United States

Department of

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 128

November 2011 

Direct and Intermediated 

Marketing of Local Foods 

in the United States

Sarah A. Low

Stephen Vogel 
www.ers.usda.gov

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Recommended citation format for this publication:

Low, Sarah A., and Stephen Vogel. Direct and Intermediated Marketing of 

Local Foods in the United States, ERR-128, U.S. Department of Agriculture, 

Economic Research Service, November 2011.

Cover photos: Local dairy sign, ERS, USDA. All others, Thinkstock.
United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Economic

Research

Report

Number 128

November 2011

Direct and Intermediated 

Marketing of Local Foods 

in the United States

Abstract

This study uses nationally representative data on the marketing of local foods to assess 

the relative scale of local food marketing channels. This research documents that sales 

through intermediated marketing channels, such as farmers’ sales to local grocers and 

restaurants, account for a large portion of all local food sales. Small and medium-sized 

farms dominate local foods sales marketed exclusively through direct-to-consumer channels (foods sold at roadside stands or farmers’ markets, for example) while large farms 

dominate local food sales marketed exclusively through intermediated channels. Farmers 

marketing food locally are most prominent in the Northeast and the West Coast regions 

and areas close to densely populated urban markets. Climate and topography favoring the 

production of fruits and vegetables, proximity to and neighboring farm participation in 

farmers’ markets, and good transportation and information access are found to be associated with higher levels of direct-to-consumer sales. 

Keywords: Local foods, direct marketing channels, direct sales, intermediated sales

Acknowledgments: The authors are grateful to Economic Research Service reviewers 

Robert Hoppe and Michael Hand; Larry Lev from Oregon State University; Debra Tropp 

from USDA’s Agricultural Marketing Service; and an anonymous reviewer for insightful 

comments and suggestions. 

Sarah A. Low, slow@ers.usda.gov

Stephen Vogel, svogel@ers.usda.gov
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Contents

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

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

Farms Marketing Local Foods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Historical Trends in Direct Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Which Marketing Channels Do Local Food Sales Farms Use? . . . . . . . . 4

What Commodities Are Being Produced for Local Food Sales? . . . . . . . 7

Comparing Farms That Market Local Foods With Farms 

That Do Not Market Local Foods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Location of Local Food Sales Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Local Food Sales in U.S. Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Local Food Sales Highest in Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . 12

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Appendix 1: Developing Data on Marketing of Local 

Agricultural Products Using the 2008 ARMS . . . . . . . . . . . . . . . . . . . . 18

Appendix 2. Modeling Direct-to-Consumer Sales . . . . . . . . . . . . . . . . . 23

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Hypothesis and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Empirical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
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Summary

What Is the Issue?

Despite increased production and consumer interest, locally grown food 

accounts for a small segment of U.S. agriculture. For local foods production 

to continue to grow, marketing channels and supply chain infrastructure must 

deepen. Information on U.S. local food producers and their marketing channels, however, is incomplete. New information on farmers that market foods 

locally and the marketing channels they use presented in this report could 

aid private- and public-sector efforts to support this sector of the agricultural 

economy. This report uses the 2008 Agricultural Resource Management 

Survey (ARMS) to explore farmers’ use of both direct-to-consumer and 

inter-mediated marketing channels in selling locally produced foods to 

consumers. 

What Did the Study Find? 

•	Marketing of local foods, via both direct-to-consumer and intermediated 

channels, grossed $4.8 billion in 2008—about four times higher than estimates based solely on direct-to-consumer sales. 

•	Farms marketing food commodities exclusively through intermediated 

channels reported $2.7 billion in local food sales in 2008—over three 

times higher than the value of local foods marketed exclusively through 

direct-to-consumer channels, and two times higher than the value of local 

foods marketed by farms using a combination of direct-to-consumer and 

intermediated channels.

•	Small farms (those with less than $50,000 in gross annual sales) accounted 

for 81 percent of all farms reporting local food sales in 2008. They averaged $7,800 in local food sales per farm and were more likely to rely 

exclusively on direct-to-consumer marketing channels, such as famers’ 

markets and roadside stands.

•	Medium-sized farms (those with gross annual sales between $50,000 and 

$250,000) accounted for 17 percent of all farms reporting local food sales 

in 2008. They averaged $70,000 in local food sales per farm and were 

likely to use direct-to-consumer marketing channels alone or a mix of 

direct-to-consumer and intermediated marketing channels.

•	Large farms (those with gross annual sales of $250,000 or more) 

accounted for 5 percent of all farms reporting local food sales in 2008. 

They averaged $770,000 in local food sales per farm and were equally 

likely to use direct-to-consumer channels exclusively, intermediated channels exclusively, or a mixture of the two.

•	Large farms accounted for 92 percent of the value of local food sales 

marketed exclusively through intermediated channels.

•	For small and medium-sized farms with local food sales, more operators 

identified their primary occupation as farming and devoted more time 

to their farm operation than operators of similarly sized farms without 

local sales. 
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•	Vegetable, fruit, and nut farms dominated local food sales.

•	Direct-to-consumer sales of food commodities were affected by climate 

and topography that favor fruit and vegetable production, proximity to 

farmers’ markets and neighboring local food farms, and access to transportation and information networks. 

•	The value of locally sold food is highest in metropolitan areas and is 

geographically concentrated in the Northeast and on the West Coast.

How Was the Study Conducted?

We used the 2008 ARMS data to analyze farmers’ use of specific direct-toconsumer marketing channels (i.e., use of roadside stands, farmers’ markets, 

onfarm stores, and community-supported agriculture arrangements) and 

intermediated marketing channels (i.e., farmers’ sales to local retail, restaurant, and regional distribution outlets), but also farm characteristics and the 

value of sales for farmers engaged in local food sales. Data from the 2007 

Census of Agriculture supported the spatial econometric model used to identify determinants of direct-to-consumer sales.
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Introduction

Although locally grown foods sold by farmers directly to consumers, restaurants, or grocers have become more popular, information on local U.S. food 

producers and their marketing channels is incomplete. Case studies have 

provided detailed information about local food sales in certain U.S. regions 

(e.g., King et al., 2010), but the scope of nationally representative data has 

been limited to direct-to-consumer sales. This report provides new information on marketing channels based on the 2008 Agricultural Resource 

Management Survey (ARMS).

Whether purchased at a farmers’ market or at a nearby grocer, “local food” is 

an ambiguous characteristic of consumer purchases. In this study, the definition of “local food” is based on the set of marketing channels (as measured 

from the farmgate to the consumer) used by farmers (Hand and Martinez, 

2010; Martinez et al., 2010). Thus, direct-to-consumer and intermediated

(direct-to-grocer/restaurant) food sales are considered “local foods” in this 

study.1 National data on direct-to-consumer food sales have been available 

since the 1978 Census of Agriculture, long before the current surge in local 

foods interest. Nationally representative data on intermediated sales by farm 

operators, however, became available in the 2008 ARMS. In this report, we 

explore the use of both direct-to-consumer and intermediated sales by size 

of farms, their commodity specializations, and the characteristics of farm 

operators participating in local foods marketing channels. Data from the 

2007 Census of Agriculture allow us to explore regional patterns in direct-toconsumer sales. 

1Martinez and others (2010) explored 

the conceptual and policy cross-currents embedded in defining local foods. 

Defining local foods through farmers’ 

marketing channels avoids problematic definitions based on geographic 

distances between producers and 

markets and is congruent with relevant 

literature.
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Farms Marketing Local Foods

Farmers selling food commodities through farmers’ markets, roadside stands, 

and other local food sales outlets may account for a small segment of U.S. 

agriculture, but this segment has experienced recent growth and increased 

popularity. Historically, nationally representative data on food sold directly 

to consumers by farmers has been used to characterize farms participating in 

such sales. 

Historical Trends in Direct Sales

Data on direct-to-consumer food sales were first collected in the 1978 Census 

of Agriculture, after the Farmer-to-Consumer Direct Marketing Act was 

passed, and direct-to-consumer sales data have been collected every 5 years 

since (except for 1987). 

Over the 1978-2007 period, farms with direct-to-consumer food sales represented an average 5.5 percent of all farms, and total direct-to-consumer sales 

accounted for 0.3 percent of total farm sales. The number of farmers engaged 

in direct-to-consumer sales peaked in 1982, likely due to the 1976 Farmer-toConsumer Direct Marketing Act, which provided funding for activities that 

fostered direct marketing, such as technical assistance via agricultural extension (fig. 1). Between 1992 and 2007, the number of farmers participating in 

direct-to-consumer sales increased by 58 percent to 136,000, and the constant 

dollar value of direct sales increased by 77 percent to $1.2 billion.2 

The Census of Agriculture contains a limited amount of data on direct-toconsumer sales—the number of farms engaged in this activity and the value 

of direct-to-consumer sales. Researchers have called for the census to collect 

information on different local food marketing channels (Brown, 2002; Lev 

2In 1997, USDA adjusted census data 

to correct for small farms previously 

missed and, consequently, part of the 

1997 increase may be due to changes in 

data collection and weighting procedures.

