The Role of Cost-Share Rates and Prices on the Size of Conservation Investments in EQIP. Michael S. Hand* Cynthia Nickerson.

Transcription

1 The Role of Cost-Share Rates and Prices on the Size of Conservation Investments in EQIP Michael S. Hand* Cynthia Nickerson April, 2009 Selected paper prepared for presentation at the Agricultural and Applied Economics Association s AAEA & ACCI Joint Annual Meeting, Milwaukee, WI, July 26-28, The authors are economists with the U.S. Department of Agriculture, Economic Research Service, 1800 M St. NW, Washington, DC The views expressed are those of the authors and do not necessarily represent the views of the U.S. Department of Agriculture. * Corresponding author. mhand@ers.usda.gov. Phone: (202)

2 The Role of Cost-Share Rates and Prices on the Size of Conservation Investments in EQIP Abstract: Per-unit costs for conservation investments in the Environmental Quality Incentives Program (EQIP) are determined by market prices for conservation practices and payment cost-share rates. This study examines the relative importance of these factors in determining the size of the most common conservation investment in EQIP. Results suggest that per unit costs have a significant but inelastic impact on the size of conservation investment; as expected, higher per-unit costs are associated with smaller investments. This effect is largely due to variation in market prices of conservation practices for the majority of farmers. However, cost-share rates may play a role for some limited resource farmers.

3 The Role of Cost-Share Rates and Prices on the Size of Conservation Investments in EQIP Federal expenditures on conservation programs for working farmland and ranchland have greatly expanded in recent years. The Environmental Quality Incentives Program (EQIP), the largest Federal working lands program, has seen annual funding increase from about $185 million under the 1996 Farm Bill to about $1.5 billion under the Farm Bill passed in The vast majority of these funds are paid to farmers to encourage voluntary adoption of conservation management practices (through incentive payments) and investments in conservation structures (through costsharing agreements). Given the size and growth in Federal conservation programs, and EQIP in particular, it is desirable to have a thorough understanding of the factors affecting participation and investment decisions; even relatively small unintended consequences can have a large aggregate impact on the allocation of program funding. This paper seeks to better understand the role of costs in conservation decisions, and in particular the role of government cost-share rates as a determinant of the size of conservation investments in EQIP. A great deal of research has examined the determinants of conservation program participation (e.g., Lynch and Lovell 2003; Onianwa et al. 2004; Gan et al. 2005; Obubuafo et al. 2008) and the factors that affect adoption of best management practices (BMPs) or other conservation practices (Traoré, Landry, and Amara 1997; Wu and Babcock 1998; Onianwa, Wheelock, and Hendrix 1999; Soule, Tegene, and Wiebe 2000; Lambert et al. 2006; Gillespie, Kim, and Paudel 2007; Lambert, Sullivan, and Claasen 2007; Prokopy et al. 2008). However, less is known about whether and to what degree farmers alter the size of conservation

4 investments in response to differences in the per unit costs they face. Research suggests that farmers are more likely to adopt BMPs and other conservation practices if incentive payments are larger (Lohr and Park 1995; Cooper and Keim 1996; Lichtenberg 2004; Wu et al. 2004), and farmers may enroll more acres in conservation programs (Cooper 1997) and increase conservation effort (Lynne, Shonkwiler, and Rola 1988) when incentive payments are larger. These studies support a general underlying premise of voluntary conservation programs, that larger payments will increase the probability of participating and adopting conservation practices. Beyond the general role of incentives in conservation programs, farmer responses to per unit costs may be particularly important for better understanding behavior in EQIP. The cost of conservation investments faced by farmers is determined by a non-policy component (market prices for materials and labor, the opportunity cost of time, etc.) and a policy component (the cost-share or reimbursement rates set by Federal, state, and local governments). However, costshare rates in EQIP can vary between states and local jurisdictions, and certain groups of farmers (beginning and limited resource farmers prior to 2008) are eligible for higher cost-share rates in some locations. Thus, the magnitude of farmer responses to cost-share rate variation and the roles of cost-share rates and non-policy price variation may be an important determinant of the distribution of Federal conservation investments. 1 Recent changes to EQIP also highlight the need for a better understanding of responses to cost-share rate and per-unit cost differences. As of 2008, Federal legislation extends higher costshare rates to socially disadvantaged farmers, in addition to beginning and limited resource farmers who have been eligible for higher rates since To the extent that these groups are not identical and participate in EQIP in different ways, knowledge of farmers price response

