Munich Personal RePEc Archive

Measuring the effect of farmland
preservation on farm profitability
Schilling, Brian J. and Attavanich, Witsanu and Sullivan,
Kevin P. and Marxen, Lucas J.
Department of Agricultural, Food Resource Economics and Rutgers
Cooperative Extension, Rutgers University, Department of
Economics, Faculty of Economics, Kasetsart University, Office of
Research Analytics, New Jersey Agricultural Experiment Station,
Rutgers University, Office of Research Analytics, New Jersey
Agricultural Experiment Station, Rutgers University

January 2014

Online at https://mpra.ub.uni-muenchen.de/100122/
MPRA Paper No. 100122, posted 06 May 2020 14:26 UTC

1
1. Introduction
The displacement of agriculture as a dominant element of American society and the national
economic landscape is among the most significant transitions of the past century (Jackson-Smithand Jensen 2009; Lobao and Meyer 2001). Evidence of this decline includes the falling numberof United States counties designated as farming dependent, the fact that fewer than two percentof Americans are now engaged in farming, and the large number of farms operating without aprofit motive (so-called “lifestyle farms”) (Dimitri et al. 2005; Ghelfi and McGranahan 2004).
However, it is the conversion of farmland to build infrastructure attendant to residential,commercial or industrial development that is the most visible manifestation of farm decline.
These land use changes have elevated concern about the retention of agricultural lands, resulting
in significant public investments in farmland preservation.
Protection of the nation's farm sector has long been a federal policy objective, rationalized inpart by the priority of maintaining domestic food production capacity and reflective of inherent
value Americans place on rurality and the Jeffersonian ideal of small family farms (Danborn
1996). Since the mid-1900s, urban expansion and the low-density, exurban growth patterncommonly identified as “sprawl” have joined the vagaries of market fluctuations, weather, pests
and disease as a significant threat to farming in many parts of the nation (Rudel et al. 2011;
Heimlich and Anderson 2001; Sorensen, et al. 1997; Daniels and Bowers 1997; Lopez et al.
1988; Berry 1978). Research in the late 1970s conducted as part of the National AgriculturalLands Study increased awareness of the pressures on the nation's agricultural resources andconcerns over the loss of farmland to development. The rate of land conversion to developed
uses has been exceeding the rate of population growth and one-third of the total developed land2
area in the continental United States (approximately 40 million acres) was developed between
1982 and 2007 (USDA 2009).
Jackson-Smith and Sharp (2008) find that more than half of national farm sales are derived
by farms now operating at the rural-urban interface. An estimated 91 percent of the nation's fruit
production and 78 percent of vegetable production occurs in counties designated by the USDA's
Economic Research Service as "urban-influenced" (American Farmland Trust 2013).
Notwithstanding these land use trends, farmland remains an abundant resource on a national
scale and domestic food self sufficiency is not imminently imperiled. However, at state andlocal levels, concern over farmland fragmentation and conversion (and the loss of associatednon-market amenities) has assumed an elevated position in public policy discourse, particularlyin the Northeast region. In the 1970s, states began creating purchase of development rights(PDR) programs to preserve farmland and rural amenities, advance growth management
objectives, and support farming as a business.1 As of May 2012, 27 states have created PDR
programs as a tool to preserve farmland. These programs have preserved nearly 2.3 million
acres at a cost of $5.7 billion (AFT 2012). Program activity has been heavily concentrated in the
New England and greater Mid-Atlantic states.
Despite these substantial investments in PDR, empirical assessment of program success ineffectuating the legislative intent of publically funded farmland preservation is limited.
Common metrics of progress (e.g., acreage enrollment statistics) provide little insight, forexample, into the effects of public farmland preservation investments on the economic
performance and viability of preserved farms. Previous research has focused on the effects ofeasement restrictions on preserved farmland values (Nickerson and Lynch 2001; Lynch, Gray,and Geoghegan 2007; Anderson and Weinhold 2008; Lynch, Gray, and Geoghegan 2010) and3whether landowners invest easement monies in farm improvements and modernization (Lynch
2007; Lynch and Duke 2007; Duke and Invento 2004). Important questions, however, remainunanswered. Among them is whether farmland preservation is having a meaningful impact on
the economic viability of farms, particularly in urban-influenced areas where farming andassociated support infrastructure has undergone significant decline.
The objectives of this study are therefore to empirically examine the impact of PDR program
participation on farm profitability and evaluate whether effects of participation areheterogeneous across different farm types. This is accomplished by estimating the average"treatment effect" of participating in farmland preservation on per-acre farm profitability. A
challenge with this type of observational study lies in an inability to assume that treatment
assignment (i.e., voluntary participation in a PDR program) is random. We employ a propensity
score matching approach (Rosenbaum and Rubin 1983a) to address issues of selection bias
arising from a landowner's self-assignment into the treatment by controlling for inherent
differences that may exist between preserved and unpreserved farms. New Jersey, a leader infarmland preservation, provides the geographic context for the analysis.
The balance of the paper is organized as follows. The following section provides
background on the use of PDR as a farmland preservation technique. The third section
introduces the propensity score matching technique and provides theoretical examination offactors that may influence a landowner's decision to preserved farmland. The fourth section
describes data and matching estimators used in the analysis. The fifth section presents empiricalresults. The final section provides concluding remarks and policy implications.

