Predictors of Supplemental Nutrition Assistance Program Use at Farmers’ Markets With Monetary Incentive Programming

Abstract

Purpose:

Healthy food incentive program implementation targeting people receiving Supplemental Nutrition Assistance Program (SNAP) benefits is supported by the federal Food Insecurity Nutrition Incentive (FINI) grant program. This study examined factors contributing to increased SNAP use at farmers’ markets with an FINI-funded incentive program.

Design:

Implementation evaluation.

Setting:

Sixteen states and District of Columbia.

Participants:

Two hundred eighty-two FINI-funded farmers’ markets open in 2016.

Measures:

Weekly SNAP sales and transactions per 1000 SNAP households in the Zip Code Tabulation Areas around markets.

Analysis:

Two-level hierarchical regression modeling.

Results:

Most farmers’ markets (53%) had less than 100 SNAP transactions in 2016. Weekly SNAP sales and transactions per 1000 SNAP households were 69.9% and 47.7% higher, respectively, if more than 1 incentive was available versus 1. Not having paid market staff resulted in declines in these sales (−34.3%) and transactions (−38.1%) compared to markets with paid staff. There was a 6.2% and 5.1% increase in SNAP sales and transactions for each additional produce vendor. Weekly SNAP sales and transactions were about 2 to 3 times higher in rural areas compared to metropolitan. Clustering of markets within states explained 10% of the variation in weekly SNAP sales and transactions.

Conclusion:

Four implementation factors were identified that may facilitate the reach of SNAP-based monetary incentive programs at farmers’ markets to maximize reach and impact among SNAP shoppers.

Keywords

food security nutrition interventions behavioral economics motivation strategies farmers market population health low income underserved populations specific populations

Purpose

In the United States, food costs influence dietary decision-making and related behaviors in several ways. First, healthier diets tend to be more expensive.¹ Americans with higher income can and do spend more money per kilocalorie on foods compared to those with lower income.² Second, lower income populations may be less willing to take financial risks to purchase new foods such as a healthier option (ie, fruit or vegetable) that might be wasted if taste preferences are not met.³ Third, healthy food choices available for purchase in lower income neighborhoods are often limited, exacerbating cost constraints (ie, travel time).⁴ Taken together, these factors contribute to gradients in diet quality such that each monetary unit increase in spending on food is associated with corresponding increases in conformity to US dietary guidelines.⁵

The Supplemental Nutrition Assistance Program (SNAP) is the largest federal food assistance program in the United States aimed at reducing food cost barriers among low-income populations. Supplemental Nutrition Assistance Program served over 28.8 million households in 2016, the year of the present analysis, providing an average monthly household benefit of $254.61.⁶ There is evidence that Americans who qualify for SNAP benefits and other low-income populations have poorer diet quality.^7
-9 Difference in fruit and vegetable consumption between high- and low-income Americans is one of the main drivers of socioeconomic disparities in overall diet quality.^10,11 In the United States, fruits and vegetables often cost more than other foods.^12,13

There is growing interest in population health interventions designed to improve diet quality by reducing food costs for low-income populations, such as those receiving SNAP. Fruit and vegetable incentives are an example of this type of intervention approach. Interventions offering a 10% decrease in prices of fruits and vegetables have been found to increase produce consumption by 14% and reduce body mass index by 0.04 kg/m².¹⁴ Further evidence suggests SNAP-based fruit and vegetable incentive programs may reduce disparities in cardiovascular disease.^15,16

Farmers’ market specific fruit and vegetable incentive programs are emerging in the United States through support from the federal Food Insecurity Nutrition Incentive (FINI) grant program funded by the US Congress in the Agriculture Act of 2014.¹⁷ These SNAP-based fruit and vegetable incentives are an additional food assistance benefit for people receiving SNAP. The goal of SNAP-based incentive programs is to increase purchasing of fruits and vegetables while also benefiting the local agricultural economy. To achieve population improvements in diet, these SNAP-based monetary incentive programs need to be used by the target population. This is a challenge because farmers’ markets are an alternative food retail space and barriers, such as lack of technology to accept SNAP payments, low awareness of farmers markets and incentive programs, and irregular hours limit use by low-income populations.¹⁸ Nevertheless, evidence suggests farmers’ market use is desirable among some SNAP recipients.¹⁹ However, farmers’ market use overall tends to be more common among populations with higher income.²⁰ Monetary incentive programs are one strategy to address this income gap in farmers’ market use.