1978

Farms selling local foods 

directly to consumers Direct sales 

of local foods

1982 1992 1997 2002 2007

60

80

100

120

140

160

200

400

600

800

1,000

1,200

1,400

Note: Inflation adjusted sales were calculated based on the gross domestic product implicit price 

deflator published by the Bureau of Economic Analysis, U.S. Department of Commerce and 

calibrated to 2007=100.

Source: 1978, 1982, 1992, 1997, 2002, and 2007 U.S. Censuses of Agriculture.

Census year

Thousand farms Million dollars (constant 2007)

Figure 1

Direct-sales farms and direct sales of local foods, 1978-2007
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and Gwin, 2010). The 2008 ARMS collected this information, and these data 

suggest that prior data collection efforts that focused on direct-to-consumer 

sales missed a relatively large portion of local food sales—sales of food for 

human consumption to grocers and restaurants (see box, “What Can We 

Learn From the 2008 ARMS Data on Local Food Sales?”). 

Using the 2008 ARMS, we measured direct-to-consumer sales of local foods 

through four outlets: farmers’ markets, roadside stands, onfarm stores, and 

community-supported agriculture arrangements (CSAs).3 Intermediated

marketing channels include sales to regional distributors and grocery stores, 

restaurants, or other retailers.4 Generally, marketing channels are classified 

as intermediated when local food products pass through one or more intermediate steps in the local food supply chain before reaching the consumer 

(King et al., 2010). While the 2008 ARMS collected data on farmers’ use of 

specific local food marketing channels, it did not collect data on the value 

of sales linked to a specific channel. Without these data, researchers must 

adhere to the strict trichotomy that groups local food sales by exclusive use 

of direct-to-consumer outlets, exclusive use of intermediated channels, or 

marketing through both channels (fig. 2).

The 2008 ARMS estimates shed light on two characteristics of local 

food supplies. 

•	Gross sales of locally marketed food (to consumers and local intermediaries) are four times larger than previous census and ARMS estimates 

suggested, representing 1.9 percent of total gross farm sales, primarily 

because intermediated sales were included for the first time. 

•	Most local foods are marketed through intermediated channels, accounting 

for 50-66 percent of the value of all local food sales. 

Our findings validate local foods researchers’ concerns that direct-toconsumer sales account for only a relatively small portion of total local food 

sales (Lev and Gwin, 2010; Clancy and Ruhf, 2010). 

3A CSA buying club is a marketing 

arrangement in which a group of households agree to purchase shares of a 

farmer's expected yield before planting. 

These upfront cash payments allow the 

farmer to buy inputs and share the output and yield risks with CSA members. 

These arrangements are referred to as 

“subscription agriculture.” Some CSAs 

tie households to formal contracts and 

others to informal arrangements and/

or barter. 

4Regional local food distributors make 

up a very small portion of intermediated local food sales, suggesting that 

farmers perceive the difference between 

regional local food distributors and 

national distributors. Also, the 2008 

ARMS did not explicitly collect data on 

emerging institutional outlets, such as 

farm-to-school arrangements.

Direct-to-consumer 

outlets

Intermediated 

marketing channels Both

$4.8 billion in sales

Figure 2

Farmers’ local food marketing, 2008

71,200 farms

$877 million in sales

• Farmers’ markets

• Roadside stands

• Farm stores

• CSA arrangements

13,400 farms

$2.7 billion in sales

• Grocers

• Restaurants

• Regional distributors

22,600 farms

$1.2 billion in sales

CSA=Community-supported agriculture.

Source: USDA, Economic Research Service calculations based on 2008 Agricultural Resource Management Survey, conducted by 

USDA, National Agricultural Statistics Service and Economic Research Service.

Exclusively

Exclusively
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Which Marketing Channels Do Local Food 

Sales Farms Use?

Given that the market for local foods is more extensive than previously 

measured, we explored how local food sales farms (by farm sales class) 

differ in their use of marketing options. According to the 2008 ARMS, 

small local food farms (gross farm sales less than $50,000) represented 

almost 81 percent of all local food farms; medium-sized farms (gross farm 

sales $50,000-$249,999) represented 14 percent; and large farms (sales of 

$250,000 or more) accounted for almost 5 percent of all local food farms 

(table 1). 

The ratio of local food sales to total farm sales measures how these farms 

rely on local food sales for their financial viability.5 For all sizes of local food 

farms, marketing local food products accounts for 61 percent of gross farm 

sales, on average (table 1). The median ratio of local food sales to total farm 

sales is higher than the mean ratio for each farm size, highlighting the fact 

that many of these farms rely on local food sales. Almost two-thirds of all 

local food producers reported that local food sales accounted for at least 75 

percent of their total gross farm sales, while 22 percent of all local food sales 

farms reported that such sales accounted for less than 25 percent of their total 

gross farm sales. Higher local food sales shares suggest that local food sales 

farms are well integrated into existing direct-to-consumer and intermediated 

supply chains.6

A difference exists in the size class distribution of direct-to-consumer sales, 

as observed in the 2007 Census of Agriculture, and total local foods sales, as 

observed in the 2008 ARMS. Small farms in 2008 accounted for 11 percent 

of total local food sales; medium-sized farms accounted for 19 percent of 

total local food sales; and large farms accounted for almost 70 percent (table 

1). These shares of total local food sales based on farm size are at odds with 

those for direct-to-consumer sales only, in which each sales class accounted 

for roughly a third of direct-to-consumer sales. 

Small farms are more likely to market through direct-to-consumer outlets, 

perhaps because small farms cannot generate enough volume for distributors 

and institutions that demand high volumes of local food (i.e., grocers, restaurants, and schools) (Gale, 1997; Brown, 2002). Small farms account for 81 

percent of all local food sales farms and many rely on exclusive use of direct5About 20 percent of all farms are point 

sales farms (sales less than $1,000). 

Due to statistical reliability problems 

associated with few observations, this 

study was not able to report any local 

food farms classified as point sales 

farms. This suggests that point sales 

local food farms may have been part 

of the 146,000 farmers excluded from 

this study that reported direct sales 

to consumers but failed to report the 

marketing channels used or the value 

of their direct sales (see Appendix 1 for 

further discussion).

6Local foods specialists and analysts 

studying the economic viability of smalland medium-sized farms advocate 

public- and/or private-sector initiatives 

to deepen and widen the infrastructure 

of these supply chains (Clancy and 

Ruhf, 2010; Matteson and Heuer, 2008). 

As an example of a private-sector 

initiative on local food, Walmart’s 

Heritage Agriculture program has been 

covered in the press including The New 

York Times (Clifford, 2010) and The 

Atlantic Monthly (Kummer, 2010), 

but no study to date has looked at this 

program’s effects on small farms and 

local communities.

Comparing ARMS Data on Local Foods Sales

Prior to the 2008 ARMS, data collected on local food sales to consumers referred to “direct sales of agricultural products for human consumption” in the 

Census of Agriculture and ARMS surveys. The 2008 ARMS questionnaire 

asked an extensive set of questions on farmers’ direct sales; however, the 

phrase “for human consumption” was omitted. As a result, the 2008 ARMS 

data had to be manipulated to provide estimates of direct food sales that were 

consistent with estimates from previous ARMS and census of agriculture data.*

* See “Appendix 1: Developing Data on Marketing Local Agricultural Products 

Using the 2008 ARMS” for a discussion of the 2008 ARMS questionnaire and how 

sales of local foods were estimated for this study.
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to-consumer marketing channels, grossing $6,740 in sales on average in 2008 

(table 1). The share of small- and medium-sized farms exclusively marketing 

through direct-to-consumer channels is higher than the corresponding share 

of large farms. 

Selling local foods through intermediated outlets may require less farm labor 

than selling via direct-to-consumer outlets because farmers are not required 

to spend time at intermediated outlets (Brown, 2002). Larger farms may have 

a comparative advantage in intermediated sales because many restaurants, 

grocers, and regional distributors demand timely delivery of large volumes of 

food with consistent quality. In 2008, large local food sales farms accounted 

Table 1 

Marketing channels used by local food sales farms, by farm size

Farm size

IItem

Small

(sales of less than 

$50,000) 

Medium 

(sales of $50,000- 

$249,999) 

Large

(sales of $250,000

or more) All

----------------------------------Number----------------------------------

Local food sales farms 86,726 15,202 5,301 107,229

----------------------------------Percent-----------------------------------

Local food sales farms 80.9 14.2 4.9 100.0

All farms 5.3 5.1 2.5 5.0

Average ratio of local food sales to total 

farm sales 68.8 67.2 57.5 61.2

Median ratio of local food sales to total 

farm sales 100.0 80.0 80.0 100.0

Farms by marketing channels 100.0 100.0 100.0 100.0

Direct-to-consumer channels only 72.1 46.5 31.0 66.4

Intermediated marketing channels only 11.3 10.4 37.1 12.5

Both marketing channels 16.6 43.0 31.9 21.1

Local food sales: ---------------------Percent---------------------- Million dollars

Marketed through all channels 11.1 19.1 69.8 4,806

Direct-to-consumer channels only 33.7 38.9 27.4 887

Intermediated marketing channels only 3.5 3.6 92.9 2,720

Both marketing channels 11.7 39.5 48.8 1,199

Average local food sales per farm: -----------------------------------Dollars-----------------------------------

Marketed through all channels 7,856 69,985 771,965 56,240

Direct-to-consumer channels only 6,737 66,247 305,181 17,621

 Intermediated marketing channels only 10,242 73,126 1,338,257 217,150

Both marketing channels 9,768 72,312 352,375 53,103

Source: USDA, Economic Research Service calculations based on 2008 Agricultural Resource Management Survey, conducted by 

USDA, National Agricultural Statistics Service and Economic Research Service.
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for 93 percent of the $2.7 billion in sales generated exclusively through intermediated channels, averaging $1.3 million in local food sales per farm (see 

table 1). Medium and large local food sales farms together accounted for 88 

percent of almost $1.2 billion in sales marketed by farms using both intermediated and direct-to-consumer outlets. 