4
2. Background on PDR Programs
Over the past several decades, the unquestioned acceptance and encouragement of growth
has shifted to a more tempered realization of the potential negative effect development has onrural economies, land use, and culture (Fodor 1999; Libby 2005). This has led to substantial
academic discourse and planning practitioner attention centered on land use (Burchell et al.
2005). Farm retention and the cycle of decline predicted as urban expansion and exurban growthpressures expand into rural-agricultural regions has been a specific thread of academic research
since the 1970s (Berry 1978; Fischel 1982; Lisansky 1986; Lopez, Adelaja, and Andrews 1988;
Daniels and Bowers 1997; Daniels and Lapping 2003; Lynch and Carpenter 2003; Oberholtzer,
Clancy, and Esseks 2011). Embedded within this thread is the concept of a critical mass inagriculture, the premise that a local farming industry will become unsustainable once agricultural
infrastructure (e.g., farms, farmland, agricultural suppliers and markets) declines to a certain
level (Daniels and Lapping 2001; Lynch and Carpenter 2003). The "impermanence syndrome"is one symptom of this problem in urbanizing areas, as uncertainty about the long-term viabilityof agriculture causes farmers to reduce their planning horizons in farming and, subsequently,
curtail investments in farm technology and modernization (Berry 1978; Lopez et al. 1988;
Adelaja et al. 2011).
Various farm retention mechanisms have been adopted in all fifty U.S. states to mitigate theadverse impacts of development on agriculture, including use-value assessment for farmland,
right to farm legal protections, agricultural zoning, and farmland preservation programs. Support
for farm retention in urban-influenced areas is commonly rooted in the public's interest in
maintaining rural amenities conferred by farms that are often quasi-public goods underprovisioned in land markets (i.e., ecological and environmental services, cultural heritage, local

5food availability, and outdoor recreational opportunities), growth management, and retention of
capacity for local food production (Gardner 1977; Bromley and Hodge 1990; Lopez, Shah andAltobella 1994; Kline and Wichelns 1996; Hellerstein et al. 2002; Nickerson and Hellerstein
2003; Duke and Ilvento 2004a; Bergstrom et al. 2011).
While zoning and use value assessment programs may slow farmland loss and support theeconomic viability of farming, neither is a permanent form of land preservation. In contrast, apurchase of development rights program affords permanent protection of farmland fromconversion to non-agricultural development. Participation in a PDR program requires alandowner to forfeit the right to develop farmland for nonagricultural purposes and aconservation easement (a negative easement) is placed on the land. In exchange, the landownerreceives a monetary payment (or, in some cases, a tax incentive) and retains ownership and allother land rights.
PDR programs are an attractive public policy from a property rights perspective because
landowner equity is protected due to the voluntary and compensatory nature of program
participation, thus avoiding political and legal challenges to the constitutionality of regulatorybased land management approaches (Daniels and Bowers 1997; Echeverria 2005). In addition to
the permanence of farmland protections, PDR programs offer several other advantages. It istheorized that the infusion of easement monies may help reverse the impermanence syndrome
which Berry (1978) identified as afflicting urban-influenced farms. However, Duke and Ilvento(2004b) find that the majority of preserved farmland owners in Delaware used easement monies
for personal savings or investments. Further, restricting future non-agricultural development
options should, again in theory, reduce the cost of farmland. However, empirical research has

