Using Enrollment Discontinuities to Estimate the Effect

Using Enrollment Discontinuities to Estimate the Effect of Voluntary Conservation On Local Land Values Jacob N. Brimlow♣ and Michael J. Roberts♠ May 2010

♣

College of Agriculture, California State University, Chico, 400 West First Street, Chico, CA 95929-0310 Email: [email protected]. ♠ Department of Agricultural and Resource Economics, North Carolina State University, Box 8109, Raleigh, NC. Email: michael [email protected].

Selected Paper prepared for presentation at the Agricultural & Applied Economics Associations 2010 AAEA, CAES & WAEA Joint Annual Meeting, Denver, Colorado, July 25-27, 2010.

Copyright 2010 by Jacob N. Brimlow and Michael J. Roberts. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided this copyright notice appears on all such copies.

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Introduction

Offering payments in exchange for voluntary land use restrictions is an increasingly popular way of conserving ecosystem services such as wildlife habitat, air and water quality enhancements, and carbon sequestration on agricultural land. The Conservation Reserve Program is the largest U.S. federal program of this kind. It currently enrolls over 34 million acres of productive cropland (about the size of Iowa), paying enrolled landowners over $1.8 billion for contracts active in 2008. Besides the direct effects of conservation activities in exchange for money, there may be spillovers stemming from voluntary conservation programs. For large-scale programs like CRP, some argue that conservation activities reduce local demand for agricultural labor, inputs and services, thereby causing local economic decline - i.e., negative economic spillovers. To date, evidence of such spillover effects, even in areas where a significant share of the local cropland is enrolled in CRP, suggests these effects are minimal (Sullivan et al. 2004). This evidence shows that overall trends in employment and population, even in areas with large CRP enrollments, were unperturbed around the time of those enrollments, and similar to patterns in otherwise similar areas with little or no CRP enrollment. Other anecdotal evidence suggests CRP enrollments have enhanced recreation opportunities, particularly for hunting and for bird watching. Thus, it could be that negative spillovers on agricultural economic activities are offset by positive spillovers in other sectors of rural economies. Finally, CRP enrollment could have far reaching economic effects through commodity prices. CRP contracts have removed 10 percent of the productive cropland in the United States, representing a significant downward shift in the supply of cropland acreage and thereby increasing crop prices. In this paper we examine the effect of CRP on agricultural land values, including possible effects from local economic spillovers. Asset pricing theory predicts that land prices will embody present and future returns to land, and therefore should capture direct and at least some indirect effects of CRP. For example, if payments for conservation activities generally exceed the amount farmers would have been willing to accept, this could cause an increase in land value for farmers able to gain enrollment. Alternatively, if CRP caused a reduction in the supply of inputs and labor for non-participating farmers, it could cause their land values to decline. CRP might also enhance bird and wildlife populations and thereby increase demand for recreational services, which could cause land values to increase, perhaps even for non-participating landlords. One potentially useful aspect of land values in comparison to other outcomes is that they embody speculation about broader effects even before those 1

effects occur. Empirically evaluating the effect of CRP on land values is complicated by a strong form of selection bias. The program is designed in a way that gives farmers with low-quality land much greater incentive to offer their land for enrollment as well as a higher chance of acceptance. Thus, land enrolled in CRP is quite unlike land not enrolled. A standard cross-sectional analysis of land values in relation to CRP enrollment shows a strong negative association, but this does not indicate a causal relationship going from CRP to land values. Causation likely flows in the opposite direction, with land values determining the areas with greater CRP enrollments. In this paper we present a new approach to estimating the causal effect of increased CRP enrollment on county-level land values. This approach attempts to exploit a sharp discontinuity in the way USDA selects which offers for CRP are selected into the program. Most CRP enrollment is conducted through a competitive enrollment scheme based on an Environmental Benefits Index (EBI). This index combines environmental attributes of the offered parcel with the landowner’s requested rent, and is used to score bids relative to a critical threshold of acceptance unknown to landowners. All offers with EBI scores above the critical threshold are accepted while all offers below the threshold are rejected. The distribution of EBI scores is bell-shaped, but there is a sharp discontinuity in acceptance at the critical threshold, which tends to be near the mode of the offer distribution. Our maintained hypothesis is that parcels with similar EBI scores are likely to be of similar exante value. Thus, to discern the causal effects of CRP, a fairer comparison is between the changes in outcomes on parcels just accepted into the program and the changes in outcomes on parcels just rejected. The difference between our study and standard regression discontinuity (e.g. Hahn et al. 2001, Lee and Card 2008, Cook and Campbell 1979) is that we focus on county-level rather than parcel-level effects. This is because we wish to discern both direct effects of CRP and indirect spillover effects on nearby land not enrolled in CRP. To adapt regression discontinuity to consider larger-scale effects, we aggregate to the county level. That is, we relate the change in county land values to the share of county offers just accepted into the program while conditioning on the sum of the share of offers just accepted and just rejected. By conditioning on the sum of the share of offers just accepted and just rejected, the sharejust-accepted variable isolates a source of variation around the narrow band of acceptance. The regression ostensibly compares the change in land values in counties with a larger share of land just above the threshold of acceptance to counties with a larger share of land just

