the Role of Fiscal Policy,” American 

Economic Review 99(2), May, 556-559.

Fox, Mary Kay, William Hamilton, and Biing-Hwan Lin. 2004. “Effects of

Food Assistance Programs on Nutrition and Health. Volume 3, Literature 

Review.” U.S. Department of Agriculture, Economic Research Service, 

FANRR-19-3. http://www.ers.usda.gov/Publications/fanrr19-3/

Fraker, Thomas M. 1990. The Effects of Food Stamps on Food Consumption: 

A Review of the Literature. Prepared by Mathematica Policy Research, 

Inc., for U.S. Department of Agriculture, Food and Nutrition Service. 

Gale, William G., and Peter R. Orszag. 2004. “Budget Deficits, National

Saving, and Interest Rates,” Brookings Papers on Economic Activity 2, 

101-187.

Groshen, Erica L., and Simon Potter. 2003. “Has Structural Change 

Contributed to a Jobless Recovery?” Federal Reserve Bank of New York, 

Current Issues in Economics and Finance, 9(8).

Hall, Robert. 2009. “By How Much Does GDP Rise if the Government Buys 

More Output?” NBER Working Paper 15496.
33

The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

Hansen, Alvin. 1951. Business Cycles and National Income. New York: 

W.W. Norton & Co.

Hanson, Kenneth. 2003. “Food Assistance Research Brief: Importance 

of Child Nutrition Programs to Agriculture.” U.S. Department of 

Agriculture, Economic Research Service, FANRR 34-12. http://ers.usda.

gov/publications/fanrr34/fanrr34-12

Hanson, Kenneth, and Elise Golan. 2002. “Issues in Food Assistance: Effects 

of Changes in the Food Stamp Expenditures across the U.S. Economy.” 

U.S. Department of Agriculture, Economic Research Service, FANRR 

26-6. http://www.ers.usda.gov/publications/fanrr26/fanrr26-6/

Hanson, Kenneth, and Victor Oliveira. 2009. Economic Linkages between 

the WIC Program and the Farm Sector. U.S. Department of Agriculture, 

Economic Research Service, EB-12. http://www.ers.usda.gov/

Publications/EB12/

 Hewings, Geoffrey, J.D. 1985. Regional Input-Output Analysis. London: 

Sage Publications.

Holland, David, and Peter Wyeth. 1993. “SAM Multipliers: Their 

Interpretation and Relationship to Input-Output Multipliers,” in 

Microcomputer-Based Input-Output Modeling, Daniel M. Otto and 

Thomas G. Johnson (eds.), Boulder, CO: Westview Press.

Horowitz, Karen J., and Mark A. Planting. 2006. Concepts and Methods of 

the Input-Output Accounts. U.S. Department of Commerce, Bureau of 

Economic Analysis. Revised April 2009. http://www.bea.gov/papers/pdf/

IOmanual_092906.pdf

International Monetary Fund (IMF). 2002. “The Effectiveness of Fiscal 

Policy in Stimulating Economic Activity—A Review of the Literature.” 

Working Paper, WP/02/208. http://www.imf.org/external/pubs/cat/

longres.cfm?sk=16198.0

International Monetary Fund (IMF). 2008. World Economic Outlook. 

Chapter 5. Fiscal policy as a countercyclical tool. http://www.imf.org/

external/pubs/ft/weo/2008/02/index.htm

Jackson, Randall W., and Jonathan C. Comer. 1993. “An Alternative to 

Aggregate Base Tables in Input-Output Table Regionalization,” Growth 

and Change, 24, spring, 191-205.

Kahn, R. F. 1931. “The Relation of Home Investment to Unemployment,” 

Economic Journal, 41, June, 173-98.

Keynes, John M. 1936. General Theory of Employment, Interest, and Money. 

New York: Harcourt, Brace & Co.

Knotek, Edward S. 2007. “How Useful is Okun’s Law?” Economic Review, 

Federal Reserve Bank of Kansas City, fourth quarter, 73-103.
34

The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

Kramer-LeBlanc, Carol S., Peter Basiotis, and Eileen Kennedy. 1997. 

“Maintaining Food and Nutrition Security in the United States with 

Welfare Reform,” American Journal of Agricultural Economics 79(5), 

Nov., 1600-7.

Krugman, Paul. 2005. “Is Fiscal Policy Poised for a Comeback,” Oxford 

Review of Economic Policy, 21(4), 515-23.