6yielded mixed results on the presence and extent of price reductions (see, for example, Nickersonand Lynch 2001; Anderson and Weinhold 2008; Lynch, Gray, and Geoghegan 2010).
A downside of PDR programs is the large public expenditures required to purchaseeasements and the uncertainties regarding public funding availability. More than 10 years ago, it
was estimated that the cost of preserving U.S. cropland faced with urbanization pressure would
cost $130 billion (Heimlich and Anderson 2001). Further, PDR deeds of easements restrict
future nonagricultural development, but do not require that land be actively farmed (Daniels &Bowers 1997). Lastly, Liu and Lynch (2011) observe that PDR programs are less able to protect
large contiguous blocks of farmland and industry agglomeration benefits than zoning policies.
2.1 Farmland Preservation in New Jersey
New Jersey is a highly urbanized state bordered by the major New York City andPhiladelphia metropolitan centers. Approximately 16 percent of the state land base (733,000
acres) remains in agricultural production (USDA-NASS 2009). Most of this farmland lies on thewestern half of the state in areas under significant urban influence (Figure 1). The mostagriculturally productive region of the state lies southwest of Philadelphia and includes
Cumberland, Gloucester and Salem counties wherein the state's high-value horticultural
production is concentrated. Another farming intensive region lies in the central/western regionof the state and comprises northern Burlington County, a nationally recognized leader infarmland preservation, and parts of western Monmouth, Mercer, and southern Middlesex
counties.
Since its creation in 1983, 2,200 farms and approximately 205,000 acres of farmland havebeen preserved under New Jersey's farmland preservation program (SADC 2013). New Jersey
has preserved the highest proportion (28%) of its farmland base among all fifty states (AFT72012). More than 83 percent of preserved farmland acreage is contained within three large
clusters of program activity (Figure 2). The largest is in the northwest part of the state(approximately 70,000 acres). The central and southwest clusters comprise roughly 55,000 acres
48,000 acres, respectively.

3. Methodology
In experimental studies, treatment assignment can be randomized and, therefore, comparisonof potential outcomes between treated and control groups can provide statistically reliable
estimates of treatment effects. Farm enrollment in a PDR program, however, is not random dueto the voluntary nature of the program. Estimation of the effect of participation in a PDR
program on farm profitability may be confounded by the possible correlation between farm
profitability outcomes and factors influencing a landowner's decision to preserve farmland. For
example, the owner of a more (or less) profitable farm may hypothetically be less (or more)
likely to sell development rights.
To overcome the problem of selection bias, we can use the potential outcome frameworkwith two potential outcomes Y1 (a profitability outcome for preserved (treated) farms) and Y0 (aprofitability outcome for unpreserved, or control, farms). The observed profitability outcome forany individual farm i can be written as: Yi = Ti  Yi1 + (1 − Ti )  Yi 0 , where T  {0,1} indicatestreatment status, with T=1 if a farm is preserved. The gain/loss of individual farm i fromparticipating in the program is  i = Yi1 − Yi 0 . Because we cannot observe both outcomes forindividual farm i, estimating the individual farm treatment effect  i is not possible and we haveto concentrate on (population) average treatment effects (Caliendo and Kopeinig 2008). The
most commonly-used evaluation parameter is the “average treatment effect on the treated"