Small local food sales farms gross, on average, $10,240 per farm annually 

when marketing exclusively through intermediated channels (see table 1). 

Formal and informal collaboration with other farmers provides a way for 

these small farms to meet the quantity, quality, packaging, and delivery 

requirements of grocers and restaurants (PFI, 2009). Medium-sized farms 

accounted for 17 percent of food farms relying solely on intermediated 

marketing channels, averaging $203,900 in local food sales per food farm. 

In 2008, 107,000 local food sales farms reported using 160,800 marketing 

channels to sell local food (table 2). Direct-to-consumer outlets accounted 

for approximately 75 percent of these marketing channels. Roadside stands 

and farmers’ markets accounted for about 80 percent of the direct-toconsumer outlets used by farmers. According to the 2008 ARMS, farmers 

selling local food at farmers’ markets traveled an average 30.7 miles, driving 

past the nearest town of 10,000 residents to their destination, suggesting 

that small towns may not generate enough consumer demand to support 

farmers’ markets.7 Onfarm stores and CSAs were used much less frequently. 

Intermediated outlets accounted for the remaining 25 percent of local food 

marketing channels used by farmers. 

7 Farmers travel 30.7 miles to their 

farmers’ market, on average, whereas 

their nearest town of 10,000 or more 

residents lies 5.3 miles away. The 

median distance traveled to a farmers’ 

market was 15 miles, while the maximum distance was 275 miles.

Table 2

Local food marketing channels used, by farm size

Farm size

Sales channels

Small

(sales of less than 

$50,000) 

Medium 

(sales of $50,000- 

$249,999) 

Large

(sales of $250,000

or more) All

Number

Local food sales outlets used 121,198 15,202 5,301 160,795

Average number of outlets 

used per farm 1.4 1.7 2.1 1.5

Percent

By marketing outlet 100.0 100.0 100.0 100.0

Direct-to-consumer outlets 78.0 70.7 55.5 75.3

Roadside stands 34.1 24.9 23.7 31.8

Farmers’ markets 34.6 25.9 14.7 31.8

Onfarm stores 8.3 17.4 15.7 10.4

CSAs 1.1 2.5 1.4 1.3

Intermediated outlets 22.0 29.3 45.0 24.7

Grocers and restaurants 17.2 26.0 23.7 19.2

Regional distributors 4.8 3.4 21.4 5.5

CSAs=Community-supported agriculture.

Source: USDA, Economic Research Service calculations based on 2008 Agricultural Resource Management Survey, conducted by 

USDA, National Agricultural Statistics Service and Economic Research Service.
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As the size of local food sales farms increases, the frequency of farms selling 

through direct-to-consumer marketing channels declines and the frequency of 

sales through intermediated marketing channels increases. Small local food 

farms are three times more likely to use direct-to-consumer outlets than intermediated outlets (see table 2). With larger sales, large local food sales farms 

divide local food sales in a 55-45 split between direct-to-consumer and intermediated marketing channels. Reducing direct-to-consumer marketing likely 

reduces marketing costs for these large farms.

Consumers may acquaint the public face of “local foods” with farmers using 

direct-to-consumer outlets because they represent most producer-consumer 

interactions (see table 2). Small- and medium-sized farms account for 95 

percent of direct-to-consumer local food sales farms. In a variety of surveys, 

consumers reported that consumer-farmer interactions and consumers’ 

desires to support local producers were as important as the quality of the 

commodity (Hunt, 2005; Brown and Miller, 2008; Thilmany et al., 2008). 

Direct-to-consumer marketing channels, however, are not how most local 

foods are purchased; at least 60 percent of the value of local food sales 

passed through intermediated channels dominated by large food farms 

(see table 1). Could consumer interactions with small and medium farmers 

at direct-to-consumer outlets have translated into increased local foods 

purchases at grocery stores and restaurants? The popular press assumes this 

to be the case, but the extent to which consumer-farmer interactions at directto-consumer outlets have influenced retail purchases of local foods has yet to 

be tested empirically. 

What Commodities Are Being Produced 

for Local Food Sales?

Just as marketing outlets vary, commodities produced by local food farms 

differ from all U.S. farms. Local food farms principally produce fresh vegetables, fruits, and nuts, contrasting with traditional farm production, which is 

principally composed of livestock and program commodity crop production. 

According to the 2008 ARMS, vegetable, fruit, and nut farms represented 

almost 6 percent of the 2.1 million farms, yet they accounted for 43 percent 

of all local food farms and generated $3.0 billion, or 65 percent, of total sales 

of locally grown food.8 While only 5 percent of all farms engaged in local 

food sales, about 40 percent of vegetable, fruit, and nut farms sold through 

local food channels. That is, a vegetable/fruit/nut farm is eight times more 

likely to sell food commodities locally than other farms. 

Vegetable, fruit, and nut farms also rely more on local food sales to generate 

gross farm sales than field crop or livestock farms. Among all local food 

farms, local food sales account for 65 percent of gross farm sales for fruit, 

vegetable, and nut farms, on average, but only 37 percent for livestock and 

field crop farms. Excluding local food sales farms marketing solely through 

intermediated channels, vegetable, fruit, and nut farms grossed $32,000 per 

farm in local food sales in 2008 compared with $13,800 per farm for field 

crops and livestock farms.9

Vegetable, fruit, and nut farms participate at varying levels in the three 

major marketing channel combinations, but they account for the largest share 

8ARMS classifies farms into 19 production types according to the agricultural 

commodity that accounts for at least 

50 percent of farm sales. For more 

information, see USDA, NASS, 2009. 

For the purposes of this study, we aggregated all food farms into three basic 

categories: fruit/vegetable/nut farms, all 

other field crop farms, and farms producing livestock and livestock products.

9Including farmers who use only 

intermediated channels would skew the 

distribution of average sales per farm 

even further. Among those local food 

sales farms that rely solely on intermediated marketing channels, vegetable, 

fruit, and nut farms grossed $509,400 

per farm compared with $105,900 per 

farm for field crops and livestock farms. 
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of sales in each combination. Vegetable, fruit, and nut farms represent 40 

percent of farmers using direct-to-consumer sales exclusively, 25 percent 

of farmers using intermediated channels exclusively, and almost 60 percent 

of farmers using both types of marketing channels. Vegetable, fruit, and nut 

farms generate roughly 60 percent of all local food sales that pass through 

direct-to-consumer and intermediated channels, and over 70 percent of local 

food sales marketed by farms using both types of channels. 

The disproportionate presence of vegetable, fruit, and nut farms among all 

local food farms shapes the typical profile of local food sales farms. These 

farms operate fewer acres while generating higher gross sales per acre than 

field crop or livestock farms.10 The average local food sales farmer grows 

high-valued food commodities on 149 acres that yield, on average, $590 per 

acre in sales. In contrast, the operator of the average farm generates $304 in 

sales per acre on 392 acres. 

Comparing Farms That Market Local Foods 

With Farms That Do Not Market Local Foods

By some measures, a higher percentage of farmers who market local foods 

appear to devote more time to farming as an occupation than is the case for 

farmers who do not market local foods. In particular:

•	Primary operators of local food sales farms are 30 percent more likely to 

list their primary occupation as farming (table 3). Small local food sales 

10Vegetable, fruit, and nut farms, on 

average, generate $1,338 per acre in 

sales on 76 acres—four to six times the 

revenue per acre on a farm that is 33-50 

percent the size of the average field 

crop or livestock farm. Average gross 

sales per acre ranges from $640 per 

acre for vegetable, fruit, and nut farmers using direct-to-consumer outlets 

only to $1,310 per acre for those using 

both direct-to-consumer and intermediated outlets, and to over $3,100 per 

acre for those relying exclusively on 

intermediated outlets.

Table 3

Farms that have local food sales compared with those with no 

local food sales

Item

Farms with 

local food sales

Farms with no 

local food sales

Primary operator characteristics: 

Age of the primary operator 57.2 57.8

Women as primary operators 

(percent of all farms) 10.2 10.5

Beginning farmers (with 10 years or less 

experience—percent of all farms) 25.4 23.3

Age first became farm operator* 33.7 31.7

Years of experience as an operator ** 23.4 25.9

Years of education*** 14.0 13.2

Internet use (percent of farms)* 69.9 63.4

Measures of operator commitment to farming:

Farming as primary occupation 

(percent of farms)*** 58.3 44.6

Full-time equivalent operator jobs per farm*** 1.3 0.9

Either one or both spouses work in off-farm 

jobs (percent of farms) 57.3 61.4

Average off-farm labor income (dollars)* 36,739 44,196

Note: Difference-of-means test statistics (t) were calculated for each variable. *Statistically 

significant at the 10-percent level. **Statistically significant at the 5-percent level. ***Statistically 

significant at the 1-percent level. 