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below the threshold, but with similar shares within the just-accepted/just-rejected band. The smaller the band, the less potentially biased will be the estimated effect of CRP; however, estimates with a smaller band will also be less powerful. To apply this technique we exploit contract-level offers obtained from USDA that include both accepted and rejected offers and the county location of the offered parcels for each of the general competitive signups between 1992 and 1997. After finding offers for a series of bands around acceptance thresholds, we merge these data with land value data obtained from Agricultural Censuses from 1992 and 1997. We find a negative relationship between land-value changes and increases in CRP bids, suggesting that our identification strategy captures selection bias induced by the EBI enrollment mechanism. However, restricting analysis to small EBI score intervals around the threshold for acceptance decreases the amount of information available for identification, and our estimates of the effect of CRP enrollment on land value have large standard errors.

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Estimating the Effect of CRP Enrollment on Land Value

To illustrate the potential for bias in estimates of the effect of CRP on land values, we begin with the county-level model V aluei = γ1 + γ2 · CRPi + Controls0i γ3 + νi ,

(1)

where V aluei is the average per acre land value in county i, CRPi is the share of county land enrolled in the CRP, the vector Controlsi contains other variables affecting land value such as land quality and location, and the regression error, νi , contains variation due to unobserved or omitted variables. The coefficient of interest is γ2 , the estimate of the marginal effect of increases in CRP enrollment on per acre land value in county i conditional on the variables in Controlsi . For estimates of γ2 to be unbiased, CRP enrollment must be conditionally independent of the regression error, νi . This can be written as the conditional independence assumption E[CRPi · νi |Controlsi ] = 0;

(2)

conditional on Controlsi , the share of land enrolled in the CRP must not be correlated with omitted variables in the regression error, νi . 3

In practice, regressions based on equation 1 violate the conditional independence assumption because of selection bias introduced by the CRP enrollment mechanism. Since 1990, “general”1 CRP sign-ups have used the Environmental Benefits Index (EBI) to rank bids and determine acceptance into the program. General CRP sign-ups begin with a bidding period during which eligible2 landowners submit bids to receive cost share and yearly rental payments in return for installing conservation practices on land for ten to fifteen year contract periods. Bids are collected by the Farm Service Agency (FSA) and ranked by EBI score. After all bids to enroll are collected, the FSA determines an EBI score threshold, and landowners submitting bids that score above the threshold are enrolled in the CRP and paid their proposed rental rate. Landowners can generate expectations about the EBI acceptance threshold from public information about prior sign-ups, but do not know the threshold score for the current sign up when they submit their bids. Total EBI scores are a combination of scores in five environmental categories and one cost category.3 The EBI cost factor is a primary source of the selection bias that complicates empirical estimation of the effect of CRP on land value. The cost factor is determined using CostF actor = ω(1 − r/HIGH) + 10(1 − s) + M in(15, rm − r),

