Updated (Preliminary) Estimates of the 

SNAP Multiplier 

Erik Scherpf

Pat Canning

USDA Economic Research Service

FCSM Fall Conference

10/26/2023 

The findings and conclusions in this presentation are those of the author(s) and should 

not be construed to represent any official USDA or U.S. Government determination or 

policy.
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SNAP as an Economic Multiplier

• SNAP is the largest food assistance program of the U.S. Department of Agriculture

– 2019: $60 billion in 2019

– 2022: nearly $120 billion

• In addition to its primary goal of reducing food insecurity, SNAP acts as an automatic 

stabilizer during economic downturns

• SNAP spending can boost economic activity in local economies 

– SNAP generates income for those involved in production, distribution, marketing 

and sales of goods purchased with SNAP benefits, which is in turn spent on more 

goods and services

– This creates a multiplier effect whereby each SNAP dollar generates more than 

dollar in economic activity
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Summary

• Build on approach in Canning & Stacy (2019) to estimate the economic multiplier 

effect of an additional $1 billion in SNAP benefit payments using Social Accounting 

Matrix

– SAM based on input-output model using 2016 data

– Canning & Stacy found a SNAP multiplier effect of 1.5 

• Estimate demand system using household microdata 

– Departure from Canning and Stacy (2019) which estimated demand system on aggregate time 

series data

– relies on pre-pandemic data

• SNAP multiplier will soon be updated using 2021/2022 data
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Social Accounting Matrix

• Social Accounting Matrix (SAM) is a fully integrated economic accounting system 

that summarizes all transactions and transfers between economic agents

• ERS Food Environment Data System Social Accounting Matrix (FEDS-SAM)

– Extension of an input-output model

– Based on empirically estimated marginal consumption and saving behaviors of 

two representative households: SNAP-recipient and non-SNAP-recipient 

households

– Modeling marginal behavior captures household responses to new government 

spending
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FEDS-SAM baseline model 

–Fixed Price Multiplier 

• unique expenditure and savings elasticity parameters for each 

consumer good and for household savings

–No ‘crowding out’ 

–Fiscal stimulus during a period of full employment or rising interest rates 

may lead to increasing labor costs and increased competition for scarce 

capital resources.

–Income effects are main driver of multiplier 
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Key Assumptions of the SAM Multiplier Model

1. Slackness in factor markets: supply of labor and production capacity 

exceed demand for these inputs

2. Additional labor and capital added in response to new spending is equally 

productive as the factors already in use

3. New spending scenarios do not change existing relative prices in factor, 

commodity, and product markets

Assumptions 1 and 3 more difficult to justify in current economy

–However, SNAP tends to flow to pockets of the economy still marked by slackness
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Some Previous Research on Federal Spending Multipliers

• Moody’s Analytics model (Blinder and Zandi, 2015): increased spending on 

food stamps had the highest multiplier of any of the ARRA stimulus 

programs (1.74)

• Pender et al. (2019) estimated that $10,000 increase in SNAP spending at 

the county level translated to an increase of between 0.4 and 0.5 jobs (or 

40,000 to 50,000 jobs per billion spent) between 2001-2014.

• Hanson (2010) used a Food Assistance National Input-Output Multiplier 

(FANIOM) model to estimate a SNAP multiplier of 1.79 on GDP
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Moody Analytics Fiscal Multipliers (2022)

Zandi and Yaros (2021). Macroeconomic Impact of Home and Community-Based 

Services Expansion. Moody’s Analytics Analysis.
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Structure of FEDS-SAM Multiplier Model

• Endogenous Sectors in the multiplier model

– Sectors which are allowed to adjust in the short-term in response to the new spending 

scenario

• Domestic industry production

• Aggregation of industry output into commodities and products 

• Households (SNAP & non-SNAP)

• Exogenous Sectors

– Government and capital markets 

• International Sector treated as aggregate endogenous sector

– Allow imports to meet some of the new direct and induced consumption expenditures

– Maintain size of overall US trade deficit relative to total volume of international trade

• No evidence of relationship between SNAP enrollment and trade deficit 
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Data 

• Bureau of Economic Analysis (BEA) National Income and 

Product Accounts

• Bureau of Labor Statistics (BLS) Consumer Expenditure Survey 

(2015-2019)

• 2016 BLS Make and Use Tables
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Steps in Estimating the Multiplier

• Estimate LES Demand System using CE household data for SNAP and nonSNAP recipient households

–uses CE-PCE concordance

• Inverse variance weighted LS adjustment in the household LES parameters 

estimated from CE data in order exactly fit the 2016 PCE data compiled by 

BEA

• Derive the multiplier effect of a given injection of government spending 

(i.e., increase in SNAP benefit payouts) using the SAM
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Structure of FEDS-SAM Multiplier Model
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Structure of FEDS-SAM Multiplier Model

• T captures all endogenous transactions in the model 

– 202 industries, 201 commodities, 15 consumer products, 2 households, and 1 aggregate int’l 

sector 

– includes household MPC’s and MPS’s, production coefficients within the ‘Activities’ block, and 

the various income and commodity flow parameters between the different subaccounts