5 may indicate how this policy change will affect conservation investments, and ultimately, conservation outcomes. To examine farmer responses to price and cost-share rate variation, we use EQIP contract data to calculate the size of conservation investment for fences (the most common structural practice), the total per unit cost, and the per unit cost-share payment. We control for environmental characteristics, state-level differences in allocation criteria, and beginning or limited resource farmer status, to isolate the relative impact of price differences and cost-share rates on the size of an investment in fencing for conservation purposes. Although the contract data set is not ideal (e.g., it lacks basic farm-level information that may be related to size of investment), it has the advantage of identifying the price variables and the size of conservation investment for all EQIP participants. A simple empirical model of conservation investments The general hypothesis of this paper is that farmers respond to variations in the cost of installing conservation structures by altering the size of their investment. For example, a farmer who operates a farm in a county where the price of installing a fence is 50% lower than a neighboring county will install a longer fence, ceteris paribus, than a neighbor who operates a similar farm in the higher-price county. If farmers face budgetary constraints in making conservation investments, then the lower price may allow the farmer to install fencing on an adjoining parcel of land that she would not have included under a higher price regime. Besides the role of own-price that a farmer faces for a particular investment, other factors may affect the investment decision. Adapting the model of conservation practice adoption from Lichtenberg (2004), farm i s quantity demand for a particular practice (q i ) at a given point in

6 time is a function of its own price (p i ) and the vectors of other input and conservation practice prices (p xi ), land productivity factors (z i ), fixed capital factors (c i ) (e.g., farm size, human and natural capital), and available technology (t i ), or: q = f (, p X, z, c, t ). i p i If demand for the practice is linear in the parameters, then we can formulate a general empirical model that, under certain assumptions, can be estimated with ordinary least squares: (1) qi = α + β p i + Φ Xi + μi, where µ i is the i.i.d. error term, β i is the price parameter of interest, Φ is the vector of other parameters to be estimated, and X i is the vector of other independent variables in the demand function. In the estimation presented below, we use a semi-log specification, so q i is expressed as the natural log of the size of conservation investment. In the demand function and equation (1), prices are in terms of the per unit cost faced by farmers. For EQIP participants, which are the focus of this study, price is calculated net of the cost-share payments, or, (2) p = p ( 1 sr ) i 0 i i, where p i is the per unit cost faced by farmers, p 0i is the posted or market per unit price, and sr i is the cost-share rate ( 0 sr 1) paid through EQIP. i An important feature of the demand for conservation investments is that the effect of price variation can be decomposed into a posted-price effect and a share-rate effect. Substituting equation (2) for p i in equation (1) yields, (3) qi = α + β p i β p i sri δ sr i + Φ Xi + μi 1. This specification allows for examination of the relative effect of variation in the posted (market) i i i i price of investments and the effect of different share rates offered through EQIP. We include sr i

7 as a separate independent variable to allow for response to share rate variation that is independent of the posted prices. We might expect the decomposed price model to be relevant (i.e., β 1 - β 2 and δ 0) if there is either very little variation in posted prices or share rates (e.g., farmers are only paying attention to cost-share rate differences), or if cost-share rate differences primarily affect the decision of whether or not to participate in EQIP. This is an empirical question that may have policy implications for the design of voluntary conservation programs. Do higher cost-share rates, which are set by Federal, state and local governments, encourage participation of those eligible for the higher rates? Or do they primarily affect the behavior of those already participating in the program? The empirical model is only capable of addressing the latter question; if cost-share rate differences do not affect the intensity of conservation investments, then an open question remains as to whether cost-share rates affect participation decisions. EQIP administrative data To estimate the parameters in equations (1) and (3), we use data from EQIP contracts signed in fiscal year Previous studies of participation and behavior in EQIP tend to rely on survey data (e.g., Obubuafo et al. 2008). This study is unique in that we have access to information about all EQIP contracts in the U.S. from This data has advantages and disadvantages compared to survey data. A primary advantage is that we have comprehensive information about all of the conservation practices adopted in EQIP, including payments and the cost of adopting each practice. This feature allows us to examine the intensity of conservation investments by using the size of each practice adopted, rather than focusing attention only on whether or not a practice was adopted or how many practices were adopted.