Source: USDA, Economic Research Service calculations based on data from the 2008 

Agricultural Resource Management Survey, conducted by USDA, National Agricultural 

Statistics Service and Economic Research Service.
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farm operators are 50 percent more likely to do so, but this difference 

disappears for large local food sales farm operators. 

•	Household members of farms marketing local foods devote more time to 

farm operation than do household members of farms that do not market 

local foods. Local food sales farms devote 40 percent more operator work 

time to farming—filling 1.3 operator full-time-equivalent jobs (1 FTE 

equals 2,000 hours worked annually) compared with a 0.9 operator FTE 

job for the average farm. 

•	Farm households that sell local foods earn 17 percent less, on average, 

in off-farm labor income than average farm households that do not sell 

local foods. 

These measures suggest that the occupational and time commitments to 

farming are valued more by local food sales farm households than the foregone labor income they could have earned off farm.

To examine how local food farmers’ commitment may translate into 

increased farm business viability, we compared two financial performance 

measures between local food farms and farms without local food sales: 

•	Farms earning positive profits.

•	Mean operating expense ratios.

The same share of farms with and without local food sales earned positive 

profits. For the lowest and highest sales classes, we found some statistical 

evidence of differences in mean operating expense ratios (defined as total 

cash expenses divided by gross cash income); however, they were not 

detected for the sample as a whole. Once farmers pass $10,000 in annual 

gross sales, operating expense ratios of farms engaged in local food sales 

may be lower than the average farm not engaged in local food sales, implying 

that local food sales farms may reach profitability at a lower gross sales point.

When comparing other farm operator characteristics, we detected differences in experience and education between farm operators who market local 

foods and those who do not market local foods. Operators of local food sales 

farms have an average of 2 years less experience in farming, and they started 

farming 2 years later in life than the average farmer (see table 3). Local food 

sales farm operators have completed an average of 1 more year of education 

and are about 6.5 percent more likely to use the Internet. We did not find 

significant differences in operator characteristics with regards to gender, the 

average age of the primary operator, beginning farmers as a percentage of all 

farmers, or whether one or both spouses worked in off-farm jobs.
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Location of Local Food Sales Farms

To better understand where local food sales farms are located, we supplemented our analysis of the 2008 ARMS data with direct-to-consumer sales 

data from the 2007 Census of Agriculture. Modeling direct-to-consumer 

sales with Census of Agriculture data allows us to examine the location of 

production, while controlling for other factors (e.g., urbanization and cropland availability).11 There were not enough respondents with local food sales 

in the 2008 ARMS sample to model local food production.

We found that proximity to a metro area, access to farmers’ markets and 

farmland, and being in the coastal regions of the United States are drivers 

of direct-to-consumer sales, but we cannot say much about consumer-side 

drivers of direct-to-consumer sales (e.g., demographics). Our results suggest 

that local food sales have the greatest potential for economic development 

in specific places and regions of the country. These results are consistent 

with prior research also using the 2007 Census of Agriculture (Vogel and 

Low, 2010).

Local Food Sales in U.S. Regions

Local food sales vary regionally. Direct-to-consumer sales are highest in 

the Northeast, on the West Coast, and around a few isolated metropolitan 

areas throughout the country (fig. 3). Even after controlling for urbanization, our analysis of direct-to-consumer sales suggests that such sales are 

significantly higher on the West Coast and in the Northeast.12 This result 

correlates with the evidence on both direct-to-consumer sales and intermediated sales from 2008 ARMS data and other research (USDA-AMS, 2009). 

Direct-to-consumer sales analysis suggests that some factors affecting the 

supply of direct-to-consumer sales are influenced by neighbors, while others 

are regionally influenced. For example, farms with direct-to-consumer sales 

are most likely to have neighbors who also participate in direct sales—this is 

a neighborhood effect rather than a regional effect. Direct-to-consumer sales 

are highest in regions that produce more fruits and vegetables—a result likely 

driven by the geographic suitability for growing fruits and vegetables (e.g., 

regional climate, topography, and infrastructure). 

According to 2008 ARMS data, farms on the West Coast (California, 

Oregon, and Washington State) with local food sales accounted for only 7.8 

percent of all local food sales farms, but they accounted for 23.8 percent 

of all local food sales and 31.4 percent of all local foods sales of fruit, nut, 

and vegetable sales. Recognized for its varied microclimates, long growing 

season, and extensive irrigation networks, the West Coast supplies the Nation 

with 56 percent of all vegetables, fruits, nuts, and other specialty crops. 

The West Coast has a long-standing system of farmers’ markets and farmerto-grocers’ marketing channels dating back to the 1970s. Small-scale farmers 

began selling organic and high value-added niche foods to upscale restaurants in the late 1970s (now a national trend) and are now part of farm-toschool marketing arrangements. Another U.S. hot spot for local food sales 

is the Atlantic seaboard, particularly the Northeast census division. Local 

food sales farms in the Northeast generated 14.4 percent of U.S. local food 

11We estimated county-level and 

commuting-zone level spatial econometric models of factors correlated 

with direct-to-consumer sales and its 

location using 2007 Census of Agriculture data on direct sales as a dependent 

variable. For a detailed discussion 

about the econometric analysis, see 

Appendix 2.

12This result would likely be even 

stronger if intermediated sales were 

included in Census of Agriculture data.
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production. Regional differences in local food sales may be explained by the 

availability of logistics and distribution infrastructure (e.g., King et al., 2010). 

Direct-to-consumer and intermediated food sales’ marketing practices differ 

between regions. West Coast local food sales farms are more likely than 

those in the Northeast to be large farms located farther from metro areas. As 

a result, these farms predominantly market through intermediated marketing 

outlets, which are less time and effort intensive than direct-to-consumer 

marketing outlets. Indeed, 85 percent of West Coast local food sales occurred 

through intermediated channels. Local food sales farms in the Northeast tend 

to be smaller, located closer to densely populated urban markets, and more 

likely to use only direct-to-consumer marketing outlets.

The share of local food producers who are beginning farmers with 10 years 

of experience or less also varies regionally and is highest in the West. Fortyeight percent of West Coast local food producers are beginning farmers as are 

28 percent of Northeast local food producers, both higher than the national 

share of 24.3 percent. More beginning farmers may be driven by high local 

food demand, but without data on the same producer over time, it is difficult 

to understand why more beginning farmers are located on the West Coast and 

in the Northeast. 

Figure 3

Value of direct-to-consumer sales, by county, 2007

Median sales or less

$123,000 up to $1 million

$1 million up to $2.5 million

$2.5 million or more

Not available/disclosure issues

Legend

Source: USDA, National Agricultural Statistics Service; 2007 Census of Agriculture.
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Local Food Sales Highest in Urban Areas

According to the 2008 ARMS, over half of all farms with local food sales 

were located in metropolitan counties, compared with only a third of all 

U.S. farms.13 Using Census of Agriculture data on direct-to-consumer sales, 

we found that even when controlling for region-specific factors, the dominance of direct-sales farms in metropolitan counties holds (see “Appendix 

2: Modeling Direct-to-Consumer Sales”), suggesting that proximity to urban 

markets is strongly related to the production of directly sold goods. There 

were not enough observations in the ARMS data to test metropolitan dominance, but tabular evidence suggests this result would not change if intermediated sales were included.

The dominant metro location of direct-sale farms could be driven by 

demand-side factors (e.g., access to thickly populated markets and farmers’ 

markets) or by supply-side factors (e.g., access to labor, agricultural land, 

or transportation networks). Our analysis suggests that, all other factors 

being equal, both demand-side factors and supply-side factors, including 

regional production of fruits and vegetables and availability of tillable land, 

affect direct-to-consumer sales at the county and commuting-zone level. Our 

evidence did not find a clear correlation between consumer characteristics 

(population demographics) and direct-to-consumer sales, however.

More than 50 percent of small local food sales farms were found in metro 

counties and 30 percent in rural counties adjacent to metro counties, while 

nonlocal food sales farms were, on average, more equally distributed across 

metro, adjacent rural, and remote rural counties. On the demand-side, metropolitan concentration gives producers access to the urban local food sales 

markets essential to their economic viability. On the supply-side, concentration near urbanized areas may be the result of urban development pressures on land prices leading to the dissolution of large farms. As such, the 

remaining farm operations are smaller and must produce higher valued, niche 

agricultural commodities (Heimlich and Anderson, 2001; Nickerson, 2001). 

Larger local food farms are more likely to be located in remote, nonmetropolitan areas. ARMS estimates show that 50.1 percent of large farms with 

local food sales were located in nonmetro counties not adjacent to metropolitan areas—only 32.6 percent of all large farms were located in these remote 

counties—and there were far fewer of these large local food farms.