8(ATT), which in our context represents the difference between the expected profitability ofpreserved farms and the expected profitability of unpreserved farms had they been preserved.
Algebraically, this can be explained as ATT = E (Y 1 T = 1) − E (Y 0 T = 1) .
As a practical matter, we cannot observe E (Y 0 T = 1) because treatment assignment ismutually exclusive; a farm is either preserved or it is not. Estimating the ATT associated with
PDR program participation by comparing the mean difference between E (Y 1 T = 1) andE (Y 0 T = 0) will be erroneous due to selection bias (i.e., there may be inherent differences
between farms that enter a PDR program and those that do not).
To address selection bias, a growing number of studies have used the propensity scorematching (PSM) technique proposed by Rosenbaum and Rubin (1983a) (see, for example, Liu
and Lynch (2007) and Uematsu and Mishra (2012) for examples within an agricultural policy
context). To evaluate whether the profitability impacts of PDR program participation areheterogeneous across farm types and to lessen the possibility of mismatching, we implement a
perfect match as first applied in Heckman et al. (1997; 1998) by splitting the full sample into
three subgroups of farm types according to the differences in economic scale and operator
characteristics. The first subgroup (residential lifestyle/retirement farm) includes small family
farms operated by individuals for whom farming is not a primary occupation or retired persons.
The second subgroup (small farms with low sales) includes small family farms with low sales(<$100,000 gross sales) that are operated by individuals for whom farming is a primaryoccupation. The last subgroup (commercial farms) includes family farms with high sales(>$100,000 gross sales) that are operated by individuals for whom farming is a primaryoccupation.

9
To ensure that the covariates balancing property is satisfied, we employ the methodsuggested by Becker and Inchino (2002) as a prematching test. We also conduct a post-matchingbalancing test.2 Once the post-matching balancing test is satisfied, the matching of preserved
and unpreserved farms based on estimated propensity scores is utilized to derive the impact of
the PDR programs on profitability outcomes. To ensure overlap in the distributions of covariates
X in the preserved and unpreserved farms, this study imposes the common support criterion. In
addition to the imposition of common support, we address the problem of limited overlap in thecovariate distributions between preserved and unpreserved farms using the trimming method
proposed by Crump et al. (2009).
Application of the PSM technique requires the estimation of the predicted probability of
being in the treatment group, based on observed factors that simultaneously influence thelandowner's enrollment in a PDR program and farm profitability. This is achieved through theestimation of a probit model wherein a farm's preservation status (a binary treatment variable) isregressed on independent variables linked to a landowner's PDR program participation decisionand farm profitability. The set of independent variables in this study is derived from theoreticalunderpinnings advanced by Nickerson and Lynch (2001) and Lynch and Lovell (2003).
Generally, a farm owner is assumed to be an economically rational agent seeking to maximize
the present value of utility derived from owning the land over a given planning horizon, which isdetermined by agricultural returns per acre, various farm and operator characteristics, off-farmincome, non-consumptive values derived from land ownership, and expectations of development
proceeds. We extend this conventional framework by explicitly accounting for empiricalobservations that the prices of preserved farms sold in arms-length market transactions may behigher than those predicted by economic theory due to capitalized value of amenities or retained10development opportunities (see, for example, Nickerson and Lynch 2001, Anderson andWeinhold 2008, Schilling, Sullivan and Duke 2013).3 More specifically, we hypothesize that alandowner's decision to enroll land in a PDR program may be influenced by expectations that
preserved farmland may be sold for prices in excess of capitalized agricultural rents in certain
land markets due to demand for rural residences. More details on the theoretical framework areprovided in the Appendix.

4. Data
Data in our analysis are derived primarily from respondent-level 2007 Census of Agriculture records
collected by the National Agricultural Statistics Service (NASS) and administrative records of the State
Agriculture Development Committee (SADC), the administrative agency responsible for the New Jersey
farmland preservation program. Additional data for covariates used in the first-stage probit model werecollected from the New Jersey Department of Environmental Protection's Bureau of Geographic
Information Systems, the Office of the New Jersey State Climatologist, U.S. Bureau of Labor Statistics,
and U.S. Census Bureau.
The federal Census of Agriculture is conducted every five years, providing detailed information on
agricultural land use, farm and operator characteristics, farm income and expenses, and other informationneeded to understand the structure and trends of the United States farm sector. The initial Census of
Agriculture dataset, accessed through the New Jersey Field Office of NASS, contained 7,575 complete
respondent-level records for New Jersey farms. Through March 2007, SADC records documented 1,621
farmland preservation closings.4 Each PDR closing was aligned to a corresponding Census record basedon examination of block and lot designations and secondary validity checks (e.g., parcel acreage, physicaladdress, owner name, etc.). PDR closings for which Census records are unavailable (i.e., the farm owner
did not participate in the Census) were omitted. In many instances, multiple farmland preservation