Although there is growth in the number of US farmers’ markets offering SNAP-based fruit and vegetable incentive programs, most research in this area is focused on a small number of markets operating similar incentive models in similar contexts.^21
-23 The goal of this research was to examine heterogeneous models of SNAP-based fruit and vegetable incentive programs implemented nationally to identify market, community, and state factors related to improved SNAP use at participating farmers’ markets. This research examines the incentive program as a lever for promoting SNAP use at farmers’ markets. A key barrier to carrying out this kind of research in the past has been lack of a centralized system to collect data related to farmers’ market incentive program implementation. This barrier was addressed in the present study by analyzing data collected through a standardized electronic evaluation platform used at farmers’ markets in 16 US states and the District of Columbia.²⁴

Methods

This study was approved as an exempt protocol by the institutional review board of Case Western Reserve University (protocol no. IRB-2014-903).

Description of Study Sample

This national-scale implementation evaluation included farmers’ markets participating in FINI-funded projects operating in 2016 that were using a shared data collection tool for recording SNAP payments. Technical support for standardized data collection was provided to markets by a national nonprofit, Wholesome Wave.

All markets included in this study offered monetary incentive programming to improve the affordability of fruits and vegetables available at farmers’ markets for people receiving SNAP benefits. In its simplest version, these SNAP-based incentive programs match an expenditure of SNAP benefits with additional funds, which are restricted for fruit and vegetable purchases. In this study, a variety of SNAP-based incentive program models were implemented based on local decision-making. Incentive program models varied in 3 ways. First, there was variability in the incentive matching model. The most common matching model was a 100% match (eg, spend $1 in SNAP, get $1 in incentives). Second, there was variability in the maximum amount of incentive that could be received per SNAP transaction. Third, markets varied in the number of incentive programs offered. All markets had at least 1 SNAP-based fruit and vegetable incentive program. In addition, some markets had other types of incentive programs, not funded by an FINI grant, that served SNAP customers only or other specified populations. One example of an SNAP-related additional incentive was a “close the gap” program that supported SNAP recipients at the end of the month when federal benefits may have run out. This type of program allowed a SNAP recipient to receive the SNAP-based incentive by showing their SNAP card without spending any of their SNAP benefits. Another example is an incentive that provided matching funds for people with Medicaid health insurance, a federally funded insurance serving low-income populations in the United States.

Measures

The main outcomes, measured at the market level, were weekly SNAP sales and number of SNAP transactions standardized per 1000 households receiving SNAP within a participating market’s Zip Code Tabulation Areas (ZCTAs).²⁵ More information about ZCTAs is given subsequently. These variables were computed by summing up daily SNAP sales and daily number of SNAP transactions and dividing this sum by the number of weeks a market was open in 2016. To standardize the outcomes across diverse contexts, these values were further divided by the number of households receiving SNAP per 1000 households within a market’s ZCTA.²⁶ Predictor variables for the models included self-reported characteristics about each market as well as information about the market’s community and state context.

Farmers’ market data were collected via a standardized data collection platform at all participating markets.²⁴ Staff or volunteers at each market were trained to enter data into the platform, and quality control measures were implemented to address data inconsistencies that were resolved by markets. The present analysis is focused on data collected from January to December 2016. Data were exported from the platform in July 2017. A unique market was defined as a market operating at the same address during the year. Among all unique markets using the standardized data collection platform in 2016 (N = 407), data were limited to markets (a) not enrolled in a randomized controlled trial organized by Wholesome Wave that was examining the impact of different incentive models; (b) open 4 days or more in 2016 to exclude holiday markets; (c) had at least 1 SNAP transaction during the year; (d) identified as a farmers’ market versus a farm stand, mobile market, or community supported agriculture; and (e) had SNAP recipients living in the ZCTA around the market. This resulted in a total of 282 farmers’ markets for the present analysis.

Farmers’ market-level variables

Market-level data were entered into the data platform by market staff (eg, the number of weeks a market was open in 2016; whether the market operated on weekend day(s) or weekday(s) or both weekend and weekdays; market manager payment model; number of total vendors at market; and number of fruit and vegetable vendors). Additional dummy variables were assigned for the fruit and vegetable incentive maximum benefit (<$10, >$10, or no maximum) and number of incentive programs at the market (1 vs 2+).

Community-level variables

Community-level variables were calculated based on the market zip code entered into the data collection platform. For each market, we defined a community using ZCTAs.²⁵ Since each market was associated with a zip code rather than a ZCTA, we used a crosswalk to identify ZCTAs for the markets.²⁷ In this crosswalk, a ZCTA may be comprised of one or more zip codes. Using ZCTAs, we linked each market to 5-year estimates data on percent of black residents using the American Community Survey.²⁶ We designated each market as being located in a metropolitan, small town, or rural area using the primary rural–urban community area (RUCA) codes.²⁸ Markets with primary RUCA code 1 to 6 were classified as metropolitan markets, 7 to 9 as small-town markets, and 10 as rural markets.