(3)

where r is the rental rate proposed by the landowner, rm is the parcel’s soil-based maximum rental rate,4 HIGH is the highest soil specific rental rate allowed for all bids received nationally, ω is a scaling parameter set by the government after all bids are submitted, and s=1 if the farmer chooses to request cost share assistance, and 0 otherwise.5 The cost factor favors lower quality land by penalizing higher proposed rents and requests for cost share assistance. The first term in the equation is larger when the rental rate requested by a landowner is low relative to the highest rate allowed nationally, HIGH. The maximum yearly rental rate allowed for each bid, rm , is determined by a formula that uses the 1 There are two types of CRP enrollment: general and continuous. The vast majority of CRP acreage is enrolled in general sign-ups; over 30 million of the 34.7 million total acres enrolled in CRP in 2008 were enrolled in general sign-ups. Continuous sign-ups target high priority areas and land use practices, and acres enrolled during continuous sign-ups do not go through the same competitive EBI bidding process as acres enrolled during general sign-ups (USDA 2007). 2 Eligibility for the CRP is determined by active land ownership; landowners are required to own and operate the land they offer, and to have cropped the land in three of the five years prior to enrollment. 3 The five environmental EBI categories reflect the history of the CRP as an erosion control program and the current environmental focus of the program: wildlife, water quality, erosion, enduring benefits, and air quality. A brief description of each of the categories is provided in the appendix. 4 Bidding rules require that r ≤ rm . 5 Some notation is borrowed from (Kirwan et al. 2005)

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relative productivity of soils within each county, the prevalence of the three most prominent soil types on the subject parcel, and the average county dryland cash rent. HIGH is the nationwide maximum rm . The weighting parameter ω is set by the FSA and determines if and how much of a bonus is given to landowners requesting relatively low rental rates, whether because they choose to or because their bids are limited by low rm . The scale parameter ω has been constant at 125 since sign-up 15. The second term in equation 3 gives landowners a 10 point bonus if they do not request cost share assistance,6 and the final term, added for sign-up 16 in 1997, adds one point to a landowner’s score - up to 15 - for every dollar they bid below the maximum allowed for their specific parcel, rm . If a landowners bid is accepted, the rental rate requested in the bid determines the annual payments received over the life of the contract; rental rates are not adjusted during the contract period. On average, landowners with lower quality land will be more willing to forgo cost share assistance or decrease their requested rental rates, causing lower valued land to be enrolled in the program. The selection bias introduced by the EBI will only lead to biased estimates of the effect of CRP enrollment if the variation in land characteristics that drives differences in both land value and EBI score are observed. However, data limitations and private information held by landowners make this difficult, resulting in estimates that are confounded by omitted variables bias. More specifically, the EBI induces negative correlation between CRP enrollment and land value, E[CRPi · νi |Controlsi ] < 0,

(4)

and estimates of γ2 will be biased downward. Selection bias, and not the causal effect of CRP on land value, likely explains the strong negative correlation between CRP enrollment observed in the literature (e.g. Sullivan et al. 2004, Parks and Schorr 1997, Plantinga et al. 2001, Brimlow 2009). We use two unique methods to address selection bias and generate unbiased estimates of the effect of CRP on land value. First, we restrict our comparisons to county acreage contained in bids that fall within a narrow range of the EBI acceptance cutoff. Because EBI scores are correlated with land characteristics, focusing on counties with bids receiving similar EBI scores reduces potentially confounding variation. The ability of individual landowners to determine EBI scores supports our assumption 6

The government typically pays half the cost of establishing the proposed conservation practice if landowners request cost share assistance.

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that restricting comparisons to land with EBI scores near the EBI threshold permits comparisons of land with similar ex-ante value. Landowners are able to make small adjustments to their EBI scores through rental rate and conservation practice choices. These choices may be independent of land characteristics. From the standpoint of a landowner submitting a bid to enroll in the CRP, acceptance into the program is a Bernoulli trial, where the expected probability of being accepted (success) varies with EBI score. Near the unknown EBI cutoff, the probability of acceptance expected by each landowner approaches .5, maximizing the variance of the trial and causing landowners to treat the trial as random. Facing a random trial, landowners adjust their EBI scores according to individual expectations or information about the EBI cutoff and their willingness to trade lower rental rates for higher expected probability of acceptance. Because the EBI enrollment mechanism favors lower valued land, counties with poorer land quality will tend to have larger shares of land accepted into the CRP. To account for this, we condition our estimates on the share of land in each county bidding to enter the CRP with EBI scores just above and just below the cutoff for acceptance. The share of land bidding to enter the CRP serves as a proxy for observable and unobservable land quality characteristics that determine the share of each county’s land falling in bids near the EBI cutoff. Focusing on a narrow range of EBI scores near the cutoff for acceptance and conditioning on the share of acres bidding to enter the CRP allows us to compare counties with similar ex-ante value but different levels of CRP enrollment. The discontinuity of enrollment at the EBI threshold generates significant variation in CRP enrollment for relatively small changes in EBI score.7 We implement our estimation strategy using the regression model V aluei = β1 + β2 · Accepti + β3 · Bidi + X0i β4 + i ,