Our analysis of direct-to-consumer sales data from the Census of Agriculture 

suggests that, while controlling for urbanization, the availability of farmland and other costs may drive location decisions of farms with direct-toconsumer sales. The availability, cost, and quality of labor may affect local 

food sales farms because they are more labor-intensive than comparable 

farms not engaging in direct sales. Fruit and vegetable farms with local food 

sales employed 61,000 workers in 2008, or 13 FTE employees per million 

dollars of sales, while fruit and vegetable farms not engaged in local food 

sales employed only 3 FTE employees per million dollars of sales. 

13We used the Office of Management and Budget’s 2003 definition of metropolitan counties in 

this analysis. Counties outside but 

adjacent to a metropolitan statistical area are referred to as adjacent

counties. Remote counties are those 

outside of and not adjacent to a 

metropolitan area.
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Conclusion

The 2008 ARMS data provide a broader picture of farms engaged in 

marketing local foods. By assessing both direct-to-consumer and intermediated sales, we can develop a more complete picture of local food markets 

and producers. Local food sales via intermediated marketing channels are an 

important component of the industry that has not previously been extensively 

studied. 

We found that small farms with gross sales under $50,000 accounted for 

81 percent of local food sales farms and were more likely to use direct-toconsumer marketing channels, such as farmers’ markets and roadside stands, 

exclusively. Making up 14 percent of all local food sales farms, mediumsized farms were equally likely to use only direct-to-consumer marketing 

channels or a mixture of direct-to-consumer and intermediated marketing 

channels, with only 11 percent using intermediated channels exclusively. 

Combining marketing channels may represent the appropriate market 

strategy for medium-sized farms to thrive. Large farms represented 5 percent 

of all local food sales farms. Most local food sales by large farms were 

marketed by those exclusively using intermediated channels. In doing so, 

these farms were able to reduce labor expenses per dollar of sales by leaving 

the labor-intensive distribution of local foods up to intermediaries.

According to the 2008 ARMS, for small and medium local food sales farms, 

more primary operators identified their primary occupation as farming and all 

operators devoted more work time to production than similarly sized farms 

without local sales. 

Our model of the location of producers with direct-to-consumer sales and 

the analysis of direct-to-consumer and intermediated local food sales with 

respect to location indicates that local food sales are a regional phenomenon 

and that marketing practices vary among regions. Controlling for various 

production factors, direct-to-consumer sales were highest in and near urban 

areas and production likely depended on the availability of labor, tillable 

land, and the market infrastructure essential for direct-to-consumer sales. 

Policy decisions that foster local food sales must account for the importance 

of vital, but unalterable, regional characteristics, such as climate, water availability, and access to densely populated markets, which affect the viability 

of local foods as an economic development tool. Findings suggest that local 

food sales have the potential for community economic development in 

certain areas of the country, particularly those close to urban areas.

While the findings of this study provide additional quantitative information 

at the national level on farmers engaged in local food marketing, the 2008 

ARMS data are not without problems. These data are not comparable with 

previous USDA direct sales estimates. Additionally, we do not have dynamic 

data that might enable us to understand the tenure and success of local food 

sales farms. Further work is necessary to understand the profitability of local 

food sales farms and noneconomic reasons for direct-to-consumer marketing.

Improving data collection methods on local foods occurs iteratively given 

the time span between developing and refining the current year’s ARMS 
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questionnaire. The 2009 ARMS questionnaire restored the phrase “for human 

consumption.” The 2010 ARMS separates the value of direct-to-consumer 

sales from intermediated marketing sales and, for the first time, also includes 

institutional sales as part of the intermediated marketing category. These data 

sets are available to researchers.
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Appendix 1—Developing Data on Marketing 

Local Agricultural Products Using the 

2008 ARMS

The 2008 ARMS is the first nationally representative survey to query 

farmers about the local marketing channels they used to sell their agricultural 

commodities. The intent of the questions in the 2008 ARMS questionnaire 

was to focus on marketing local foods (USDA/NASS, 2009). However, the 

design and structure of the questions created obstacles to deriving estimates 

that were both internally and externally consistent. Internal consistency 

means that tabulated responses to one or more questions must be consistent with tabulated responses to subsequent questions. External consistency 

means that the 2008 ARMS must produce estimates on direct-to-consumer 

sales of food as close as possible to estimates generated from the 2007 

ARMS and 2007 Census of Agriculture when attempting to measure the 

same phenomenon. 

Results for questions 21-28 of Section I, “Farm Management and Use of 

Time,” of the 2008 ARMS questionnaire are presented in appendix table 1. 

To see the exact wording of each question, refer to the end of this appendix. 

If a respondent answered “yes” to questions 21a or 21b, the respondent 

proceeded to the remaining questions in the module. Question 22 asks the 

farmer a set of questions related to the commodities sold and if processing 

was required before sale. Question 23 queries which marketing channels the 

farmer used to sell a product. Question 23 allows us to distinguish between 

direct-to-consumer and intermediated sales. Question 24 asks what share of 

total farm sales were marketed through any of the channels listed in question 

23, which allows us to calculate the value of local sales. Questions 25-28 

asks the farmer about other practices indirectly linked to sales marketed in 

the channels listed in question 23.

We used the farmers’ positive responses to questions 23a-23e and 23g to 

construct an estimate of the number of farmers selling farm goods “locally” 

to consumers. Responses to question 23f on State branding of farm products 

were excluded because State-branded products are often marketed nationally, 

perhaps even internationally. According to appendix table 1, the number of 

farms using one or more marketing channels accounted for 134,200 farmers 

marketing $8.0 billion of food and nonfood products through 199,000 directto-consumer (23a-23d) and intermediated outlets (23e, 23g). 

Since question 21a does not include the phrase “for human consumption” or 

specify if local channels were used, positive responses to this question generated an estimated 280,100 farms selling farm goods directly to consumers—

more than twice the number of farmers who reported direct sales for human 

consumption in the 2007 ARMS and the 2007 Census of Agriculture. This 

means that 145,900 farmers reported selling farm output to consumers in 

2008 but failed to specify their marketing channels and the volume of direct 

sales. Two possibilities may explain this result. First, responses to question 

21a may capture sales of food and nonfood products without distinguishing 

between local and national direct marketing channels, such as Internet or 

mail-order sales. Second, responses to this question may capture point sales 

to consumers by farmers whose sales were too low to report using direct 
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marketing supply chains. These farmers may be just “testing the waters.” 

Hence, this question by itself adds no useful information and was omitted 

from our evaluation. Question 21b also suffers from this same lack of 

marketing-channel and geographic specificity and was not used.

The 2008 ARMS survey does not allow the researcher to separate out the 

value of sales of food and nonfood products sold directly by a respondent, 

forcing us to rely on indirect methods to estimate the value of food sales. 

We used the Economic Research Service (ERS) farm production typology 

to separate out direct sales farms selling food products and those selling 

nonfood products. The ERS farm typology categorizes farms by commodity 

if a particular commodity accounts for at least 50 percent of farm sales. 

Farms categorized as “Nursery and Cut Tree Farms” were classified as 

nonfood farms and accounted for over 95 percent of direct nonfood sales.1

Excluding this category generated 107,200 estimated famers marketing $4.8 

billion in local food products through 160,000 channels (see appendix table 1).

As a result, 2008 ARMS estimates of farmers engaging in local food sales 

appear similar to estimates from the 2007 ARMS and 2007 Census of 

Agriculture. That the estimated value of local food sales is four times higher 

than previous estimates suggests that new information in the 2008 ARMS 

provides a broader, more complex picture of farmers engaged in direct 

marketing of local foods.

The 2008 ARMS did not collect data linking the value of sales to the use of 

a particular marketing channel. Question 24 asked for the volume of sales 

associated with any of the channels listed in question 23. The design of this 

question forced us to adhere to a strict trichotomy, grouping sales by exclusive use of direct-to-consumer outlets, exclusive use of intermediated channels, or marketing simultaneously through both channels (see appendix table 

1). If we were to try to tease out the value of local food sales by marketing 

channel, we would encounter problems with double counting, confidentiality, and statistical reliability. For those farms using both types of marketing 

1Although nurseries and cut tree farms 

can and do sell food items directly 

to consumers, we have no way of 

quantifying their food sales separate 

from direct sales of nonfood products 

in the 2008 ARMS. According to the 

2007 ARMS, 3,400 nurseries and cut 

tree farms, or 7 percent of all nursery 

and cut tree farms, sold $171 million 

of food products directly to consumers. For this small segment of nursery 

and cut tree farms, direct sales of local 

food are important to the viability of 

their farm operations, accounting for 

34 percent of total gross sales. For the 

sector as a whole, however, direct sales 

of food represent less than 2 percent 

of total gross sales of nursery products 

and cut trees. Less than 2,000 farms 

sold directly to consumers and were 

also categorized as primarily producing 

nonfood products, such as horses, other 

live animals not for meat consumption, 

and aquaculture. These farms accounted for very little of the direct sales 

reported in question 23.