State-level data

Markets were clustered in 16 states and District of Columbia. The states (corresponding number of markets per state) included diverse regions across the United States: Arizona (2), Connecticut (13), District of Columbia (6), Florida (17), Georgia (33), Hawaii (2), Maine (36), Missouri (2), New Hampshire (17), New Jersey (8), Ohio (49), Rhode Island (19), Tennessee (4), Virginia (25), Vermont (44), Wisconsin (1), and West Virginia (4). State-level variables were related to factors hypothesized to influence SNAP use at farmers’ markets. These included the percentage of individuals in the state meeting the recommended fruit and vegetable intake guidelines as defined by the 2015 to 2020 Dietary Guidelines for Americans²⁹ and dollars of SNAP benefits issued to the state in 2016.³⁰ Additionally, 5 indicators were included to capture state support for local food systems (eg, number of farmers’ markets per 100 000 residents, state-level food policy council, etc).³¹

Data Analysis

All statistical analyses were conducted with R version 3.5.2 (R Core Team, 2018). Baseline characteristics for the study sample were reported using means and standard deviations for continuous variables that were not skewed and medians and interquartile range (IQR) reported for skewed variables. For categorical variables, frequencies and percentages were calculated. To investigate potential market-, community-, and state-level predictors of SNAP utilization, we performed separate multilevel linear regression analyses to examine characteristics associated with (a) SNAP sales and (b) number of SNAP transaction. For continuous variables, annual averages or raw numbers for each market were entered as independent variables into the model, while for categorical variables, annual proportions were entered into the model. Each analysis used a 2-step modeling procedure. In step 1, a series of univariate and correlation analysis were used to examine market- and community-level characteristics that were associated with the outcome of interest. In step 2, 4 models were fit for each outcome.³² The 4 models included model 1: with no predictors and random effect of the intercept (null model); model 2: model 1 plus the farmers’ market variables; model 3: model 2 plus state-level variables; and model 4: model 2 plus the state-level variables with a variance inflation factor below 4.

The model 1 was used to compute intraclass correlation, a measure of variation in the outcomes that is attributable to markets clustering within a state.³³ We used Akaike information criterion (AIC) and Bayesian information criterion (BIC) to assess model fit to the data. To select a parsimonious model among the 5 models, we compared them using ordinary likelihood ratio tests. The general model for the 2-level mixed-effect model is specified as follows:

y_{ij} = β_{0 j} + \sum_{p = 1}^{k} β p_{x} p_{ij} + e_{ij}, e_{ij} \sim N (0, σ_{ij}),

β_{0 j} = γ_{00} + \sum_{j = 1}^{19} γ_{0j} S_{j} + u_{0j}, u_{0j} \sim N (0, σ_{0j}),

where (ij) denotes the ith market in the jth state such that i = 1,…, 282 and j = 1…, 17 and p represents the pth market (and community) level hypothesized predictor for the outcome. Note that in this analysis, y_ij represents the log-transformed outcome (either SNAP sales or number of SNAP transactions) for the ith market in the jth state because of large variability in the 2 outcomes. Due to this transformation of outcomes, model coefficients were interpreted as percentage increase or decrease.³⁴ Specifically, the coefficients were transformed using the expression that follows.³⁴ For all analyses, statistical significance was set at P <.05.

100 \times (e^{β} - 1) .

Results

Descriptive Statistics

A total of 282 farmers’ markets were analyzed. These markets had wide variability in terms of market operations. As shown in Table 1, 62% of the markets had total annual SNAP sales below $2000; 23% reported annual SNAP sales above $4000. Fifty-three percent of the markets had less than 100 SNAP transactions in 2016 and 20% had 300 or more. Among these markets, the median number of weeks open across 12 months was 22 (IQR: 16) and the median number of market vendors per week was 15 (IQR: 19); of these, about 5 were produce vendors. Many of the markets were open on weekends only (41%) or weekdays only (39%). The majority (72%) offered 1 healthy food incentive program, while 28% offered 2 or more programs. There was variability in the maximum amount of FINI-funded incentive benefits provided per SNAP transaction, which were categorized into 3 groups for this analysis, including (a) less than or equal to $10 (37%), (b) more than $10 (36%), or (c) no limit on the maximum incentive benefits (26%).

Table 1.

Descriptive Statistics of Farmer’s Markets Offering SNAP Incentives, United States, 2016 (N = 282).