8 Two disadvantages are that we can observe behavior only of EQIP participants, and that the administrative data contains very little farm- or household-level information. The former issue limits our ability to extrapolate from the results; to the extent that non-participants differ from participants, it may be difficult to make conclusions about the conservation practices adopted by non-participating farmers, or how non-participants might behave if they were to participate in the future. The latter issue creates difficulties in controlling for characteristics that may have an impact on conservation investment decisions. For example, we cannot observe farm size for each participant; beginning and limited resource farmer status categorical variables and state-level fixed effects control for some systematic variation in unobserved characteristics, but the possibility remains that some important variables have been omitted. More than 200 conservation practices are eligible for funding in EQIP, including the adoption of conservation plans, soil and livestock management practices, and installation of structures and vegetative features to reduce the negative environmental impacts of farming. Structures and vegetative features are most appropriately viewed as conservation investments. These practices generally involve a one-time or short-term cost to install permanent physical structures that provide conservation benefits over a long time period. To simplify the analysis, we focus on the installation of the most common conservation structure from 2006: fences. About 25% of contracts in 2006 included fencing as all or part of the agreed conservation practices. Fences in EQIP are used to address several natural resource concerns, the most common of which are plant condition, domestic animal-related concerns, water quality, and soil erosion. The dependent variable in the empirical model is intensity of conservation investment, which we define in this case as the size of investment. For the purposes of this study, the size of

9 investment is defined as the total length of fence installed as part of each 2006 contract (LENGTH). 4 Taking the natural log of LENGTH gives the dependent variable used to estimate equations (1) and (3), LNLENGTH. The independent variables of interest are the per unit price the farmer faces, less costshare payments, to install the conservation investment (PRICE), the posted or market per unit price (POSTED), and the cost-share rate (RATE). These correspond respectively to p i, p 0i, and sr i in equation (3). Each of these variables is calculated from the administrative data. Whether a farmer is eligible for higher cost-share rates depends on the state and county where the farmer is located and whether the farmer meets the definition of a beginning or limited resource farmer. 5 We include categorical variables for beginning (BF) and limited resource (LR) farmer status to control for whether the farmer is eligible for higher rates in EQIP. However, these types of farmers also tend to have different farm and household characteristics that could influence conservation investment decisions (see Hoppe et al. 2007), and BF and LR may to some degree proxy for these differences as well. Environmental factors and landscape characteristics may affect conservation investments. For example, results in Lambert et al. (2006) suggest that erodibility is associated with adoption of working lands conservation practices. The administrative data contains latitude and longitude information for each contract, which we use to calculate the share of land near each contract that is designated as highly erodible (HEL), the distance to the nearest impaired water feature (DIST), and total length of linear water features near the contract (WATER). 6 These variables are meant as controls for variation in environmental sensitivity; it is expected that larger conservation investments are needed in areas where more erosion is possible, and where water features are impaired or at risk of impairment.