• x: a vector that consolidates exogenous inflows into the endogenous accounts

– 2 government entities and one aggregate capital sector

– 2 exogenous household accounts (committed expenditures)

• l: “leakage block” 

– a vector that consolidates outflows from the endogenous sector

• y and y’: total inflow and outflow vectors, respectively

• SAM accounts must balance inflows and outflows: T + x = y and T+l = y’
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SAM Multiplier Model

• Dividing each element in T by its corresponding column total in y’ converts the 

endogenous block into a technical and behavioral parameters matrix: AA = TTyy−11

• The multiplier for an exogenous injection of spending x is derived as follows:

AA + xx = yy

xx = II − AA yy

yy = II − AA −11xx

yy = MM

where M is the fixed-price multiplier matrix

• For this scenario, x is an inflow vector representing a hypothetical SNAP 

enrollment change of an additional $1 billion in SNAP benefit payouts 

– i.e., new benefits to households that were previously not participating in SNAP.
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Linear Expenditure System

HH expenditure in the FEDS-SAM is characterized by a Linear Expenditure System:

ppiiqqii = ppiiγγii + ββii MM − pp1γγ1 − pp2γγ2 − ⋯ − pp14γγ14 , ii = 1, ... , 14

SSSSSSSSSSSSSSSSSSSSSSSSSS II IIII

• ppii is the price and qqii is the quantity demanded of commodity group ii

• γγii is committed, or subsistence, expenditure on commodity group ii

• ββii is marginal expenditure on commodity group ii

• MM is ‘income’ (total expenditure)

• ∑ββii = 1 and γγii > 0, ii = 1, ... , nn

• LES estimated for both SNAP and non-SNAP households
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LES Results – Marginal Expenditures ββii

No SNAP SNAP

Accommodations 0.024* 0.005*

Clothing and 

Footwear 0.021* 0.026*

Financial & 

Insurance 0.070* 0.060*

Food at Home 0.044* 0.092*

Food Away from 

Home 0.051* 0.043*

Furnishings 0.043* 0.034*

Healthcare 0.019* .0155* 

Housing and 

Utilities 0.131* 0.160*

Motor Vehicles 0.264* 0.318*

Other Durables 0.006* 0.004*

Other Nondurables .0321* 0.066* 

Other Services 0.203* 0.130*

Recreation Services 0.035* 0.012*

Recreational Goods 0.056* 0.036*

* significant at .01 level
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LES Results

• High marginal propensity to spend on Motor Vehicles and Parts for both 

SNAP and non-SNAP households

• Parker et al. (2013), using CE survey, found high MPC out of the 2008 

Economic Stimulus Payments of 2008

• A significant part of Parker et al.’s (2013) high MPCs came from spending on 

motor vehicles

• Orchard, Ramey & Weiland (2023) find that Parker et al. (2013) found that 

micro MPC estimates from CE survey generated implausible macro 

counterfactuals
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Marginal Propensity to Spend on Food at Home

• A key parameter in our model is the MPC food-at-home (FAH) among SNAP recipient 

households

• LES results suggest ~10 cents of every additional dollar of income is spent on foodat-home

• Other research that has looked specifically at marginal spending out of SNAP 

benefits puts the MPC FAH out of SNAP higher in the range of 0.15 to 0.50

• Using difference-in-differences we estimate the MPC out of SNAP to be ~ 0.30
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$1 Billion Injection Vector based on LES parameters

SNAP-induced household expenditures and savings of new SNAP 

households

Product New SNAP Households

$million

Food and beverages at home 300.0

Food services 27.3

Clothing and footwear 44.8

Other nondurable goods 89.4

Motor vehicles and parts 12.5

Furnishings and durable household equipment 60.1

Recreational goods and vehicles 100.1

Other durable goods 39.7

Housing and utilities 94.5

Health care 121.3

Recreation services 28.6

Accommodations 13.5

Financial services and insurance 15.1

Other services 39.8

Personal Savings 0.0
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SNAP Multiplier 

• The new SAM model—based on micro LES estimates—yields a SNAP 

multiplier of ~1.4

– Canning & Stacy (2019) found a SNAP multiplier of ~1.5

• The $1 billion of new SNAP benefits generates ~12,000 jobs

– Canning & Stacy (2019): ~13,500 jobs

• Higher marginal spending on motor vehicles & parts—an import-intensive 

category—leads to greater ‘leakage’ and hence lowers multiplier.

– Import share of domestic availability ~41%

• Lower marginal spending on domestically provided services like healthcare
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Future Work

• Incorporate additional representative households into the model

–Disaggregate non-SNAP recipient households by income quintiles

– More accurately reflect heterogeneity of expenditures across the income 

distribution 

• Build out the SAM model to a Computable General Equilibrium Model 

– Move capital account to endogenous sector 

– Short-run price adjustments

– Better capture effect of changes in SNAP when economy is at or near full 

employment (e.g., crowding-out effect)
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