	Results
Farmer’s market operations
Total annual (2016) SNAP sales, n (%)
<$1000	107 (38)
$1001-2000	67 (24)
$2001-3000	23 (8)
$3001-4000	21 (7)
>$4000	64 (23)
Total annual (2016) number of SNAP transactions, n (%)
<100	150 (53)
101-200	55 (20)
201-300	21 (7)
>300	56 (20)
Annual number of transaction weeks, median (IQR)	22 (16)
Number of vendors per week, median (IQR)	15 (19)
Number of produce vendors per week, median (IQR)	5 (5)
Days market is open, n (%)
Weekday only	109 (39)
Weekend and weekday	55 (20)
Weekend only	118 (41)
Number of incentives at farmer’s market, n (%)
One	202 (72)
Two or more	80 (28)
Spending cap of fruit and vegetable incentive, n (%)
≤$10	105 (37)
>$10	102 (36)
No cap	75 (26)
Staffing status of farmer’s market manager, n (%)
Paid	169 (60)
Unpaid	70 (25)
Don’t know	43 (15)
Farmer’s market context
Number of households receiving SNAP in ZCTA of farmers’ market, median (IQR)	1158 (1867)
Percentage of residents identifying as African American within ZCTA of farmers’ market, median (IQR)	5 (26)
Geographic area type, n (%)
Metropolitan	226 (80)
Rural	26 (9)
Small town	30 (11)
ZCTA area, in square miles, median (IQR)	22 (52)
State level (16 states and Washington, DC)
Percentage of residents meeting recommended fruit intake, mean (SD)	12.3 (2)
Percentage of residents meeting recommended vegetable intake, mean (SD)	9.2 (2)
Number of farmers’ markets per 100 000 residents, median (IQR)	4.2 (5)
Percentage of farmers’ markets accepting WIC, median (IQR)	27.6 (30)
State-level food policy council, N (%)	11 (65)
Number of local food policy councils (mean, SD)	4 (6)
Number of food hubs, mean (SD)	3.9 (3)
SNAP issuance in million dollars, median (IQR)	923 (1130)

Abbreviations: IQR, interquartile range; M, mean; SD, standard deviation; SNAP, Supplemental Nutrition Assistance Program; WIC, Special Supplemental Nutrition Program for Women, Infants, and Children; ZCTA, ZIP Code Tabulation Areas.

Most of the farmers’ markets (80%) were located within a metropolitan area with the median size of the ZCTA around the market of 22 square miles (IQR: 52). In the ZCTA where the markets were located, the median number of households receiving SNAP was 1158 (IQR: 1867) and residents identifying as African American was 5% (IQR: 25%), indicating variability in the sociodemographic context of the participating farmers’ markets. About half of the markets were located in 4 states: Ohio (17%), Vermont (16%), Maine (13%), and Georgia (12%). On average, for the 16 states and District of Columbia, 12% and 9% of the residents met the recommended fruit and vegetable intake, respectively. Median annual SNAP issuance to the state was $923 (IQR: $1130) million per state.

Model Selection

The following results are related to 2 main outcomes including weekly SNAP sales and number of SNAP transactions per 1000 households receiving SNAP within a participating market’s ZCTA. Henceforth, these are referred to as Weekly SNAP Sales and Weekly SNAP Transactions.

As shown in Table 2, the Weekly SNAP Sales model (model 4) with market-level variables and 2 state-level variables had the least AIC and BIC (798.01 and 853.23, respectively) indicating best fit. As shown in Table 3, for Weekly SNAP Transactions, model 4 provided the best fit with the least AIC and BIC (761.51 and 816.73, respectively). The ICC for both Weekly SNAP Sales and Weekly SNAP Transactions was 0.10, which was statistically significant at P < .05. Thus, 10% of the variation of Weekly SNAP Sales and Weekly SNAP Transactions were due to clustering of markets within states.

Table 2.

Estimates From 2-level Mixed-Effect Model of Weekly SNAP Sales ($) in 2016 Per 1000 Households Receiving SNAP Benefits Within a Farmers’ Market ZCTA (N = 282 Farmers’ Markets).