Leontief, Wassily. 1936. “Quantitative Input and Output Relations in the 

Economic System of the United States,” Review of Economics and 

Statistics, 18(3), Aug., 105-125.

Leontief, Wassily. 1986. Input-Output Economics. New York: Oxford Univ. 

Press, 2nd edition.

Levedahl, J. William. 1995. “A Theoretical and Empirical Evaluation of the 

Functional Forms Used to Estimate the Food Expenditure Equation of 

Food Stamp Recipients,” American Journal of Agricultural Economics

77(5), Nov., 960-68.

Machlup, Fritz. 1939. “Period Analysis and Multiplier Theory,” Quarterly 

Journal of Economics, 54(1), Nov., 1-27.

Mankiw, Gregory. 2006. “The Macroeconomist as Scientist and Engineer.” 

NBER Working Paper 12349.

Miernyk, William. 1965. The Elements of Input-Output Analysis. New York, 

NY: Random House.

Miller, Ronald E., and Peter D. Blair. 1985. Input-Output Analysis: 

Foundations and Extensions. Englewood Cliffs, NJ: Prentice-Hall, Inc. 

Minnesota IMPLAN Group, Inc. 2010. “IMPLAN.com: Economic Impact 

Modeling Solutions.” Stillwater, MN., web access June. http://implan.

com/V3/Index.php

Moore, Fredrick. 1955. “Regional Economic Reaction Paths,” American 

Economic Review 45(2), May, 133-48.

Moore, Fredrick, and James Petersen. 1955. “Regional Analysis: An 

Interindustry Model of Utah,” Review of Economics and Statistics 37(4),

Nov., 368-83.

Pyatt, Graham. 2001. “Some Early Multiplier Models of the Relationship 

between Income Distribution and Production Structure,” Economic 

Systems Research 13(2), June, 139-63.

Pyatt, Graham, and Jeffery I. Round. 1979. “Accounting and Fixed Price 

Multipliers in a Social Accounting Matrix Framework,” Economic 

Journal 89(356), Dec., 850-73.
35

The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

Robinson, Sherman. 2006. “Macro Models and Multipliers: Leontief, Stone, 

Keynes, and CGE Models,” in Poverty, Inequality and Development: 

Essays in Honor of Erick Thorbecke. Alain de Janvry and Ravi Kanbur 

(eds.), New York: Springer Science, pp. 205-32. 

Romer, Christina, and Jared Bernstein. 2009. “The Job Impact of the 

American Recovery and Reinvestment Plan.” Jan. 9. http://otrans.3cdn.

net/ee40602f9a7d8172b8_ozm6bt5oi.pdf

Samuelson, Paul A. 1940. “The Theory of Pump-Priming Reexamined,”

American Economic Review 30(3), Sept., 492-506.

Samuelson, Paul A. 1942. “Fiscal Policy and Income Determination,”

Quarterly Journal of Economics 56(4), Aug., 575-605.

Schluter, Gerald, and William Edmondson. 1994. USDA’s Agricultural 

Trade Multipliers: A Primer. U.S. Department of Agriculture, Economic 

Research Service, AIB-697. 

Stern, Irving. 1975. “Industry Effects of Government Expenditures: An 

Input-Output Analysis,” Survey of Current Business 55(5), May, 9-23. 

Stewart, Ricky, Jessica Brede Stone, and Mary Streitwieser. 2007. “U.S. 

Benchmark Input-Output Accounts, 2002,” Survey of Current Business, 

87(10), Oct., 19-48. U.S. Bureau of Economic Analysis. https://www.bea.

gov/scb/toc/1007cont.htm

Sullivan, Patrick, Daniel Hellerstein, Leroy Hansen, Robert Johansson, 

Steven Koenig, Ruben Lubowski, William McBride, David McGranahan, 

Michael Roberts, Stephen Vogel, and Shawn Bucholtz. 2004. The 

Conservation Reserve Program: Economic Implications for Rural 

America. U.S. Department of Agriculture, Economic Research Service, 

AER-834. http://www.ers.usda.gov/publications/aer834/

Taylor, John B. 2009. “The Lack of an Empirical Rationale for a Revival of 

Discretionary Fiscal Policy,” American Economic Review 99(2), May, 

550-555.