(5)

where Accepti and Bidi are the shares of farmland in county i accepted and bidding to enter 7

(Thistlethwaite et al. 1960) originally demonstrated that program assignment based on a known, deterministic function of a continuous forcing or selection variable could be used to identify the causal effect of program enrollment. Their framework, Regression Discontinuity (RD) design, requires that enrollment (in/out) is fully dictated by the selection variable. Bid-level data linking land value, EBI score, and CRP acceptance are confidential, so the direct treatment/outcome relationship based on EBI score cannot be established. There is not a direct county-level analog to a bid-level RD design to estimate the CRP Effect, because observed enrollment is a share of total county farmland acreage, and observed per acre land values are averages of the values of county acres enrolled and not enrolled in CRP. A more thorough description of RD design and its limitations for estimating the CRP Effect using county level data is provided in the appendix.

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the CRP within a narrow range of the EBI cutoff for acceptance, and Xi is a vector of additional control variables included to reduce the variance of . Denoting the EBI score for bid j in county i as EBIij , and the EBI cutoff by EBI, and defining δ such that (EBI − δ) < EBIij < (EBI + δ) places EBIij within a ‘narrow’ range of EBI, the share of land accepted into the CRP, Accepti , is computed as PNi

dij ·bidij , Acresi

j=1

Accepti =

for (EBI) < EBIij < (EBI + δ),

(6)

where bidij is the number of acres in bid j in county i, Ni is the number of bids, Acresi is the number of acres of farmland in county i, and dij is a random variable that equals one if bid j in county i is accepted by the CRP (its EBI score was above the threshold for acceptance) and zero otherwise. The share of land in county i bidding to enter the CRP near the EBI threshold, Bidi , is P Ni

Bidi =

3

j=1 bidij , Acresi

for (EBI − δ) < EBIij < (EBI + δ).

Data

We use county-level data for the 1737 US counties that submitted bids to enter the CRP during “general”8 sign-ups in 1997. The data include county land values from the 1992 and 1997 Census of Agriculture as well as EBI scores and shares of county land bidding to enter and enrolled in the CRP provided by the USDA Economic Research Service (ERS). To limit estimates to the effect of CRP on land values at the time of enrollment, we pair county-level farmland value data from 1997 with CRP enrollment and bidding data from CRP general sign-up 15 in the same year.9 We use two measures of county farmland value in our estimations: mean value per acre and acre-weighted median value. To estimate the effects of both direct and spillover effects of CRP enrollment on land value, the mean and median value measures are for farmland acres in each county, and are not restricted to acres enrolled in the CRP or to acres falling 8

There are two types of CRP enrollment: general and continuous. The vast majority of CRP acreage is enrolled in general sign-ups; over 30 million of the 34.7 million total acres enrolled in CRP in 2008 were enrolled in general sign-ups. Continuous sign-ups target high priority areas and land use practices, and acres enrolled during continuous sign-ups do not go through the same competitive Environmental Benefits Index(EBI) bidding process as acres enrolled during general sign-ups (USDA 2007). 9 Sign-up 15 occurred in the spring of 1997, and the Census of Agriculture questionnaire is sent out at the end of each census year, so land values reported by farmers for the 1997 Census of Agriculture will reflect CRP enrollments from sign-up 15.