10 Complete data for the environmental factors could be accurately matched to EQIP contracts in three regions of the U.S., the Northern Plains, Southern Plains, and Delta regions. Thus, the empirical analysis is limited to contracts from these regions. 7 Other controls include the stated resource concern addressed with fences on the contract and state fixed effects. The four most common resource concerns described above are included as categorical variables (DA, PC, WQ, and SE), with the reference category including all other resource concerns (including fish and wildlife habitat, water quantity, air quality, and soil quality; fewer than 30% of contracts address these concerns). State fixed effects are used to control for differences between states in funding priorities and other administrative differences at the state level that are not evident in the contract data. 8 The state fixed effects are also included as interactions with BF and LR to control for differences in behavior (and characteristics) of these types of farmers between states that may be unrelated to the price variables of interest. Table 1 lists the variables used in the analysis, with summary statistics. The total sample size used for the analysis is 2,413 contracts. Empirical results: The impact of prices and cost-share rates on fence length Table 2 presents the parameter estimates of equations (1) and (3). Two specifications are presented for each empirical model; the first (columns A and C) represents the simplest model that controls for BF and LR status but does not contain interactions between these variables and the other independent variables. The second specification (columns B and D) includes interactions between LR and the price variables and between LR and the state fixed effects. 9 State fixed effects are included in all specifications, but for brevity are not reported.

11 Equation (1) includes as an independent variable the per unit price that an EQIP participant faces to install fences, net of cost-share payments. Columns A and B in table 2 present these results. The parameter for PRICE is negative and statistically significant, indicating that participants who face higher prices invest in smaller fences, ceteris paribus, than those who face higher prices. With the semi-log specification used for the estimates, the parameter estimate can be interpreted as a percentage change in the dependent variable for a one unit change in the independent variable. Because the sample mean of PRICE ($0.61) is less than one, it may be useful to consider the own-price elasticity of installing a fence, i.e., ε = β PRICE. Evaluated at the sample mean of PRICE yields ε = for the specification with interactions (column B); a 1% increase in the price an EQIP participant faces to install a fence results in a decrease of about 0.18% in the total length of the fence. To put this in perspective, an increase in average per unit prices by one standard deviation (about $0.46) would result in a decrease in the length of the average fence by 550 ft, or 13.5% of the average total fence length. A limitation of the PRICE variable is that it doesn t allow us to isolate the response to changes in the cost-share rate, the policy focus of this paper, without assuming that farmers respond to changes in the share rate and the posted market price in the same way. Estimates of equation (3) (columns C and D in table 2) relax this assumption by including POSTED and RATE as the decomposition of the PRICE variable. These estimates give insight into whether EQIP participants are responding to changes in the cost-share rate or market prices (or both). The decomposed price model suggests a more nuanced role for prices and cost-share rates in determining conservation investments. In the model without interactions (column C), none of the price variables are significant at traditional levels. The parameter estimate for PRICE PRICE PRICE

12 POSTED is of the expected sign with a p-value of about 0.12, but the POSTED RATE and RATE parameters are imprecisely estimated and not distinguishable from zero. Including the LR interactions with the state fixed effects and price variables (column D) suggests that limited resource farmers may respond to price components differently than other farmers. For all farmers, the role of posted prices is negative and significant at the 10% level; an increase in the gross per unit cost (prior to cost-share payments) reduces the total length of fence that is installed on a contract. The LR POSTED and LR POSTED RATE are statistically different from zero, indicating that market prices and share rates have a different effect on the investments of limited resource farmers. To clarify the interpretation of the results where there are first- and second-order interactions, the regression equation can be differentiated with respect to either POSTED or RATE. For limited resource farmers (i.e., LR = 1), these are: LNLENGTH (4) = RATE, POSTED LNLENGTH (5) = 1.4 POSTED. RATE Equation (4) indicates that the effect of market prices on fence length depends on costshare rate that a participant receives. This makes intuitive sense as the share rate determines how much of the market price the participant will have to pay. In this case, if the share rate is above LNLENGTH about 0.53 then < 0 ; i.e., increases in market prices results in shorter fences, POSTED which is expected. The second derivative of (4) with respect to the share rate is also negative, indicating that the response to changes in market prices becomes more elastic (i.e., more negative) at higher cost-share rates. For example, the highest share rate available in 2006 was 0.9, which was only available to beginning or limited resource farmers. At RATE = 0.9,