Appendix table 1

Direct sales farms, by farm size, 2007 Census and 2007 and 2008 ARMS

Data source

Number of direct 

sale farms Percent of all farms

Gross value

of direct sales

Percent of all 

farm sales

Thousands Percent

Millions

(nominal dollars) Percent

2007 Census of Agriculture 136.8 6.2 1,211 0.4

2007 ARMS 115.5 5.3 1,292 0.6

2008 ARMS – all farms 134.2 6.3 8,058 3.2

2008 ARMS – local food sales farms 107.2 5.0 4,806 1.9

 Direct-to-consumer 71.2 3.3 877 0.4

 Intermediated 13.4 0.6 2,715 1.0

 Both marketing channels 22.6 1.1 1,198 0.5

Source: USDA, National Agricultural Statistics Service 2007 Census of Agriculture; 2007 and 2008 Agricultural Resource 

Management Survey, Version 1, conducted by the National Agricultural Statistics Service and Economic Research Service.
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channels, the data did not allow us to quantify the contribution each type of 

marketing channel makes to overall farm performance. 

Optimal marketing strategies are of particular importance when assessing the 

viability of the medium-sized farms that make the greatest use of both types 

of channels. Clancy and Ruhf (2010) pointed out that without direct and 

intermediated marketing channels, sufficient scale economies for mid-sized 

farms cannot be achieved. 

The data presented additional challenges for our study, namely our ability 

to distinguish how local food sales through the Internet may be tied to our 

estimates of intermediated sales.2 As described in Martinez et al. (2010), the 

term “local food sales” does not include any geographic reference. Only 15 

percent of the respondents reporting direct sales responded in question 28

that they used the Internet to directly market their products. Because there 

was no follow-up question related to the volume of Internet sales, responses 

to question 28 remain ambiguous and we cannot estimate the volume of 

Internet sales among the 15 percent of farms indicating local food participation. As Internet use becomes more ubiquitous in all facets of farm operations, a set of questions will need to be included that identify Internet sales as 

a distinct marketing outlet. 

2For direct-to-consumer sales, we 

avoided this issue by counting only those 

producers and the value of their sales 

linked to the specific place-based direct 

marketing channels in question 23.
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MANAGEMENT PRACTICES

21. Next, I have some questions about your marketing practices. Are you currently using... CODE

a. direct sales to consumers?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1151

b. sales to retail outlets?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1152

c. advisory services?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1153

d. options?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1154

e. futures?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1155

f. on-farm storage?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1156

g. contract shipping (hiring the hauling of your products)?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1157

h. collaborative marketing or networking to sell commodities?. . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1158

i. farmer owned co-ops?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1159

ENUMERATOR NOTE: [If 21(a) = 1 or 21(b) = 1 continue, otherwise go to Item 29]

22. In 2008, did you sell products originating from: 

CODE

a. crop production other than nursery or floriculture products?. . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1160

 (i) did you have these products processed prior to sale?. . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1161

b. nursery and floriculture production? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1162

c. livestock production? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1163

 (i) did you have these products processed prior to sale?. . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1164

 (ii) if meat products were sold, did you sell by “cut” of meat?. . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1165

 (iii) if meat products were sold, did you sell by “fraction of the animal”?. . . . . . . . . . . . . . . . YES = 1 1166

d. poultry production? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1167

 (i) did you have the birds processed prior to sale?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1168

e. other animal production? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1169

SECTION I continued on next page.

- 27 -

2008 ARMS Questionnaire Questions
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23. Did you use any of the following outlets to market these products (Item 22): 

CODE

a. roadside stand or on-farm facility (exclude on-farm store)? . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1171

b. on-farm store? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1172

c. farmer’s market? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1173

MILES

(i) If product was sold through a farmer’s market, what was the distance to 

 the market where the majority of the product was sold?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1174

CODE

d. Community Supported Agricultural (CSA) buying club? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1175

PERCENT

(i) If product was sold through a CSA buying club, 

 what percent of the households/families participating in the club reside in the 

 same county as your farm?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

1176

e. regional distributor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1177

f. State branding program? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 1178

g. direct sales to local grocery stores, restaurants, or other retailers?. . . . . . . . . . . . . . . . . . . . . YES = 1 1179

PERCENT

24. If product was sold through any of the marketing outlets in Item 23, what percent 

 share of total farm sales did these outlets represent in 2008?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

1180

CODE

25. If you sold livestock, poultry, or other animal products (slaughtered animals, milk, 

 cheese, etc.) through any of the above marketing outlets, were facilities available 

 locally (within 50 miles) for processing?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 

1181

26. Did you collaborate with other farmers to market these products?. . . . . . . . . . . . . . . . . . . . . . . . YES = 1 

1182

27. Was your product sold as a farm or regional brand?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES = 1 

1183

28. Did you use the Internet or mail order to market any of these products?. . . . . . . . . . . . . . . . . . . YES = 1 

1184
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Appendix 2—Modeling Direct-to-Consumer 

Sales

Prior research has raised many questions about the production and consumption of local foods, but relatively few answers exist (Brown, 2002; Lev and 

Gwin, 2010). Are local foods production and consumption in equilibrium? 

Are farmers producing local food for nonmarket reasons (e.g., enjoyment, 

environmental impact, or as an entry into national marketing)? Data on 

direct-to-consumer sales drawn from the 2007 Census of Agriculture does 

allow us to address factors correlated with supply and demand for local food 

and its spatial location. We based our conceptual model on the Brown et al. 

(2006) county-level model of direct sales for West Virginia and extended it 

to encompass all U.S. counties and commuting zones, while accounting for 

detected spatial autocorrelation. 

We found that direct sales are driven by immobile spatial factors correlated 

with growing conditions and marketing opportunities. Results of our countylevel and commuting zone-level models supported the regional analysis 

of 2008 ARMS data and also provided additional evidence of the patterns 

detected in the ARMS data. Model results enabled us to draw more datadriven conclusions than the ARMS data alone would have yielded due to 

geography and the number of observations in the Census of Agriculture that 

cannot be matched in the ARMS. 

Data

Most studies on the determinants of local foods use survey data (e.g., Morgan 

and Alipoe, 2001; Lyson and Guptill, 2004; Brown et al., 2007), and a few 

have used secondary data (Thilmany and Watson, 2004; Brown et al., 2006). 

We attempted to model local food sales using 2008 ARMS data, but the 

sample size was too small to generate a statistically significant regression, 

precluding testing of the hypotheses. Using ARMS would offer a better 

understanding of intermediated sales. Due to data limitations, however, we 

can only model direct-to-consumer sales. Until additional years’ ARMS data 

are available and we can pool surveys, we use data on direct-to-consumer 

sales to test the location hypothesis, to understand the drivers of local food 

supply and demand, and to provide additional support for our conclusions. 

The dependent variable—direct-to-consumer sales of agricultural products 

for human consumption—comes from the 2007 Census of Agriculture. 

Explanatory variables were drawn from the 2002 Census of Agriculture, the 

2000 U.S. decennial Census, and other publicly available county-level data 

sources. All variable descriptions, sources, and summary statistics are available in appendix table 2 for the county-level model and appendix table 4 for 

the commuting zone model. 

Variables for the commuting zone model differ only for the purposes of 

aggregating county-level data up to the commuting-zone level. We used 

Tolbert and Sizer’s (1996) commuting zones (CZ)—the most recent available—because they represent an alternative areal unit (or zonal objects) 

that might be large enough to capture most direct sales transactions (i.e., 

production and consumption occur within the same areal unit). As a plausible 
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spatial representation of commuting and commerce, commuting zones are an 

improvement over labor market areas because all U.S. counties, including the 

most rural, are included in a CZ. Additionally, the CZ may be a useful observation unit because, according to the 2008 ARMS, many farm households 

with direct sales have one member commuting to an off-farm job—84.8 

percent of small direct sale farms and 59.5 percent of all direct sale food 

farms had at least one member commuting to an off-farm job.

The Model

Our maps and descriptive analysis revealed that substantial variation exists 

in direct sales across the United States. We used regression analysis to 

better understand how location factors are correlated with direct sales, while 

controlling for theoretically appropriate variables. Since data to estimate 

structural supply and demand equations were not available, we estimated 

a reduced-form equation that included supply- and demand-side variables. 

We used dependent and explanatory variables found in Brown et al. (2006), 

changing variables to expand the model from West Virginia to the United 

States. Due to limitations of available data, we cannot include in our models 

all the factors that theory suggests would affect direct sales. The models 

assume market equilibrium exists—that supply equals demand.1 We used 

county-level data as a proxy for local markets and assumed that most 

consumers shop for groceries locally and producers prefer to sell locally 

due to increasing travel costs for both. We expected, however, that some 

producers and consumers traveled outside the county or commuting zone to 

purchase food directly from farmers. To account for this county-to-county 

or CZ-to-CZ spillover, we utilized a spatial econometric model to control 

for any spatial autocorrelation detected in the reduced-form linear (Ordinary 

Least Squares or OLS) regression. 

Hypotheses and Variables

The supply of locally produced food is determined by producers, but we 

know little about producers’ motivation (Martinez et al., 2010). From 

existing literature and ARMS data, we know that local food producers are 

most likely to produce fruits and vegetables and be located in or around an 

urban area (Gale, 1997; Martinez et al., 2010). We hypothesized that:

1. Supply will be higher where there are adequate inputs for production but 

also ample marketing outlets, ceteris paribus. 

We tested this hypothesis with production variables included in the reducedform equations. 