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in a particular EBI score range. The Census of Agriculture questionnaire asks farmers to estimate the value of the land and buildings on their farms. The farmer estimates are used by the USDA to construct county-level total and per acre “value of land and buildings” statistics for publication in the Census of Agriculture. The 1992 and 1997 county mean per acre value is the sum of the estimated values reported by each farm in the county divided by the total land in farms in the county. The acre-weighted median value is not reported in the Census of Agriculture, and was provided by the ERS. The acre-weighted median farmland value in each county is the per acre value of the farm that contains the middle farmland acre in each county when the farms are ordered by per acre value. For example, in a county with ten thousand acres of farmland in 50 farms ordered by value, the acre-weighted median value is the per acre value of the farm containing the five thousandth acre. Table 1 summarizes land values from the full sample of 1737 counties receiving bids to enroll in the CRP in 1997. The table show that the mean value per acre of farmland varies widely. Hunterdon County, New Jersey has the highest mean value per acre, 7,245 dollars, and Garfield County, Montana has the lowest, 107 dollars. The mean is 1,242 dollars. The acre-weighted median has a lower mean, 1,065 dollars, and a smaller standard deviation of 684 dollars compared to 769 dollars for the average per acre value. Distributions of land values are generally positively skewed. The lower mean value for the acre-weighted median reflects the fact that the statistic is less affected by values in the upper tail of county land value distributions. Table 2 reports summary statistics for counties with zero, zero to 5, and over 5 percent of farmland acres enrolled in the CRP at the end of 1997, respectively. The majority of counties with CRP acres have a share of farmland enrolled between 0 and 5 percent (the mean is 0.043 and the standard deviation 0.045), and Bailey County, Texas tops the list with over thirty percent of farmland enrolled. The negative correlation between CRP enrollment and land value is illustrated in table 2. The changes in the mean of the average per acre county land value across the three enrollment bins are considerable: the value falls from 1,883 to 1,393 dollars when enrollment share rises from zero to between zero and five percent, and then from 1,393 to 880 dollars for enrollment greater than five percent. Average value per acre has a consistently higher mean and standard deviation than the acre-weighted median across the enrollment share bins. Aggregate CRP bidding statistics from general sign-up 15, including acres accepted and rejected grouped according to how far each bid’s EBI score fell from the national acceptance cutoff, were provided by ERS. Table 1 reports that the average per acre CRP rental rate paid

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to accepted acres in sign-up 15 was about fifty dollars, with a standard deviation of 21, and the highest per acre rental payment was 122 dollars paid to a landowner in Douglas County, Illinois. The shares of each county bidding to enter and accepted into the CRP within a small EBI range of the cutoff correspond to sign-up 15 only, and do not reflect cumulative CRP enrollment in each county in 1997. We compute the share of farmland bidding to enter and accepted into the CRP by dividing acres bid and acres accepted by the total farmland acres in each county, respectively. Micro-census data providing individual CRP bid location and other information are confidential; the data were provided by the USDA Economic Research Service after aggregating individual bid data up to the county level.

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Results

Restricting analysis to small EBI score intervals around the threshold for acceptance is crucial to our identification strategy but decreases the amount of information available for identification. Accepti and Bidi are highly collinear by construction, and we rely on narrow variation in the share of county land enrolled near the EBI threshold to identify the effect of the CRP on land value. We use several strategies to maximize the precision of our estimates. First, we use the change in county land value between 1992 and 1997 as the dependent variable to account for time-invariant characteristics affecting land value prior to sign-up 15. Second, we include 1992 land value to capture value changes that are dependent on the level of land value. Finally, we investigate several specifications of location controls, including state fixed effects and interactions between lagged value, state fixed effects, and county latitude and longitude data. Tables 3 and 4 report estimates of the effect of CRP on county land value for varying ranges of EBI score and specifications of equation 5. The estimates provide some evidence that our identification strategy has potential to reduce selection bias, but inconclusive estimates of the effect of CRP on county land value. Table 3 reports estimates for a single specification of equation 5 and varying EBI score ranges, using both average per acre and acre-weighted median land value as dependent variables. Along with Bidi and Accepti , 1992 land value and county fixed effects are included as independent variables. For restricted EBI ranges larger than 5 points, estimates of the effect of Bidi show a statistically significant negative association between the share of farmland bidding to enter the CRP and acre-wieghted median land value. This suggests that Bidi is successfully capturing negative correlation between CRP enrollment and land value induced by the EBI enrollment mechanism. Estimates