13 LNLENGTH = and the price elasticity isε POSTED = 0. 71, evaluated at the average POSTED value of POSTED. 10 The marginal effect of cost-share rates, represented by equation (5), depends on market prices but is negative, which is opposite of expectations. That is, contracts with higher cost-share rates for limited resource farmers are associated with shorter fences, but the effect is larger (i.e., more negative) when market prices are higher. A possible explanation for the counter-intuitive result is that there is a negative correlation between farm size (or some other relevant unobserved characteristic) and share rates for certain limited resource farmers; farms eligible for the highest rates may be smaller on average and tend to install smaller conservation structures. 11 A difficulty in interpreting the decomposed-price model results is potential collinearity. The source of collinearity in this case is that most limited resource farmers receive higher costshare rates; that is, there will be a high degree of correlation between LR, RATE, and POSTED RATE, particularly within certain states, which may result in imprecise estimates. Further, the specifications with the LR interactions may introduce additional collinearity in both the one-price and decomposed-price models. Initial diagnostic tests indicate that the specifications with the LR interactions have the greatest potential for collinearity problems. 12 However, collinearity may not be harmful for the variables of interest if the coefficient estimates are estimated precisely enough to determine statistical significance (Belsley et al. 1980). Estimates of the PRICE coefficients and the POSTED, LR POSTED, and LR POSTED RATE coefficients in the decomposed-price model with interactions do not exhibit large variances typical of data with a collinearity problem, suggesting that the inference that these coefficients are statistically different from zero is likely valid.

14 Other statistically significant determinants of fence length include the environmental and landscape variables. All four specifications indicate that the share of cropland designated as highly erodible (HEL) and total length of surface water features (WATER) are positively and significantly associated with fence length. The general interpretation of this result is that greater environmental sensitivity of the landscape tends to be associated with greater conservation effort, at least for fences installed through EQIP. The results for distance to impaired water features (DIST) are also consistent with this explanation (i.e., shorter distances are associated with longer fences), but the coefficients are less precisely estimated. Discussion and conclusion The goal of this study is to examine how EQIP participants respond to variation in per unit costs of conservation investments. We also examined the relative roles of posted market prices and cost-share rates set by Federal, state, and local jurisdictions. Results suggest that prices are a factor that determines the size of the most common type of conservation structure in EQIP (fences), although the effect appears to be inelastic. In general, lower per unit costs faced by participants are associated with shorter fences installed on EQIP contracts. The effect of per unit costs on conservation investments appears to be driven primarily through posted market prices, although the effect of posted prices is statistically weak for most farmers. Results suggest that cost-share rates affect the size of installed fence only for limited resource farmers. For these farmers, larger share rates (which reduce per unit costs faced by participants) are associated with a larger response to differences in posted market prices. Some of this effect may be due to a small group of farmers who are eligible for the highest share rates and may have different farm characteristics that are unobserved.

15 Empirical estimates are drawn from a unique administrative data set that includes all contracts and conservation practices adopted in EQIP in 2006 for certain regions of the country. Although the administrative data has certain advantages, a few caveats may limit our ability to generalize the results beyond the current application. Primary among these is that the EQIP contract data contains little farm- or household-level information. Some unobserved characteristics, such as farm size, are likely related to the beginning and limited resource farmer indicators available in the data. Lambert et al. (2006) found that household characteristics tend not to be related to the adoption of conservation practices, but it is possible that other omitted variables may be important determinants of the size of conservation investment. A second limitation is that we only observe the behavior of farmers who participate in EQIP. Prices and cost-share rates likely affect participation decisions, yet the current model does not incorporate this potential role. This may be of particular interest in light of the finding that cost-share rates have little impact on the size of conservation investment for most participants. Future work ideally would jointly model participation decisions, practice adoption decisions, and the size of investment. Finally, the ability to extrapolate from the results may be limited because detailed environmental data were available only for the Northern Plains, Southern Plains, and Delta regions (nine states total). The empirical results will be applicable only to these regions if the relevant factors that determine conservation investments are different in other regions. In light of these caveats, the results suggest that per unit cost and cost-share rate variation are policy relevant factors in EQIP, at least in the current application. That market prices affect the size of conservation investments suggests that price variation within a jurisdiction (e.g., a state) can affect the distribution of EQIP funds and potential environmental benefits from the