11
closings were combined to link to a single farm management unit in Census.5 In the resulting merged
dataset, 789 of 7,575 Census records were identified as having preserved acreage.
In the first stage of PSM analysis, a probit model is estimated with a dependent variable(PRESERVED) defined as '1' if any portion of a subject farm was enrolled in the farmland preservation
program between 2003 and April 2007, and '0' otherwise. This definition was predicated on the practicalchallenges of including pre-2003 farmland preservation closings (i.e., less complete or accessible program
records) and the need to limit potential causality problems between the treatment variable and observed
covariates in the propensity score estimation. Further, this timeframe marked a period of significant
enrollment in the state's PDR program due to the availability of stable state funding for land preservation.
The dataset was therefore refined to exclude 351 records for farms preserved prior to 2003. Farms that do
not meet the SADC's minimum eligibility criteria for PDR program enrollment (n=2,735) were also
excluded from the final dataset to reduce the problem of mismatching farms.6 Lastly, since the farmland
owner decides whether or not to enroll in the PDR program and receives the direct financial benefits ofparticipation (e.g., an easement payment), we also exclude 460 farm records (66 of which are preserved
farms) associated with individuals that do not own any of the land they farm. The final dataset used in theanalysis comprised 4,029 farms, of which 372 were classified as preserved farms.

Table 1 summarizes the outcome measure used to calculate average treatment effects andvariables used in the probit model of factors affecting the probability of a farm entering New
Jersey's PDR program. The farm profitability outcome measure used for estimating ATTs isagricultural profit per acre (PROFIT), which is derived as total farm sales per acre minus total
farm expenses per acre. Following the theoretical framework discussed in section 3 and the
Appendix, three categories of independent variables are constructed: farm and landowner
characteristics, agricultural returns, and development potential of land.
Farm and landowner characteristics include farm acreage and the gender, age, and farming
experience of the primary farm operator. The presence of an heir interested in farming (HEIR)12
was proxied by a constructed binary variable with an assigned value of '1' if the farm has at least
two operators and at least one additional farm operator spends the majority of his/her time
employed in agriculture (and is not a hired manager). Two additional binary variables areincluded to capture whether the operator works off-farm for more than 100 days and whether
rental income comprised the largest source of farm income.
Agricultural returns were proxied by a series of agricultural price or expense indices (i.e.,
crop, cattle, poultry, and labor) and a binary variable indicating whether the farm engaged indirect-to-consumer sales. Underlying farm productivity factors include the percentage of the
farm's soils classified as prime, average seasonal growing temperature, and annual precipitation.
Two variables were included to capture potential industry agglomeration effects, the number offarms located within the subject farm's municipality and the change in municipal agriculturalland area recorded between 1986 and 2007.
Land development potential was captured by population density variables, median housing
value and the distance between the farm and nearest major city. RESIDUAL_VALUE is amunicipal-level variable capturing the difference between the average estimated deed restrictedvalue of preserved farms (i.e., the estimated "after value" calculated by the SADC for purposesof calculating easement payments) and sales prices of preserved farms observed between 1990
and 2007. All values are converted to 2007 dollars by using the urban consumer price index ofowners' equivalent rent of primary residence provided by the Bureau of Labor Statistics.
Table 2 presents the summary statistics for variables used in the analysis for preserved andunpreserved farms across the full sample of farms and the three farm type subgroups. As the firstdata row demonstrates, per-acre farm profitability is higher for preserved farms, relative tounpreserved farms, in the full sample and the 'small farms/low sales' subsample. A test of mean

13difference between preserved and unpreserved farms reveals no statistically significant
difference between preserved and unpreserved farms across the full sample and all subgroups.
However, conclusions about the effect of farmland preservation on farm profitability based uponthese simple means comparisons are to be avoided because of underlying differences between
the two cohorts of farms.