Fixed Effect	Model 1^a	Model 2^a	Model 3^a	Model 4^a
Fixed Effect	Estimate (SE)	Estimate (SE)	Estimate (SE)	Estimate (SE)
Farmer’s market operations
Intercept	4.40 (0.16)	3.69 (0.31)^b	1.81 (9.08)	3.50 (0.46)
Spending cap of fruit and vegetable incentive (ref = $10 or less)
More than $10		0.21 (0.26)	0.59 (0.28)	0.41 (0.27)
No cap		0.20 (0.27)	0.49 (0.29)	0.41 (0.28)
Number of incentives of farmer’s market
Two or more (ref = 1)		0.46 (0.21)^b	0.60 (0.21)^b	0.53 (0.21)^b
Market open days (ref = weekday only)
Weekend and weekday		−0.09 (0.23)	0.06 (0.23)	0.06 (0.23)
Weekend only		−0.002 (0.18)	0.14 (0.18)	0.14 (0.18)
Number of vendors per week		0.02 (0.01)^b	0.02 (0.008)^b	0.02 (0.01)^b
Number of produce vendors per week		0.06 (0.02)^b	0.05 (0.02)	0.06 (0.02)^b
Staffing status of farmers market manager (ref = paid)
Unpaid		−0.43 (0.21)	−0.39 (0.21)	−0.42 (0.20)
Farmer’s market context
Percentage of residents identifying as African American within markets’ ZCTA		−0.02 (0.01)	−0.02 (0.01)	−0.02 (0.01)^b
Geographic area type (ref = metropolitan)
Rural		1.41 (0.31)	1.36 (0.30)	1.42 (0.30)^b
Small town		−0.25 (0.29)	−0.34 (0.29)	−0.25 (0.28)
State context
Percentage of residents meeting recommended fruit intake			0.57 (0.40)
Percentage of residents meeting recommended vegetable intake			−0.85 (0.50)
SNAP issuance in million dollars			0.09 (0.41)
Number of FM per 100 000 residence			0.23 (0.14)
Percentage of farmers accepting WIC Program Vouchers			−0.01 (0.02)
State-level food policy council			0.28 (0.73)	−0.07 (0.48)
Number of local food policy councils			−0.06 (0.07)	−0.01 (0.05)
Number of food hubs			−0.03 (0.09)
Model summary
Random effects
Level 1	1.45^b
Level 2 intercept (state mean)	0.47^b
Model fit
AIC (less the better)	1032.16	827.34	819.49	798.01
BIC (less the better)	1043.08	876.01	895.41	853.23

Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; SE, standard error; SNAP, Supplemental Nutrition Assistance Program; WIC, Special Supplemental Nutrition Program for Women, Infants, and Children; ZCTA, Zip Code Tabulation Area; HLM, Hierarchical Linear Modeling.

^a Model 1: HLM with only random intercept (null model); model 2: model 1 plus the farmers’ market variables; model 3: model 2 plus state-level variables; and model 4: model 2 plus the state-level variables with a variance inflation factor below 4.

^b P < .05.

Table 3.

Estimates From 2-Level Mixed-Effect Model of Weekly SNAP Transactions in 2016 per 1000 Households Receiving SNAP Benefits Within a Farmers’ Market ZCTA (N = 282 Farmers’ Markets).

Fixed Effect	Model 1^a	Model 2^a	Model 3^a	Model 4^a
Fixed Effect	Estimate (SE)	Estimate (SE)	Estimate (SE)	Estimate (SE)
Farmer’s market operations
Intercept	1.64 (0.14)	1.20 (0.28)	−1.68 (6.40)	0.85 (0.44)
Spending cap of fruit and vegetable incentive (ref = $10 or less)
More than $10		0.04 (0.25)	0.48 (0.25)	0.27 (0.25)
No cap		0.007 (0.25)	0.36 (0.26)	0.22 (0.26)
Number of incentives of farmer’s market
Two or more (ref = one)		0.33 (0.20)	0.46 (0.19)^b	0.39 (0.19)^b
Market open days (ref = weekday only)
Weekend and weekday		−0.02 (0.21)	0.15 (0.21)	0.15 (0.21)
Weekend only		−0.05 (0.17)	0.10 (0.17)	0.09 (0.16)
Number of vendors per week		0.02 (0.007)^b	0.01 (0.007)	0.01 (0.01)
Number of produce vendors per week		0.05 (0.02)^b	0.05 (0.02)^b	0.05 (0.02)^b
Staffing status of farmers market manager (ref = paid)
Unpaid		−0.50 (0.20)	−0.45 (0.19)^b	−0.48 (0.19)^b
Farmer’s market context
Percentage of residents identifying as African American within markets’ ZCTA		−0.02 (0.004)^b	−0.01 (0.005)	−0.01 (0.005)
Geographic area type (ref = metropolitan)
Rural		1.12 (0.29)^b	1.04 (0.28)^b	1.13 (0.27)^b
Small town		−0.27 (0.27)	−0.39 (0.26)	−0.30 (0.26)
State context
Percentage of residents meeting recommended fruit intake			0.60 (0.31)
Percentage of residents meeting recommended vegetable intake			−0.86 (0.39)
SNAP issuance in million dollars			0.12 (0.29)
Number of FM per 100 000			0.24 (0.10)
Percentage of farmers accepting WIC program vouchers			−0.02 (0.01)
State food policy council			0.40 (0.55)	0.14 (0.45)
Number of local food policy councils			−0.06 (0.05)	−0.02 (0.04)
Number of food hubs			−0.04 (0.06)
Model summary
Random effects
Level 1	1.29^b
Level 2 intercept (state mean)	0.42^b
Model fit
AIC (less the better)	966.73	795.74	782.73	761.51
BIC (less the better)	977.66	844.41	858.65	816.73

Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; SNAP, Supplemental Nutrition Assistance Program; ZCTA, Zip Code Tabulation Area; WIC, Special Supplemental Nutrition Program for Women, Infants, and Children, SE; standard error; HLM, Hierarchical Linear Modeling.

^b P < .05.

Table 4 provides results of the 2-level mixed-effect models predicting Weekly SNAP Sales. There were 6 statistically significant predictors of Weekly SNAP Sales at the participating farmers’ markets. Weekly SNAP Sales at markets were 69.9% higher if 2 or more incentive programs were available versus only 1. Weekly SNAP Sales increased by 2% for every additional vendor per week and 6.2% for every additional produce vendor. There was a 34.3% decrease in Weekly SNAP Sales at farmers’ markets with an unpaid volunteer serving as the farmers’ market manager compared to a paid staff member serving this role. Weekly SNAP Sales decreased 2% for each percentage increase in the number of households identifying as African Americans in the ZCTA where the farmers’ market was located. Markets located in rural areas had 3 times as many Weekly SNAP Sales as those located in metropolitan areas.

Table 4.

Estimates From 2-Level Mixed-Effect Model Predicting Weekly SNAP Sales and Number of Transactions in 2016 per 1000 Households Receiving SNAP Benefits Within a Farmers’ market ZCTA (N = 282 Farmers’ Markets).

Fixed Effect	Weekly SNAP Sales ($) Model^a		Weekly SNAP Transactions Model^a
Fixed Effect	Coefficient (SE)	Percent Change	Coefficient (SE)	Percent Change
Market operations
Spending cap of fruit and vegetable incentive (ref = $10 or less)
More than $10	0.41 (0.27)	50.7	0.27 (0.25)	31
No cap	0.41 (0.28)	50.7	0.22 (0.26)	24.6
Number of incentives of farmer’s market
Two or more (ref = 1)	0.53 (0.21)^b	69.9	0.39 (0.19)^b	47.7
Market open days (ref = weekday only)
Weekend and weekday	0.06 (0.23)	6.2	0.15 (0.21)	16.2
Weekend only	0.14 (0.18)	15	0.09 (0.16)	9.2
Number of vendors per week	0.02 (0.01)^b	2	0.01 (0.007)	1
Number of produce vendors per week	0.06 (0.02)^b	6.2	0.05 (0.02)^b	5.1
Staffing status of farmers market manager (ref = paid)
Unpaid	−0.42 (0.20)^b	−34.3	−0.48 (0.19)^b	−38.1
Farmer’s market context
Percentage of residents identifying as African American within markets’ ZCTA	−0.02 (0.005)^b	−2	−0.01 (0.005)	−1
Geographic area type (ref = metropolitan)
Rural	1.42 (0.30)^b	313.7	1.13 (0.27)^b	209.6
Small town	−0.25 (0.28)	−22.1	−0.30 (0.26)	−25.9
State context
State-level food policy council	−0.07 (0.48)	−6.8	0.14 (0.45)	15
Number of local food policy councils	−0.01 (0.05)	−1	−0.02 (0.04)	−2
Model summary
Random Effects	Standard Deviation		Standard Deviation
Level 1	1.45^b		1.29^b
Level 2 intercept (state mean)	0.47^b		0.42^b

Abbreviations: SE, standard error; SNAP, Supplemental Nutrition Assistance Program; ZCTA, Zip Code Tabulation Area.

^a Intraclass correlation (ICC) = 0.10.

^b P < .05.

Table 4 also highlights results of the 2-level mixed-effect models predicting Weekly SNAP Transactions. There were 4 statistically significant predictors of Weekly SNAP Transactions. All 4 also were statistically significant predictors of Weekly SNAP Sales. Weekly SNAP Transactions at markets were 47.7% higher if 2 or more incentive programs were available versus only 1. Weekly SNAP Transactions increased 5.1% for every additional produce vendor. There was a 38.1% decrease in Weekly SNAP Transactions at markets with an unpaid volunteer serving as the farmers’ market manager compared to a paid staff member. Markets located in rural areas had about 2 times as many Weekly SNAP Transactions as those located in metropolitan areas.