16 programs. However, the primary policy tool that alters the per unit price that EQIP participants face cost-share rates appears to be ineffective in changing behavior for most participants. Program managers who wish to alter the distribution of EQIP funds and conservation investments, perhaps for equity concerns or to address a particular environmental need, may need to look to other policies at their disposal to impact behavior. Although cost-share rates may not generally affect the size of conservation investments, results indicate that certain farmers limited resource farmers eligible for the highest cost-share rates behave differently from other farmers with respect to per unit costs. These farmers may invest in significantly smaller conservation practices, perhaps due to operating smaller farms or other constraints. This phenomenon requires further research, but it appears that role of market prices on conservation decisions that applies for most farmers is overwhelmed by other factors affecting decisions for these limited resource farmers.

17 Notes: 1 Lichtenberg s (2004) model of conservation practice adoption noted the role of varying per unit costs in determining the size of investment, but the data used in the study was only able to identify discrete adoption behavior. 2 The 2008 legislation also mandates that rates are 25 percentage points higher for these groups, up to a 90% cost-share rate. See Title II, subtitle F of the Food, Conservation, and Energy Act of 2008, H.R Available at accessed 4/3/ The EQIP information is maintained by the USDA Natural Resources Conservation Service and housed in their Protracts database. Contract data is gathered by state and local NRCS offices and compiled by the national NRCS office. 4 Observations with a value of zero for LENGTH are excluded in the analysis. This is consistent with the focus on behavior of those farmers who adopt the practices of interest (fences, in this case). A more detailed model would include the effect of prices and cost-share rates on whether or not an EQIP participant installs a fence in addition to the effect on the size of the fence. This is left to future research. 5 Although beginning and limited resource farmers are eligible for higher rates, not all of these farmers receive the highest rates and variation in RATE exists among beginning and limited resource farmers. 6 HEL is calculated by matching the contract latitude and longitude to the Soil Survey Geographic Database maintained by NRCS (available at accessed 4/3/2009); DIST uses geographic information from state listings of 303d impaired

18 waterways; WATER is calculated using geographic information from the National Hydrology Database maintained by the U.S. Geological Survey (available at accessed 4/3/2009). The area near contracts is based on a circle drawn around each latitude and longitude point with an area equal to the average farm size for each state, calculated using the 2002 Agricultural Census, conditioned on beginning or limited resource farmer status. 7 States in these regions include: Alabama, Arkansas, Kansas, Louisiana, Mississippi, Nebraska, North Dakota, Oklahoma, South Dakota, and Texas. Only three observations were available with complete data in Louisiana; these observations were dropped from the analysis because of the importance of state-level interactions with the LR variable. 8 Priorities and administrative differences can also occur at the sub-state (e.g., county) level. Incorporating these differences is left for future research. 9 Lacking a priori information about how BF and LR participants may behave differently from other participants, several specifications with different combinations of interactions were tested. A full model included interactions between BF and LR and all other variables; interactions terms that were not statistically significant at a 90% confidence level using a Wald test were dropped from the model. In general, LR interactions with state fixed effects and the price variables were jointly significant; BF interactions with state fixed effects were marginally significant, but results do not qualitatively change with the inclusion of these interactions. Results are available upon request. 10 Like the first derivative in equation (4), the elasticity with respect to POSTED equals zero when RATE = 0.53.

19 11 Estimates of equation (3) excluding observations where RATE<0.9 support this explanation, although we cannot test it directly. In this case the LRxPOSTED and LRxPOSTEDxRATE coefficients are not significantly different from zero, and only the direct effect of POSTED is statistically significant (and of the expected sign). Results are available upon request. 12 We use the variance inflation factor (VIF) and condition number calculations, see Belsley et al. (1980). The VIF for LR and LR interactions with the price variables tend to be high (e.g., greater than 100), and the condition number for those specifications is very large (e.g., greater than 1000). By these measures, the one-price model without interactions performs best, with VIFs and condition number less than 10. This is another example of where additional farm- and household-level data may provide useful information in the empirical analysis.