Tschetter, John. 2010. “Exports Support American Jobs.” U.S. Department 

of Commerce, International Trade Administration, International Trade 

Research Report No. 1, April. http://trade.gov/publications/pdfs/exportssupport-american-jobs.pdf 

U.S. Bureau of Economic Analysis. 2010a. “Annual Input-Output Accounts, 

1998-2008.” Web access June. http://www.bea.gov/industry/io_annual.

htm

U.S. Bureau of Economic Analysis. 2010b. “Regional Input-Output 

Modeling System (RIMS II).” Web access June. http://www.bea.gov/

regional/rims/

U.S. Bureau of Labor Statistics. 2009a. “Major Sector Productivity and 

Costs.” Web access April. http://www.bls.gov/lpc/
36

The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

U.S. Bureau of Labor Statistics. 2009b. “Employment Projections.” Web 

access Dec. http://www.bls.gov/emp/home.htm

 U.S. Department of Agriculture, Food and Nutrition Service. 2009. 

“Economic Stimulus – Adjustments to the Maximum Supplemental 

Nutrition Assistance Program (SNAP) Monthly Allotments.” Memo 

to all regional directors, Feb. 18. http://www.fns.usda.gov/snap/rules/

Memo/2009/021809.pdf

U.S. Executive Office of the President, Council of Economic Advisers. 2010. 

“The Economic Impact of the American Recovery and Reinvestment Act 

of 2009, Second Quarterly Report.” Jan. 13. http://www.whitehouse.gov/

administration/eop/cea/factsheets-reports

U.S. Executive Office of the President, Council of Economic Advisers. 

2009. “Estimates of Job Creation from the American Recovery and 

Reinvestment Act of 2009.” May. http://www.whitehouse.gov/

administration/eop/cea/Estimate-of-Job-Creation/

U.S. Federal Reserve Bank of Dallas. 2004. “Gauging the Impact of the San

Antonio Toyota Plant,” Vista: South Texas Economic Trends and Issues, 

Spring. http://www.dallasfed.org/research/vista/vista0401.pdf

U.S. General Accounting Office. 2005. “Military Bases: Analysis of DOD’s 

2005 Selection Process and Recommendations for Base Closures and 

Realignments.” GAO-05-785. http://www.gao.gov/new.items/d05785.pdf

Woodford, Michael. 2009. “Convergence in Macroeconomics: Elements of 

the New Synthesis,” American Economic Journal: Macroeconomics 1(1), 

Jan., 267-79. 

Zandi, Mark. 2009. “The Economic Impact of the American Recovery

and Reinvestment Act.” Moody’s Economy.com, Jan. 21. http://www.

economy.com/mark-zandi/documents/Economic_Stimulus_House_

Plan_012109.pdf

Zandi, Mark. 2008a. “Assessing the Macro Economic Impact of Fiscal

Stimulus 2008,” Moody’s Economy.com, Jan. http://www.economy.com/

mark-zandi/documents/assissing-the-impact-of-the-fiscal-stimulus.pdf

Zandi, Mark. 2008b. “The Economic Outlook and Stimulus Options.” 

Written Testimony before the U.S. Senate Budget Committee. Moody’s 

Economy.com, Nov. 19. http://www.dismal.com/mark-zandi/documents/

Senate_Budget_Committee_11_19_08.pdf
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Appendix 1—Employment Data by Industry

Employment data by industry consist of: 

FTPT-jobs: count of hired full-time plus part-time workers; 

FTE-jobs: count of hired full-time equivalent workers; 

Self-employed: count of proprietors or self-employed; 

Prod-jobs: count of production workers as distinct from nonproduction 

workers.

Data on FTPT-jobs for non-agriculture industries are derived by combining 

data from the BEA Regional Economic Accounts, State Personal Income and 

Employment tables with BLS employment projections (based on historical 

data on industry employment). The BLS employment projections data use 

the BLS, Current Employment Statistics (CES), while providing more detail 

for some industries such as construction and government and including a few 

data adjustments relative to the CES data.1 The BLS data are used to disaggregate the more aggregated BEA industry employment data. It is possible to 

use the BLS industry employment data and get similar industry employment 

numbers for most industries, but BEA makes a few additional data adjustments that improve the count of FTPT-jobs in some industries.2

Agriculture includes 10 crop production industries (NAICS 111), four animal 

production industries (NAICS 112), and an industry of support activities 

for agriculture and forestry (called agricultural services, NAICS 115). For 

the two groups of farm industries (crop and animal production), the aggregate count of FTPT-jobs is from the BEA Regional Economic Accounts 

(870,000 hired workers). This count is similar to the count of 880,500 from 

USDA-NASS (see Farm Labor publication) and the count of 811,000 from 

USDA-ERS (see ARMS survey data).3 The BEA aggregate farm employment count is distributed among the ten crop production sectors and four 

animal production sectors with data on hired labor from USDA-ERS, ARMS 

survey. 