Discussion

Evidence is growing in support of monetary incentive programs as a health promotion strategy to improve fruit and vegetable consumption among low-income populations. Incentive intervention studies have focused on different food retail environments such as supermarkets, worksite cafeterias, and farmers’ markets and found improvements in the sale of fruits and vegetables at participating retailers and/or improvement in dietary intake or body mass index among participants using the incentives.^35

-39 Moreover, people receiving SNAP benefits report interest in interventions that incentivize healthy foods such as fruits and vegetables.⁴⁰

Given this evidence, results of our study add to the field by identifying implementation factors to maximize the reach of monetary incentive programs among SNAP consumers shopping at farmers’ markets. This is important because results of the present study reveal the overall reach of the FINI-funded SNAP-based incentive programs offered at participating farmers’ markets was low. More than half of the markets included in this study had less than 100 SNAP transactions in 2016. Findings highlight several implementation factors that may facilitate the reach of SNAP-based fruit and vegetable incentive programs at farmers’ markets.

First, findings highlight that location matters. Farmers’ markets in rural communities had 3 times more weekly SNAP sales per 1000 SNAP households in the area surrounding the farmers’ market compared to markets in metropolitan areas. Weekly SNAP transactions per 1000 SNAP households were 2 times higher at rural markets. Although fewer SNAP households are located near markets in rural settings, these households were more likely to be reached by the farmers’ market SNAP incentive program. On average, there were 167 SNAP households residing near the markets in rural settings versus 1783 in metropolitan settings.²⁶ There were an average of 34 weekly SNAP transactions per 1000 SNAP households in rural settings versus 21 in metropolitan settings. The overall number of people reached was higher in urban areas (a function of population density); however, the rate of utilization of the SNAP incentive program was significantly higher in rural areas. This is in contrast to prior research that found barriers to using farmers’ markets in rural settings.⁴¹ Higher rates of SNAP use at the farmers’ markets located in rural areas may be due to the fact that there is higher farmers’ market density in metropolitan areas, which provides greater choice to SNAP consumers but distributes the consumer base among multiple markets. This may result in lower rates of SNAP use at some of the markets in metropolitan areas. Additionally, findings reveal weekly SNAP sales per 1000 SNAP households in the area surrounding participating farmers’ markets were about 2% lower for each percentage increase in African American residents living in the area near the market. Together, these findings highlight the benefits of targeting urban African American populations in future SNAP-based incentive program implementation and outreach.

Second, there was evidence that offering more than 1 incentive program was strongly related to improvements in SNAP use. Multiple incentive programs may motivate cost-conscious customers to frequent a farmers’ market because there will be several opportunities to extend financial resources for food within 1 shopping visit. Multiple incentive programs also provide opportunities for shoppers to take advantage of different subsidies as resources ebb and flow across the monthly SNAP distribution cycle.

Third, findings support paying farmers’ market management staff to operate incentive programs. Paid market staff may be more likely to be a regular presence at the market and have more time to conduct marketing and outreach for the market and incentive program outside of market hours. Consistency of market staff fosters relationships with customers who may be more likely to return because of this social connection.⁴²

Fourth, results highlight the importance of increasing the number of produce vendors selling at the market. Strategic growth of market size will require targeted efforts to expand the number of fruit and vegetable vendors who may be in short supply in some communities. To promote inclusivity, this type of expansion may focus on including vendors with similar sociodemographic characteristics of the targeted SNAP population.¹⁸ Some markets have diversified their vendor base through efforts to connect with immigrant and refugee farmers who often grow foods preferred by different ethnic groups.

Together, these implementation factors address transactional costs that influence economic decision-making. Transactional costs include all of the costs beyond the price of the food that influence a decision to purchase a good.⁴³ These are especially important since monetary incentive interventions target the price of the food but not necessarily these other transactional costs. At a farmers’ market, these may include social costs of interacting in a new space that may not feel culturally inclusive, psychic costs of engaging in a food shopping behavior that may not be normative, informational costs to determine which foods to purchase and how to prepare them, infrastructure costs such as access to refrigeration or storage to effectively manage the foods at home, transportation and time costs to get to the market, and the additional costs needed to complete food purchasing if all food needs cannot be met at the farmers’ market.