20 References Belsley, D.A., E. Kuh, and R.E. Welsch Regression Diagnostics. New York: John Wiley and Sons. Cooper, J.C Combining Actual and Contingent Behavior Data to Model Farmer Adoption of Water Quality Protection Practices. Journal of Agricultural and Resource Economics 22: Cooper, J.C., and R.W. Keim Incentive Payments to Encourage Farmer Adoption of Water Quality Protection Practices. American Journal of Agricultural Economics 78: Gan, J., O.O. Onianwa, J. Schelhas, G.C. Wheelock, and M.R. Dubois Does Race Matter in Landowners Participation in Conservation Incentive Programs? Society and Natural Resources 18: Gillespie, J., S. Kim, and K. Paudel Why Don t Producers Adopt Best Management Practices? An Analysis of the Beef Cattle Industry. Agricultural Economics 36: Hoppe, R.A., P. Korb, E.J. O Donoghue, and D.E. Banker Structure and Finances of U.S. Farms: Family Farm Report, 2007 Edition. U.S. Department of Agriculture, Economic Research Service Economic Information Bulletin No. 24, Washington, DC. Lambert, D.M., P. Sullivan, and R. Claassen Working Farm Participation and Acreage Enrollment in the Conservation Reserve Program. Journal of Agricultural and Applied Economics 39: Lambert, D., P. Sullivan, R. Claassen, and F. Foreman Conservation-Compatible Practices and Programs: Who Participates? U.S. Department of Agriculture, Economic Research Service Economic Research Report No. 14, Washington, DC.

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23 Table 1. Variable descriptions and summary statistics Variable Description Mean Std. Dev. Dependent variable LENGTH Total length of fence (in ft.) installed on the EQIP contract 4,062 4,585 LNLENGTH Natural log of LENGTH Independent variables PRICE Per unit costs of installing fencing, net of cost-share payments ($) POSTED Per unit gross costs of installing fencing ($) RATE Share of installation costs paid to the farmer BF Beginning Farmer status (binary) LR Limited Resource farmer status (binary) HEL DIST WATER DA PC WQ SE Share of cropland around contract that is designated as highly erodible Distance (in ft.) to nearest 303d-listed impaired water feature. Total length (in mi.) of streams and rivers around the contract. Contract addresses Domestic Animal resource concerns (binary) Contract addresses Plant Condition resource concerns (binary) Contract addresses Water Quality resource concerns (binary) Contract addresses Soil Erosion resource concerns (binary) ,665 20, Sources: ERS calculations from USDA-NRCS Protracts database (LNLENGTH, PRICE, POSTED, RATE, BF, LR, DA, PC, WQ, SE); Soil Survey Geographic Database, (HEL); ERS calculations of 303d impaired waterways lists (DIST); National Hydrology Database, U.S. Geological Survey, (WATER).

24 Table 2. OLS estimates of fence length models (dep. var. = LNLENGTH, obs. = 2,413) One-price models Decomposed-price models A B C D Price vars. PRICE -.289*** -.293*** POSTED * POSTED RATE RATE Other ind. vars. BF LR ** HEL.223***.232***.220***.223*** DIST -1.42e e e-6* -1.23e-6* WATER 2.64** 2.66** 2.52** 2.54** DA.093**.101***.082**.091** PC.113***.117***.102***.108*** WQ -.127** -.113** -.107** -.097* SE.124***.131***.120**.129*** LR interaction terms LR PRICE.026 LR POSTED.980** LR POSTED RATE -1.40** LR RATE 1.21 LR interactions with states? No Yes No Yes R AIC 5,912 5,923 5,908 5,920 Notes: ***, **, and * indicate significance at 99%, 95%, and 90% levels, respectively, using heteroskedasticity-corrected (White s) robust standard errors. Parameters for state fixed effects and LR interactions with state fixed effects suppressed for brevity (available upon request).