Percentage of cropland in the county (PctCropLand) affects supply, particularly in urbanized counties where the cost of farmland may be high due to 

development pressure. Percentage of farms with direct sales (DSFarms), 

or having neighbors who participate in direct sales, is tied to direct sales 

levels via neighboring local food farms. The share of agricultural sales from 

fruits and vegetables (FruitVeg) suggests how amenable the area is to fruit 

and vegetable production, which is important because fruits and vegetables 

represent most directly sold food. The number of farmers’ markets (FmrMkt) 

represents marketing outlets for producers. We expected all these productionoriented test variables to have a positive relationship with direct sales. 

1Unlike many other markets, quantity 

supplied and quantity demanded are uneven at different points in the calendar 

year and in different locales. In North 

America, supply of local food tends to 

be high mid-summer to autumn, while 

supply is almost zero at other times of 

the year, whereas demand tends to be 

more constant. By examining annual

direct sales levels, we skirt seasonality 

issues. Another unique aspect to local 

foods is the “local” nature of supply. 

Using county-level variables enables 

us to equate local supply with local 

demand, an improvement over State or 

multi-State units of observation. The 

spatial econometric model specification also controls for spatial spillovers 

between neighboring counties.
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We controlled for environmental factors that affect local food supply. 

Temperature (JulyTemp) affects the length of the growing season and the 

crops that can be grown locally. Topography (Topog) can also affect the 

crops being grown locally and the feasible scale of agriculture found in the 

areal unit. For example, modern large-scale agriculture is easiest where more 

relatively flat land is available for cultivation. Thus, we might expect local 

food production to occur in more marginalized lands that are not as attractive 

for large-scale agriculture. Finally, two variables—access to interstate highways (Hwy) and high-speed Internet (Internet)—were used to control 

for built infrastructure, which may affect the supply and marketability of 

local foods.

More research on local food consumers’ characteristics is available than 

on producer characteristics (Brown, 2002; Brown et al., 2006). A body of 

literature posits that local food consumers do not share a particular demographic, but instead are motivated to purchase local foods for the improved 

taste, quality, variety, environmental benefits, and/or to support local farmers 

(Brown, 2002; Martinez et al., 2010). 

Spatial analysis suggests urbanization may be associated with high levels of 

direct sales. We hypothesized that:

2. Local foods sales are higher in metropolitan areas than rural because 

there are more consumers. 

Since county-level consumer data for the United States are not available, 

we used county-level averages of supply-side factors2 to test this urbanization hypothesis, assuming that the supply of local food will equal demand. 

We included dummy variables for metropolitan counties (Metro) and 

nonmetropolitan counties adjacent to metropolitan counties (AdjMetro) with 

nonmetro, nonadjacent counties as the omitted condition. We also included 

dummies for regions because exploratory spatial data analysis found regional 

heterogeneity in direct sales; we included Pacific, Mountain, Midwest, and 

Northeast while the South served as the omitted condition. We included 

population density (PopLand) to control for cross-county population heterogeneity without distortion. Despite a debate in the existing literature on 

whether wealth and income affects local food consumption, we cannot test 

whether demographics or motive affect an individual's desire to purchase 

local foods using county-level means. Rather, we controlled for wealth and 

income to the extent possible by using county-level average wage and salary 

income (AveWS) and median home value (MedHomeValue). Due to the 

nature of commuting zone data (i.e., its aggregation from county-level data), 

we instead used per capita income and the maximum of the median home 

value for commuting zones.

Empirical Methods

We first estimated the models with OLS regression. As is common with 

these models, heteroskedasticity was apparent in the initial results (not 

shown, but available upon request). After applying the White-Huber correction and re-estimating the standard errors, we obtained results that remained 

consistent despite the correction. Multicollinearity was not problematic in 

the model; the variance inflation factors for the explanatory variables were 

2Regressors are represented by average 

county-wide values so that each county 

proxies for the average characteristic 

of the pool of local producers and local 

consumers.
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less than five. Endogeneity commonly exists in simple, reduced-form estimations like ours, so we used lagged explanatory variables where possible 

to reduce this effect. Nevertheless, this endogeneity persists and can cause 

biased, but consistent, parameter estimates, possibly affecting the p-values of 

our hypothesis tests. Endogeneity should not alter coefficient signs, and we 

interpreted our results with due caution.

Given the spatial patterns exhibited as we mapped U.S. direct sales, we tested 

the OLS regression for the presence of spatial processes using the Lagrange 

Multiplier (LM) test. Controlling for spatial processes can reduce statistical 

problems, such as unstable parameters and unreliable significance tests. 

Spatial error processes, or nuisance errors, can occur if spatially correlated 

variables are omitted or the value of adjacent observations move together 

due to common or correlated unobservable variables. Spatial lag processes 

occurred due to some systematic interaction among neighbor areal units. We 

tested for the presence of spatial processes in both the county and CZ model.

Conducting the LM test requires that an appropriate spatial weights matrix be 

selected, and there is very little formal guidance when choosing the optimal 

spatial weights matrix. We used a first-order queen contiguity weights matrix 

due to the nature of spatial dependence and its suitability for use with irregular polygons. The weights matrix is row-standardized to facilitate interpretation and ease computational expense.

For the county model, we found both the lag and error LM tests were significant, so we conducted robust LM tests, according to Anselin (1988). The 

robust LM tests showed preference for the spatial autoregressive (SAR) 

model, suggesting that some sort of systematic county-to-county spatial 

processes exist. When spatial lag processes are unaccounted for in a model, 

the coefficients can be biased and inconsistent, leading to the wrong sign on 

coefficients and invalid hypothesis testing. Spatial dependence of this type 

is most frequently incorporated into models using the spatial autoregressive 

(SAR) lag model:

(1) Y = ρWY + βY + ε,

where ε~ i.i.d., W is an n x n matrix defining spatial unit interaction, and 

the spatial lag process is accounted for using the spatially-weighted dependent variable, WY (Anselin, 1988). 

We repeated the LM test for the CZ regression model and expected different 

results due to the alternate areal unit. The CZ is a larger areal unit that may 

capture county-to-county sales because producers are more likely to market 

within a region that includes their place of work, church, school, etc. Results 

of the LM tests showed that both the lag and error tests were significantly 

different from zero, necessitating robust LM tests. The robust tests showed 

preference for the spatial error model in equation 2. That is, there was no 

systematic spatial pattern, rather nuisance errors and/or omitted variables 

caused the spatial processes exhibited in the CZ model. The theory behind 

the spatial error process is admittedly vague; developing a theory behind 

implementation of the spatial error model is difficult because the errors are 

not due to an underlying process, but rather to a host of micro processes. 
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(2) Y X = + β ε ,

where, ε λ ε μ = + W and μ ~i.i.d.

We estimated the county-level model with equation 1 using generalized 

methods of moments. We estimated the CZ model with equation 2 using 

maximum likelihood estimation. 

Results

Econometric model results suggest that location factors dominate direct 

sales. The coefficient on metropolitan (Metro) counties was large and 

significant in the county-level model (appendix table 3), and the coefficient 

on nonmetro counties adjacent to metro counties (AdjMetro) was positive, 

but smaller, and not statistically different from zero. Results indicate metro 

county direct sales were higher than nonmetro direct sales. Population 

density (PopLand) had a negative and significant coefficient, which taken 

together with the positive coefficient on Metro, suggests that direct sales 

were highest in metropolitan counties with enough open space for producers, 

as opposed to the most densely populated metro areas. Direct sales’ difference among U.S. regions was also evident in the results. Coefficients on 

Pacific and Northeast regions were large in magnitude and statistically 

significant in both models, while controlling for metropolitan and population density. The South was the omitted condition, and coefficients on the 

Midwest and Mountain census regions were not statistically different from 

the South.

Supply-side factors exhibited a significant relationship with direct sales. In 

addition to the urbanization variables, proximity to other direct sales farms 

(DSFarms) and others’ sales of fruits and vegetables (FruitVeg) were significant, suggesting neighborhood characteristics drive direct sales, ceteris 

paribus. The coefficient on the share of cropland (PctCropLand) had a positive sign, suggesting that the availability of tillable land is a driver of direct 

sales. The number of local farmers’ markets (FmrMkt) had a positive and 

statistically significant coefficient. We cannot speak to the direction of the 

relationship between direct sales and number of farmers’ markets, however. 

Similar to the findings from other studies, demand-side factors in our model 

exhibited mixed results. The coefficient on average wage and salary income 

(AveWS) was insignificant. Median home value, a proxy for wealth, had a 

positive and significant coefficient that was very small. These findings affirm 

Brown (2002) and Martinez et al. (2010), suggesting that consumers who 

buy local foods do not fit a demographic, but rather are motivated by ideals 

and preferences—variables we cannot capture with aggregated data.

The county-level model exhibited a systematic spatial bias, which could 

be caused by several things—most likely either the areal unit or the spatial 

weights matrix were inappropriate. Several spatial weights matrices were 

tested to assess the robustness of the first-order queen-contiguity matrix, but 

a systematic (lag) process remained, suggesting that a different areal unit 

might yield better results. LM tests on the CZ model found that using CZs 

eliminated the presence of a robust spatial lag process but detected the presence of a robust spatial error process. Thus, the CZ may be a more appro-
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priate areal unit of observation for direct-to-consumer sales. Some variables, 

however, exhibited a neighborhood effect rather than a regional effect; thus, 

each model has its own merits.3

Results from the CZ model generally affirmed county model results, although 

coefficients on the neighborhood variables were weaker (appendix table 5). 