For the count of FTPT-jobs in the agricultural services industry, data from 

BLS, Quarterly Census of Employment and Wages for all employees in 

the private sector are used.4 The count of FTPT-jobs is 309,000, which is 

in between the count of 581,000 in the BEA Regional Economic Accounts 

and the USDA-NASS count of 232,000, and significantly larger than the 

BLS employment projections count of 97,000. The BLS projections count 

is smaller since it is derived from the count of workers who report that 

agricultural services is their primary occupation in the Current Population 

Survey—it is likely that some of these workers have other occupations that 

may be reported as primary. It is not clear which of the other three sources 

for a count of FTPT-jobs in agricultural services is best so the middle value 

is used. 

Data on FTE-jobs by industry are derived from the count of FTPT-jobs and 

a ratio of FTE-jobs to FTPT-jobs from BEA, NIPA tables 6.5 and 6.4 on

industry employment as FTE and FTPT. Essentially, the NIPA data are at 

1 For BEA data see table SA27-wage 

and salary employment by industry, 

http://www.bea.gov/regional/spi/. For 

BLS data see the database for national 

employment, all employees, http://

www.bls.gov/ces/. For BLS employment projections, see historical data on 

industry output and employment, http://

www.bls.gov/emp/empind2.htm.

2 See BEA Regional Economic 

Accounts: Methodologies, Local Area 

Personal Income and Employment 

Methodology for more details on the 

data adjustments, http://www.bea.gov/

regional/docs/lapi2007/. 

3 For USDA-NASS data, see 

http://usda.mannlib.cornell.edu/

MannUsda/viewDocumentInfo.

do?documentID=1063. For USDAERS data, see http://www.ers.usda.

gov/Briefing/ARMS/. The BLS-CES 

data do not include employment for the 

agricultural industries, while the BLS 

employment projections data have a 

count of 1,010,000 hired workers for 

the farm sector. The larger value from 

the BLS data seems to compensate for 

a low count of hired workers in the 

agricultural service industry. For this 

reason, the BLS data are not used.

4See http://www.bls.gov/cew/home.

htm.
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3-digit NAICS, whereas the industry detail in the input-output accounts can 

be at 6-digit NAICS for some industries, so the same ratio is used for groups 

of industries. 

Data on the count of self-employed persons by industry for nonfarm industries are taken from the BLS employment projections data. The data are 

the count of self-employed plus unpaid family workers by industry and are 

derived from the Current Population Survey. The 10.047 million total selfemployed (including farmers) in the BLS projections data compares well 

with the total in the BEA NIPA data (table 6.7) of 9.963 million. The BEA 

notes that the count includes active proprietors or partners who devote a 

majority of their working hours to their unincorporated businesses. How partners are counted makes a significant difference in comparison with the BEA 

Regional Economic Account data where the self-employed count is much 

larger at 29 million because it includes all partners no matter how active 

they are in the business. For this reason the BEA regional data are not used. 

The count of self-employed for the farm industries are from the USDA-ERS 

ARMS survey data. The 1.781 million aggregate count for farm selfemployed is almost twice the aggregate count of 0.945 million from the BLS

employment projections data. The difference is due primarily to the ARMS 

data including proprietors who have other jobs that may be their primary 

occupation, whereas the BLS self-employed data are a count of proprietors 

who treat farming as their primary occupation.

The aggregate counts of the employment measures are comparable to 

published numbers in the BEA NIPA tables. The 137.653 million FTPTjobs are slightly larger than the aggregate count of 136.578 million in the 

BEA NIPA table 6.4 for domestic industries. The 124.053 million FTE-jobs

compares well with the aggregate count of 123.312 million in the BEA NIPA 

table 6.5. The 10.882 million self-employed workers are larger than the 

aggregate count of 9.963 million in the BEA NIPA table 6.7, due primarily to 

the treatment of self-employed farmers. 