5. Empirical Results
5.1 Probit Model Estimation
To address the bias problem from self selection into the New Jersey PDR program, we
employ the propensity score matching technique. The propensity score is the probability of afarm receiving the treatment (i.e., being preserved). In our analysis, this is derived by estimatinga probit model regressing the binary dependent variable (PRESERVED) on a multi-dimensional
vector of covariates that economic theory and empirical literature predict will influence thelandowner participation decision and farm profitability. The propensity score for each farm is
calculated as the predicted value from the regression. Farms are matched based on theirpropensity scores using several matching estimators, as described in section 5.2.
Probit models were estimated using the full sample of farms and subsamples comprisingresidential lifestyle/retirement farms, small farms with low sales, and commercial farms. Table 3
provides the parameter estimates obtained from each model. All four models perform wellaccording to hit rate, as shown in the last row, and results generally converge with profit theory
and existing literature on farmland preservation.
Model results show that having an heir interested in farming, operator age, reliance on offfarm employment, generation of direct marketing revenue, total annual precipitation, and the14percentage change in agricultural land area do not influence the probability of program
participation. An inverted U-shape relationship is found between the likelihood of program
participation and farm acreage in all groups. While New Jersey is geographically small, there isnoted microclimatic and soil variability across the state. Farms in areas with higher mean
temperatures during the growing season are more likely to enroll in farmland preservation. Not
surprisingly given the importance of soil quality in the prioritization of farms for preservation,
farms with greater proportions of prime soil are more likely to participate in the PDR program;
however, this parameter is only significant for the residential lifestyle/retirement and commercial
subsamples.
Farms in municipalities with a greater number of farms are more likely to participate infarmland preservation. However, this influence diminishes in municipalities with very large
numbers of farms, perhaps reflecting the disinclination of landowners in rural areas to preserve
farmland due to the absence of intense development and lower prevailing easement values. In asimilar manner, the probability of a farm participating in the PDR program tends to increase as
the residual value rises and again diminishes at high levels of residual value in the full sample
and the subsample of residential lifestyle/retirement farms.
5.2 Matching Estimators
The selection of appropriate matching algorithms depends on the number of observations in
the treatment and the control groups and the distribution of estimated propensity scores(Caliendo and Kopeinig 2008; Heckman, Ichimura, and Todd 1997). We estimate the
distributions of the estimated propensity scores for the full sample and each subgroup forpreserved and unpreserved farms using estimated coefficients from the probit model. As
reflected in Figure 3, the distributions across preserved and unpreserved farm groups are

15
generally different. Although distributions tend to be similar at the lower range of the estimatedpropensity scores, they become more divergent at the higher range.
To ensure the robustness of our findings, we utilize several matching algorithms. We firstuse the most straightforward matching estimator, the nearest neighbor matching (NN1) withreplacement. However, NN1 matching may result in poor matches if the closest neighbor is far
away. Therefore, we also use the radius matching with caliper recommended by Dehejia andWahba (2002) to increase matching quality.7 However, as discussed in Smith and Todd (2005),
it is difficult to know a priori what tolerance level is reasonable. We use the calipers of 0.02 and0.05 in this study. Because there are a large number of comparable untreated (unpreserved farm)observations in the full sample and subgroups, we also use oversampling with ten matchingpartners (NN10), kernel and local linear matching algorithms.8 For kernel and local linear
matching, a Gaussian kernel function is used. The optimal bandwidth is selected using the ruleof thumb suggested by Silverman (1986).9
The quality of matching outcomes was evaluated for each matching estimator on the basis ofpercent reduction of pseudo R2, Chi-square, and mean standardized bias. [Reviewer note:
Supplemental document table S1 summarizes these matching quality indicators for the fullsample and each farm type subgroup.] Overall, all matching estimators yielded relatively good
matching quality; NN1 provided the poorest matching quality in all groups. There are no
statistically significant differences between mean values of preserved and unpreserved farms
after matching based on all covariates X in the full sample or subgroups, providing confidence
that our matching results satisfy the balancing property. [Reviewer Note: The results of thebalancing test for mean difference of all covariates X before and after matching are provided insupplemental document table S2.]