For example, the coefficient on DSFarms lost statistical significance in the 

CZ model, likely due to less interaction among farms across a CZ than across 

a county and more within unit heterogeneity. Accordingly, we concluded 

that the influence of neighboring farmers participating in direct sales was a 

highly localized neighborhood effect. The coefficient on the share of farm 

sales from fruit and vegetable sales (FruitVeg) was significantly positive in 

both models, suggesting that the decision to grow fruits and/or vegetables 

was based on regional factors, such as climate, topography, and processing 

infrastructure, not on what their neighbors were doing. We found the square 

of farmers’ markets has a large positive coefficient, as expected, suggesting 

that access to markets also drives direct sales in CZs.

As we found in the county-level model, the CZ model suggests that 

producers on the West Coast and in the Northeast were more likely to participate in direct sales than producers in other parts of the country. Metropolitan 

dummy variables were not included in the CZ model because they cannot be 

aggregated up to CZs, so we substituted rural-urban commuting area codes 

for the most urban of the CZs (USDA ERS, 2005). These variables were 

insignificant due to the lack of variance across CZs. For commuting zones, 

we found that population density (PopLand) and the presence of tillable land 

(PctCropLand) was less important statistically, since this areal unit was more 

likely to include farmland and urban places. Income and wealth proxies were 

insignificant in the CZ model, in line with previous research results (Martinez 

et al., 2010).

Much remains to be understood about the affect of production and consumption choices on direct sales, ceteris paribus. This study did not address 

causality, but looked at the characteristics and place/space associated with 

direct sales. Future research with more complete micro data is needed to 

avoid ecological fallacy issues that can arise when using aggregated countylevel (or CZ level) data to test hypotheses about individuals’ behavior. Finally, 

this econometric analysis was conducted with Census of Agriculture data 

on direct-to-consumer sales, which does not include direct-to-retailer or 

intermediated sales.

3This assumption likely holds for some 

regions of the United States, but not the 

country as a whole.
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Appendix table 2

County variable description and summary statistics 

 Variable Mean

Standard 

deviation Minimum Maximum Description

DS 433.8 1550.5 0 42065 Direct sales to individuals for human consumption, 2007 (in $1,000s)

DSFarms 0.064 0.063 0 1 Farms with direct sales over total farms, 2002

PctCropLand 0.305 0.258 0.00003 1 Acres cropland over total acres1 

Internet 0.097 0.296 0 1 Counties with three or more high-speed internet providers2

JulyTemp -0.002 1.007 -2.86 6.5 Mean July temperature, Z score3

Topog -0.003 1.000 -1.19 1.84 Topography, Z score3

Hwy 0.432 0.496 0 1 Dummy, counties containing a portion of interstate highway

FmrMkt 1.723 4.624 0 94 Number of farmers’ markets4

FruitVeg 0.054 0.130 0 0.98 Fruit, nut, and vegetable sales over total sales5

AveWS 20.7 5.719 5.98 68.89 Average wage and salary income (in $1,000s)6

PopLand 223.7 1513.7 0.04 54127 Population density: Persons per square mile7

MedHomeValue 80922 42110 12500 583500 Median home value in constant 2000 dollars7

Metro 0.328 0.470 0 1 Beale code 0,1,2,3, 2003

AdjMetro 0.324 0.468 0 1 Beale code 4,6,8: Counties adjacent to metropolitan counties

Pacific 0.053 0.225 0 1 CA, OR, WA, AK, HI

Mountain 0.091 0.288 0 1 ID, MT, WY, NV, UT, CO, AZ, NM

Midwest 0.341 0.474 0 1 ND, SD, NE, KS, MN, IA, MO, WI, IL, IN, MI, OH

Northeast 0.07 0.256 0 1 ME, NH, VT, MA, RI, NY, PA, CT

1 2002 Census of Agriculture data.

2 1999 Federal Communications Commission data.

3 USDA’s Natural Amenities Scale.

4 USDA, Economic Research Service Food Environment Atlas.

5 2007 Census of Agriculture data.

6 Bureau of Environmental Analysis, Regional Economic Information System.

7 2000 U.S. Census data.

Source: USDA, Economic Research Service estimates.
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Appendix table 3

County regression results

Y=Direct-to-consumer sales

Variable Coefficient Standard error Z-value

DSFarms 2000.4 356.240 5.615 ***

PctCropLand 374.7 80.023 4.683 ***

Internet 258.4 59.428 4.348 ***

JulyTemp -102.1 20.591 -4.960 ***

Topog 33.342 17.314 1.926 *

Hwy 76.590 31.540 2.428 **

FmrMkt 58.103 4.392 13.230 ***

FruitVeg 1274.7 122.052 10.444 ***

AveWS 1.287 3.234 0.398

PopLand -0.107 0.011 -10.111 ***

MedHomeValue 0.002 0.001 3.159 **

Metro 170.6 42.525 4.012 ***

AdjMetro 7.759 35.326 0.220

Pacific 844.3 97.838 8.630 ***

Mountain 70.9 56.513 1.255

Midwest -55.3 40.280 -1.372

Northeast 502.6 74.961 6.705 ***

W_DS 0.187 0.026 7.152 ***

constant -176.7 74.146 -2.383 **

N=2961

Pseudo R-squared 0.422 Log likelihood -23788.7

 *= Statistically significant at the 10-percent level.

 **= Statistically significant at the 5-percent level.

***= Statistically significant at the 1-percent level.

Source: USDA, Economic Research Service estimates.
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Appendix table 4

Commuting zone variable description and summary statistics 

 Variable Mean

Standard 

deviation Minimum Maximum Description

DS 1564.06 3448 0 31054

Direct sales to individuals for human consumption, 2007 (in 

$1,000s)

DSFarms 0.053 0.043 0 0.349 Farms with direct sales over all farms, 2002

PctCropLand 0.29571 0.24836 0.00002 1 Acres cropland over total acres1

Internet 0.61081 0.4879 0 1 Counties with three or more high-speed internet providers2

JulyTemp 0.29137 1.25562 -2.530 6.501 Maximum of mean July temperature, Z score3

Topog 0.34092 1.03461 -1.194 1.839 Maximum of topography, Z score3

Hwy 0.61081 0.4879 0 1 Dummy for interstate highway within CZ

FmrMkt 6.9973 14.9871 0 142 Number of farmers markets4

FruitVeg 1.95723 1.78069 0 11.916 Fruit, nut, and vegetable sales as percent of total sales5

PCI 23858.1 4294.22 12432 47104 Per capita income6

PopLand 101.567 270.888 0.082 5482.17 Population density: Persons per square mile7

MedHomeValue 78562.1 42044 23825 583500 Average of median home value in constant 2000 dollars7

Pacific 0.08243 0.27521 0 1 CA, OR, WA

Mountain 0.12973 0.33623 0 1 ID, MT, WY, NV, UT, CO, AZ, NM

Midwest 0.33784 0.47329 0 1 ND, SD, NE, KS, MN, IA, MO, WI, IL, IN, MI, OH

Northeast 0.05946 0.237 0 1 ME, NH, VT, MA, RI, NY, PA, CT

Midwest 0.341 0.474 0 1 ND, SD, NE, KS, MN, IA, MO, WI, IL, IN, MI, OH

Northeast 0.07 0.256 0 1 ME, NH, VT, MA, RI, NY, PA, CT

1 2002 Census of Agriculture data.

2 1999 Federal Communications Commission data.

3 USDA’s Natural Amenities Scale.

4 USDA, Economic Research Service Food Environment Atlas.

5 2007 Census of Agriculture data.

6 Bureau of Environmental Analysis, Regional Economic Information System.

7 2000 U.S. Census data.

Source: USDA, Economic Research Service estimates.
32

Direct and Intermediated Marketing of Local Foods in the United States / ERR-128

Economic Research Service / USDA

Appendix table 5

Commuting zone regression results 

Y=Direct-to-consumer sales

Variable Coefficient Standard error Z-value

DSFarms 4747.2 3959.1 1.199

PctCropLand 990.6 639.5 1.549

Internet 946.2 266.4 3.552 ***

JulyTemp -399.3 119.8 -3.334 ***

Topog 120.9 117.7 1.027

Hwy 45.1 198.0 0.228

FmrMkt^(1/2) 1165.6 80.6 14.5 ***

FruitVeg 3332.0 739.4 4.5 ***

PCI -0.026 0.044 -0.595

PopLand -0.824 0.420 -1.96 **

MedHomeValue 0.0 0.0 0.16

MinOfBeale 39.405 54.354 0.73

Pacific 2815.7 528.3 5.330 ***

Mountain 345.7 366.7 0.94

Midwest -291.6 304.2 -0.959

Northeast 2289.7 585.9 3.908 ***

lambda 0.2 13.70 ***

constant -1495.5 756.56 -1.9767

N=720

AIC: 13139 Log likelihood -6550.611

 **=Statistically significant at the 5-percent level.

***=Statistically significant at the 1-percent level.

AIC=Akaike Information Criterion.

Source: USDA, Economic Research Service estimates.