Production workers (prod-jobs) are distinguished from nonproduction 

workers in the BLS, CES survey data and in the historical database developed for BLS employment projections. In these databases, a production 

worker is not identical with the production occupation in the standard occupation classification system, but includes workers in this occupational category. Production workers in the CES and employment projections data are 

those workers directly involved with the production activity of the business. 

In the service industries, production workers exclude supervisory employees 

and owners. In goods-producing industries, production workers also exclude 

other categories of occupations that are not directly involved in production. 

This would include support staff such as accountants, secretaries, and administrative assistants. The distinction is not as precise as the distinction among 

standard occupational categories, but can still be useful for some types of 

economic analysis. The production-nonproduction workers are distinguished 

by industry at the 3- to 4-digit NAICS codes. On average, they account

for about 75 percent of total jobs. In the FANIOM model, a percent of all 

FTE-jobs that are production jobs is calculated by industry. Also, 65 percent 

of the self-employed by industry are combined with the production workers 

for the employment measure “prod-jobs plus self-employed.” 
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Appendix 2—Reallocating Proprietors’ 

Income From Capital to Labor Income

Proprietors’ income is a part of industry operating surplus (capital income) 

in the input-output accounts. For this report, proprietors’ income is taken out 

of capital income and added to labor income. This reallocation is consistent 

with the treatment of proprietors as labor and earning a return on their labor. 

In consequence, an increase in labor and labor income from a fiscal stimulus 

will involve an increase in work by proprietors and the income they receive 

from this work. Reallocating proprietors’ income from capital income to 

labor income will affect the induced effects in the multiplier process. Both 

IMPLAN and RIMS II input-output multipliers include this reallocation of 

proprietors’ income from capital income to labor income.

To reallocate proprietors’ income in the input-output accounts from capital 

income to labor income by detailed industry, it is necessary to disaggregate 

the more aggregate industry data on proprietors’ income from BEA NIPA 

table 6.12. This report uses detailed industry data on the number of proprietors by industry to disaggregate proprietors’ income. As a result, all proprietors’ income is treated as a return to labor, which is not a bad approximation 

since proprietors’ income is net of consumption of fixed capital (depreciation), which can be treated as a return to capital and is included in the capital 

income of the input-output accounts. Proprietors’ income is about 20 percent 

of capital income and about 12.6 percent of labor income.
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Appendix 3—Food Assistance National 

Input-Output Multiplier (FANIOM) Model

The FANIOM model is programmed in the GAMS software where it is 

treated as a system of simultaneous equations (Brooke et al., 1992). Model 

parameters are calibrated to data from the 2002 Benchmark Input-Output 

Accounts data, National Income and Product Accounts, and measures of 

employment from various sources. The model is solved either as a system 

of simultaneous equations (PATH solver) or as an optimization problem 

where the change in industry production squared is treated as an objective 

function to be minimized subject to the system of simultaneous equations 

(MINOS or CONOPT solver). Still, the solution values are not affected by 

the choice of solution algorithms, nor by the use of GAMS instead of some 

other software to solve the IOM model. The advantage of using the GAMS 

software is in the specification of the model as a system of algebraic equations. This is particularly helpful in the treatment of the induced effects for 

labor and capital income, and for calculating the domestic multiplier effects 

by adjusting for the import share of demand. GAMS syntax provides flexibility in how these features of the IOM model are specified. For instance, the 

induced effects from labor income with only production workers adjusting is 

a straightforward modeling specification given the data. 

The multiplier measures are calculated from the model solution values for 

industry production, GDP or value added, and employment. The change in 

the value of production, GDP, and employment from the initial base value 

relative to the exogenous change in final demand is the multiplier. Each 

type of multiplier (types I, II, and III, with or without import adjustment) is 

derived from a different model and solved separately in a sequence of model 

statements and solve statements. Summary reports of the results from the 

sequence of model solutions are easy to prepare.

 Listed below are the variables, parameters, and model equations for the type 

III multiplier with import adjustment. The type III multiplier includes direct, 

indirect, and induced effects from labor and capital income, and an adjustment for imports that reduces the share of domestic demand that is fulfilled 

by domestic production. Specification of a few key parameters is also listed 

below. Value added/GDP and employment are not treated as variables, but 

are calculated from base ratios with industry production.