Results have implications for policy and practice. From a policy perspective, findings offer guidance for enhancing the federal FINI grant program to maximize reach among SNAP recipients. This is particularly timely because the Agriculture Improvement Act of 2018 (ie, US Farm Bill) includes expansion of the FINI grant program to $250 million over 5 years and establishes the program as a permanent part of future farm bills.⁴⁴ Findings from this research indicate there is a benefit to providing financial support to farmers’ markets to cover the costs of hiring staff to operate fruit and vegetable incentive programming. Additionally, policies that support incentivizing multiple forms of federal subsidies such as Medicaid or Special Supplemental Nutrition Program for Women, Infants, and Children within the farmers’ market context may further the reach of these interventions among people receiving SNAP. Furthermore, FINI implementation should target and tailor outreach within specific geographic areas with high rates of households receiving SNAP (eg, StrikeForce zones in rural areas, public housing communities in urban areas).

From a programmatic perspective, findings provide a framework for conceptualizing the core elements of SNAP-based fruit and vegetable incentive program implementation at farmers’ markets. Study findings suggest core elements include having (1) more than 1 incentive program, (2) paid farmers’ market staff responsible for incentive programming, (3) high numbers of produce vendors available on each market day, and (4) outreach to garner more customers overall combined with targeted efforts to promote farmers’ market use among African American SNAP recipients living in urban communities.

There are strengths and limitations to this research. A key strength is the sample size, including diversity in market models, locations, and operations. To our knowledge, this study represents the largest sample of farmers’ markets included in a study focused on incentive programming. Another strength is the analytic approach that allowed for examination of multiple factors influencing SNAP utilization while taking into account nesting of farmers’ markets within states. Weaknesses include risk of data entry errors from market staff or volunteers. There may be other important factors influencing SNAP utilization, such as marketing and communication approaches that were not captured across sites. Another limitation is the fact that this study lacks data on non-FINI-funded farmers’ markets that may be located near the markets included in this study, which could impact SNAP utilization rates per market. Finally, the markets included in this study are different from other farmers’ market across the United States because they received funding from an FINI grant to support SNAP incentive programming. Findings may not be generalizable to non-FINI-funded farmers’ markets.

In conclusion, this study extends the science of healthy food incentive programming by examining implementation factors that can be modified to maximize impact among SNAP consumers shopping at farmers’ markets. This research builds on the growing body of evidence showing support for SNAP-based monetary incentives as a strategy to improve dietary patterns among low-income populations. Our analysis of the real-world implementation of farmers’ market fruit and vegetable incentive programs operating throughout the United States reveals strategic targets to guide future policy and programmatic initiatives.

So What?

What is already known on this topic?

Healthy food incentive programs are effective strategies to promote dietary improvements.

There is growing interest in implementing these types of programs at farmers’ markets.

Strategies are needed to maximize the reach of farmers’ market incentive programs among people receiving SNAP.

What does this article add?

This is the largest study evaluating real-world implementation of incentive programming at farmers’ markets.

Results provide evidence of implementation factors related to increased use of SNAP benefits at farmers’ markets offering incentive programs.

What are the implications for health promotion practice or research?

Findings have implications for federal nutrition policy implementation by identifying programmatic components that are likely to increase the impact of healthy food incentive programs.

Findings provide an evidence-based framework for expansion of SNAP-based incentive programming to maximize reach among people receiving SNAP.

Footnotes

Authors’ Note

D. Freedman and K. Merritt conceptualized this manuscript. K. Merritt and J. Pon supported data collection. D. Ngendahimana and E. Shon conducted analysis. All authors were involved in manuscript writing and approval. This study was approved as an exempt protocol by the Case Western Reserve University institutional review board (protocol no. IRB-2014-903).

Acknowledgments

The authors thank the farmers’ market staff and volunteers as well as Wholesome Wave staff for their support with data collection. Additionally, we honor the wisdom of the late Gus Schumacher who provided advice and encouragement for the development and implementation of this national fruit and vegetable incentive program. The authors are grateful to the reviewers for their thoughtful feedback on earlier versions of this article.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: D. Freedman invented software used for data collection in this study and this software is being commercialized by an outside entity. Case Western Reserve University is the owner of the invention being commercialized by this outside entity, and as such, could gain royalties.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is a product of a Prevention Research Center supported by Cooperative Agreement Number 1U48DP005013 from the Centers for Disease Control and Prevention (CDC) and was supported by grant number 2015-70018-23350 from the US Department of Agriculture (USDA). The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of the CDC or USDA. The CDC and USDA had no role in the design, analysis, or writing of this article.

ORCID iD

Darcy A. Freedman

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