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of the effect of CRP on acre-weighted land value are positive but not statistically significant, although the estimates for 20 and 30 point EBI ranges suggest large positive effects, with p-values of 0.113 and 0.164, respectively. None of the estimates generated using average per acre value as the dependent variable are statistically significant. Estimates of the effect of Bidi are consistently negative, but estimates of the effect of Accepti reflect a large degree of uncertainty. Table 4 reports estimates for varying EBI score bands and multiple sets of control variables intended to improve model fit. The dependent variable is the change in the acreweighted median value. For each EBI band, specification [1] is equivalent to the estimation in table 3. Specifications [2] - [4] show estimates from models with interactions of 1992 acreweighted median land value, state fixed effects, and polynomials of latitude and longitude of the centroid of each county. The difference between specifications [2] - [4] is the degree of the polynomial, which increases by one from each specification to the next, beginning with degree one in specification [2]. Table 4 shows that model fit (based on R2 and Adj. R2 ) improves when the interaction terms are added and the degree of the polynomial increases, but the standard errors increase as well, and our estimates of the effect of CRP on changes in land value remain inconclusive.

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Conclusion

The CRP distributes over 1.8 billion dollars in yearly payments, but both direct and indirect effects of the program are poorly understood. Conditionally exogenous variation in CRP enrollment is necessary to obtain unbiased estimates of the causal effect of CRP on land values, but selection bias and data challenges make this difficult to achieve in practice. We utilize discontinuity in CRP enrollment generated by the EBI enrollment mechanism to attempt to overcome these challenges. Our results fail to identify a statistically significant effect of CRP enrollment on land values, but suggest that the identification strategy represents a significant step toward overcoming data limitations and bias in estimating the effects of CRP. Our estimates also suggest that the CRP may have large effects on non-CRP land. Estimates using farm-level data would not include these effects; if parcel level data become available, future work could estimate the CRP Effect with farm-level data, and comparisons of farm- and county-level estimates of the effect of CRP on land value would provide insight into the relative size of the effects of CRP enrollment on non-CRP land. Several sources may contribute to our inability to identify the effect of CRP on land

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value. First, the county land value data we used in this analysis are comprised of farmer estimates of the value of their land and buildings, and are a relatively imprecise. Considering the narrow variation used by our identification strategy, alternative measures of land values could increase the precision of our estimates. Another potential issue arises with the timing of land value effects. Our land value data capture only the expected effects of CRP enrollment because the value estimates are taken shortly after sign-up 15; to the extent that large spillovers are only realized over time, 1998 land value estimates will not capture them. Analysis of 2002 Agricultural Census land value estimates may better capture spillover effects. The identification strategy we use may have broader applicability. For example, there is disagreement in the literature about whether the CRP causes landowners to bring previously idle land into production (“slippage”), offsetting the program’s environmental benefits (Wu 2000, Roberts and Bucholtz 2005). Our could be applied to measure the effect of CRP enrollment on changes in cropland acreage to provide estimates of slippage caused by the program. Global interest in the preservation of ecosystem services and climate change is generating interest in voluntary programs designed to curb environmentally destructive land use practices and offset carbon dioxide emissions. As programs grow larger, standardized enrollment mechanisms become necessary, and measurement of program impacts will be required for policy. Our method represents a viable approach to estimating the effects of voluntary programs when data limitations and selection bias make other estimators impractical.