To clarify some of the model notation, uppercase names are endogenous 

variables and lowercase names are parameters. GAMS code is in terms of 

set notation. Industry-commodity set notation is “ic” and equivalently “jc.” 

Industries and commodities are not distinguished in the set notation but they 

are distinguished in the input-output account make-and-use matrices as well 

as industry production, value added, and final demand. 

There are subsets of “ic”: “icmyes” is for industry-commodity where 

imports exist, “icyes” is for industry-commodity where production occurs, 

while “icnot” is where no production occurs. In the benchmark input-output 

accounts, there are several special industries-commodities that do not involve 

domestic production, and they are given special treatment in the input-output 

multiplier model so that change in final demand equals change in value added 
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The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

(see second-to-last equation). The special sectors are noncomparable imports 

(s00300), used goods (s00402), and rest-of-world industry (s00900). The last

equation is a quadratic objective function that minimizes the squared change 

in industry production that occurs from an exogenous change in final demand 

(“dfd(ic)”). 

Variables:

XDUP(ic) Industry production

XDCOMUP(ic) Commodity supply from domestic production 

XXDCOMUP(ic) Domestic commodity supply from domestic 

 production 

INTUSEUP(ic) Intermediate use of (domestic and imported) 

 commodity 

FDUP(ic) Final demand for commodity

IMPORTUP(ic) Imports by commodity

CDUP(ic) Household consumption by commodity 

YHCON Household income spent in consumption

YHGROSS Household gross income

INCTAX Household income tax

SAVINGS Household savings

YHLABNET Household income from labor net of labor tax 

 (soc-sec & medical)

YHCAPNET Household income from industry capital income 

 (operating surplus)

VALABUP(ic) Industry labor income including proprietors’ 

 income

VACAPUP(ic) Industry capital income or operating surplus net 

 of proprietors income

ADJIOC(icnot) Non-producing industry dummy variable

OBJIOM Objective function value

Parameters:

iomc(ic,jc) Make matrix, industry ic making commodity jc 

iouc(ic,jc)	 Use coefficient matrix, use of commodity ic by industry

 jc

export(ic) Export of commodity ic

gd(ic) Government demand, exogenous

invfxd(ic) Fixed investment

dst(ic) Inventory change

dfd(ic) 	 Exogenous change in final demand

mtoxratio(ic) Import to x ratio, where x is domestic demand 

	 	 (intermediate plus final)

cdsh(ic) Share of household income consumed on commodity ic

yhgovtrn0 Government transfer income to households

yhoth0 Other income to households

inctaxr Household income tax rate, calculated from NIPA data
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The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

savr Household savings rate, calculated from NIPA data

labtaxr Labor tax for Social Security and Medicare, calculated 

 from NIPA 

data alabr(ic) Ratio of industry labor income (include proprietors’ 

 income) to industry production 

vacapr(ic) Ratio of industry capital income (operating surplus) to 

 industry production 

yhcapfrac Fraction of capital income that households receive, 

 calculated from NIPA data

vat0 Total value added or GDP, summed over all industries 

 from input-output account

Specification of Key Parameters for Multiplier Calculations:

XXDCOM(IC) = INTUSE(IC) + FD(IC) - IMPORT(IC)

MtoXratio(IC) = IMPORT(IC)/(INTUSE(IC) + FD(IC)) 

FD(IC) = CD(IC) + GD(IC) + INVFXD(IC) + DST(IC)

VALABR(IC) = VALAB(IC) / XD(IC) 

VACAPR(IC) = VACAP(IC) / XD(IC) 

JBR(IC,iemp) = JOBS(IC,iemp) / XD(IC) 

Consumption adjusted for trade and transportation margins 

CDTT(ICTT) = SUM(IC, 

IOMARGFD(IC,”F01000”,ICTT) ) 

CDMRKT(IC) = CD(IC) - CDTT(IC) 

CDMRGR(IC,ICTT) = IOMARGFD(IC,”F01000”,ICTT) / 

CDMRKT(IC) 

CDPURCH(IC) = CDMRKT(IC)*(1+SUM(ICTT, 

CDMRGR(IC,ICTT)