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References Brimlow, Jacob N., “Determinants of Voluntary Conservation, The USDA Conservation Reserve Program,” 2009. Working Paper. Cook, T. D. and D. T. Campbell, Quasi-Experimentation: Design and Analysis for Field Settings, Rand McNally, Chicago, Illinois, 1979. Hahn, J., P. Todd, and W. van der Klaauw, “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design,” Econometrica, 2001, 69 (1), 201– 209. Kirwan, B., R.N. Lubowski, and M.J. Roberts, “How Cost-Effective Are Land Retirement Auctions? Estimating the Difference between Payments and Willingness to Accept in the Conservation Reserve Program,” American Journal of Agricultural Economics, 2005, 87 (5), 1239–1247. Lee, D. and D. Card, “The Regression Discontinuity Design: Theory and Applications,” Journal of Econometrics, 2008, 142, 655–674. Parks, P.J. and J.P. Schorr, “Sustaining Open Space Benefits in the Northeast: An Evaluation of the Conservation Reserve Program,” Journal of Environmental Economics and Management, 1997, 32 (1), 85–94. Plantinga, AJ, R. Alig, and H. Cheng, “The supply of land for conservation uses: evidence from the conservation reserve program,” Resources, Conservation and Recycling, 2001, 31 (3), 199–215. Roberts, M.J. and S. Bucholtz, “Slippage in the Conservation Reserve Program or Spurious Correlation? A Comment,” American Journal of Agricultural Economics, 2005, 87 (1), 244–250. Sullivan, P., D. Hellerstein, L. Hansen, R. Johansson, S. Koenig, R. Lubowski, W. McBride, D. McGranahan, M. Roberts, S. Vogel et al., “The Conservation Reserve Program-economic implications for rural America.,” Agricultural Economic Report-Economic Research Service, US Department of Agriculture, 2004, (834), 112pp. Thistlethwaite, D.C., D.T. Campbell, R. Lynn, L.S. Stanlee, W.J. Popham, B. Harootunian, M. Tate, AW Anderson, C. Bereiter, E.V. Piers et al., “D. Regression-Discontinuity Analysis: An Alternative to the Ex Post Facto Experiment,” Journal of Educational Psychology, 1960, 51. USDA, “Conservation Programs,” January 2007. Wu, J.J., “Slippage effects of the conservation reserve program,” American Journal of Agricultural Economics, 2000, pp. 979–992.

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Table 1: Summary Statistics - Full Sample Variable

Mean

Std. Dev.

Min.

Max.

N

Average Value/Acre 1997 Acre-Weighted Median Value 1997 Diff in Ave Value/Acre 92-97 Diff A-W Median Value 92-97

1,242 1,065 310 280

769 684 282 259

107 62 -1,389 -911

7,245 5,379 1,835 1846

1,737 1,737 1,737 1,737

Ave Pay/Acre, Sign-up 15 Share enrolled in CRP 1997

50 0.043

21.5 .045

0 0

122 0.30

1,737 1,737

8,299 0.021 11,850 0.03

18,031 0.031 23,359 0.037

0 0 0.5 0

196,123 0.221 314,459 0.275

1,737 1,737 1,737 1,737

Acres Share Acres Share

Accepted Sign-Up 15 Accepted Sign-Up 15 Bid Sign-Up 15 Bid Sign-Up 15

Table 2: Summary Statistics - by Share Enrolled in the CRP Variable

Mean

Std. Dev.

Min.

Max.

N

5,490 5,000 0

14 14 14

7,245 5,379 75,247

1,201 1,201 1,201

3,722 3000 249,793

522 522 522

Share of 1997 Farmland Enrolled = 0% Average Value/Acre 1997 Acre-Weighted Median Value 1997 CRP Acres 1997

1,883 1,492 0

1,403 1,234 0

157 150 0

0% < Share of 1997 Farmland Enrolled ≥ 5% Average Value/Acre 1997 Acre-Weighted Median Value 1997 CRP Acres 1997

1,393 1,205 6,211

801 716 8,805

107 62 50

5% < Share of 1997 Farmland Enrolled Average Value/Acre 1997 Acre-Weighted Median Value 1997 CRP Acres 1997

880 732 38,386

13

498 423 38,927

155 99 715

Table 3: The effect of CRP on land value, by EBI range 1992 - 97 Change in County Acre-Weighted Median Value

δ δ δ δ δ

=∞ = 30 = 20 = 10 =5

Share Accept

se (Accept)

Share Bid

se (Bid)

R2

Adj. R2

-3.57 1525.65 2194.22 4119.12 3649.37

382.82 963.42 1574.43 3409.76 5690.09

-404.09 -1590.25** -2123.97** -3735.41* -4602.66

286.63 623.88 960.94 1954.88 3100.33

0.522 0.521 0.520 0.520 0.520

0.51 0.509 0.508 0.508 0.508

1992 - 97 Change in County Average Value Per Acre

δ δ δ δ δ

=∞ = 30 = 20 = 10 =5

Share Accept

se (Accept)

Share Bid

se (Bid)

R2

Adj. R2

-101.80 372.45 9.63 1083.24 -466.74

452.63 1140.01 1862.49 4033.31 6728.88

-322.26 -847.75 -799.16 -2003.75 -2417.18

339.36 738.86 1137.69 2314.33 3669.21

0.436 0.434 0.434 0.434 0.434

0.421 0.420 0.42 0.42 0.420

Notes: *** p