Income data and calibration

yhlabnet0 = (1-labtaxr)*sum(ic, valab(ic) ) 

yhcap0 = yhcapfrac * sum(ic, vacap(ic) ) 

labtaxr = labtax0 / sum(ic, valab(ic) ) 

yhcapfrac = 0.467 

yhgross0 = yhcon0 + inctax0 + savings0 

yhcon0 = sum(ic,cd(ic) )

inctaxr = inctax0 / yhgross0 

savr = savings0 / ((1-inctaxr)*yhgross0) 

yhoth0 = yhgross0 - yhlabnet0 - yhcap0 - yhgovtrn0 
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The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

Model Equations:

(1) XDUP(icyes) = SUM(jc, iomc(icyes,jc) * 

XDCOMUP(jc) ) 

(2) XDCOMUP(icyes) = XXDCOMUP(icyes) + 

export(icyes) 

(3) XXDCOMUP(ic) = INTUSEUP(ic) + FDUP(ic) - 

IMPORTUP(ic) 

(4) INTUSEUP(ic) = SUM(jc, iouc(ic,jc) * XDUP(jc) ) 

(5) FDUP(ic) = CDUP(ic) + gd(ic) + invfxd(ic) + 

dst(ic) + dfd(ic) 

(6) IMPORTUP(icmyes) = mtoxratio(icmyes) * (FDUP(icmyes) 

+ INTUSEUP(icmyes)) 

(7) CDUP(ic) = cdsh(ic)*YHCON 

(8) YHCON = YHGROSS - INCTAX - SAVINGS 

(9) YHGROSS = YHLABNET + YHCAPNET + 

yhgovtrn0 + yhoth0 

(10) INCTAX = inctaxr * YHGROSS 

(11) SAVINGS = savr * (1-inctaxr) * YHGROSS 

(12) YHLABNET = (1-labtaxr) * sum(ic, VALABUP(ic)) 

(13) VALABUP(ic) = valabr(ic) * XDUP(ic) 

(14) V ACAPUP(ic) = vacapr(ic) * XDUP(ic) 

(15) YHCAPNET = yhcapfrac * sum(ic, VACAPUP(ic)) 

(16) -ADJIOC(icnot) 

 * SUM(jc, iouc(icnot,jc)

 *XDUP(jc)) = FDUP(icnot) + export(icnot) - 

IMPORTUP(icnot) 

(17) OBJIOM = SUM(ic, (XDUP(ic)-XD(ic))

*(XDUP(ic)-XD(ic)) ) 

Given the solution values for the model variables, the multipliers for 

production, GDP, and employment are calculated as follows:

TDFD = sum(ic, dfd(ic)) total exogenous change in 

	 final demand

TXDUP = SUM(IC, XDUP.L(IC) ) total industry 

	 production after fiscal stimulus

DTXD = TXDUP - TXD total change in industry 

production *## adjustment of value added 

for special sectors with no production

IOUSEU1(IC) = SUM(JC, IOUC(JC,IC)*XDUP.L(IC) ) 

IOUC(ICNOT,JC) = IOUC(ICNOT,JC) * ADJIOC.L(ICNOT) 

IOUSEU(IC) = SUM(JC, IOUC(JC,IC)*XDUP.L(IC) ) 

VAUP(IC) = VAR(IC) * XDUP.L(IC) + (IOUSEU1(IC)-

IOUSEU(IC)) 

VAUPT = SUM(IC, VAUP(IC) ) total value added or 

	 GDP after fiscal stimulus
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The Food Assistance National Input-Output Multiplier (FANIOM) Model and Stimulus Effects of SNAP/ ERR-103

Economic Research Service/USDA

DVAT = VAUPT – VAT0 total change GDP

JOBSUP(IC,iemp) = JBR(IC,iemp)*XDUP.L(IC) industry 

	 employment after fiscal stimulus

DJOBST(iemp) = SUM(IC, JBR(IC,iemp)*XDUP.L(IC)) - 

JOBST(iemp) total change in emp

Multiplier measures:

TIOEM(“TOTAL”,”IOM-prod”) = DTXD / TDFD

TIOEM(“TOTAL”,”IOM-va”) = DVAT0 / TDFD

TIOEM(“TOTAL”,”IOM-hired”) = 1000* DJOBST(”hired”) / 

TDFD

TIOEM(“TOTAL”,”IOM-fte”) = 1000* DJOBST(”fte”) / 

TDFD

TIOEM(“TOTAL”,”IOM-self”) = 1000* DJOBST(”self”) / 

TDFD