16
5.3 Average Treatment Effects on the Treated (ATTs)
Average treatment effects on the treated in the full sample and farm type subgroups areshown in Table 4. Standard errors are reported in parentheses under each estimated treatment
effect. We present the estimated ATTs and their associated standard errors by imposing common
support and the trimming approach (thick support) suggested by Crump et al. (2009) in allcases.10
Estimated ATTs in the full sample are positive across all matching techniques (except for
NN10 matching method with usual common support), but not statistically significant at the 10
percent level. For the residential lifestyle/retirement subgroup, estimated ATTs derived using
four of the six matching indicators are negative and statistically significant at the 10 percent level
when imposing common support. The profitability of preserved farms ranges from $196 to $202
less per acre than matched unpreserved farms. However, after addressing the problem of limited
overlap in the covariates distributions between preserved and unpreserved farms (i.e., theimposition of thick support), the ATTs are not statistically significant across all matching
estimators.
The negative or insignificant effects of PDR participation on residential lifestyle/retirement
farms are not altogether unexpected. Owners of small farms seeking to fulfill rural lifestylepreferences or farmers wishing to spend their retirement years on their farms may havediminished profit motives. For them, agricultural income may be supplemental to off-farm or
retirement income or scaled to qualify their property for the tax advantages of agricultural use
value assessment. While not empirically tested in this study, participation in a PDR program
may also serve as an exit strategy from agriculture for retirement age landowners. A large

17easement payment may provide financial liquidity needed for estate planning and transfer
purposes, or serve to extract capital to support retirement.
All matching estimators yielded statistically significant ATTs for farms in the small farm/low
sales subsample. Preserved farm profitability is estimated to be between $266 and $453 higher
per acre than it is for observationally equivalent unpreserved farms. Refining the results to thosegenerated from the matching algorithms having the best matching quality narrows the
profitability differentials to a range of $414 to $436. One explanation for the improved
profitability of preserved farms may be the influx of capital into farm businesses afforded byselling development rights. The infusion of easement payments to these relatively small farms(farms in this subsample have annual sales of less than $100,000) may expand opportunities toincrease farm acreage, restructure debt, mechanize and modernize operations, diversify products
and markets, or transition into value added enterprises.
For the commercial farm subgroup, the matching estimators produced ATTs that are widely
variable, but not statistically significant. This result is interesting and warrants additional
research. The authors speculate that farms in this subgroup may pursue alternative profitenhancing strategies. For example, rather than intensifying production on existing acreage(earning more profit per acre), anecdotal evidence from southern New Jersey suggests that large,
multigenerational horticulture farms are actively expanding landholdings to achieve higher
profitability.
Using a recent simulation-based sensitivity analysis employed in Ichino, Mealli, andNannicini (2008), we confirm that results are robust to unobserved factors influencing theparticipation decision and farm profitability.11 [Reviewer Note: The results of the sensitivity
analysis conducted on the subgroup of small farms with low sales are provided in supplemental

18
document tables S3 and S4 with detailed explanation.] We also use Rosenbaum bounds with 1x1
matched pairs (see details in Rosenbaum 2002) and find that our results are robust with thethreshold gamma measuring the effect strength of unobserved variables on treatment assignment
equal to 1.25 (with 95% confidence interval). This means that the statistical significance of the
ATT for profit per acre would be questionable if the odds ratio of having a PDR program
between enrolled and non-enrolled farms differs by more than 1.25.

6. Conclusions and Policy Implications
As a policy evaluation, this study examines whether New Jersey's PDR program isstrengthening the agricultural industry, a legislative goal articulated in the enabling statute[NJSA 4:1C-11 et seq]. In practice, this is often interpreted as enhancing the economic viabilityof farming. Farmland preservation is theorized to aid the farm economy in several ways. The
retention of large contiguous areas of farmland can provide agglomeration economies, reduce
location conflicts (i.e., right to farm disputes), enhance operational efficiency, and remove
speculative pressures from farmland markets. In theory, deed-restricted farmland should be lessexpensive than unpreserved farmland, thus reducing a substantial new farmer entrance barrier
and enabling less costly expansion of existing farms. Easement payments provide farmers withfinancial liquidity that may be used to expand or modernize agricultural operations, or restructure
debt, although such funds are also used for household consumption and personal investments(Esseks and Schilling, 2013; Duke and Ilvento, 2004b).
Sokolow (2006), in his national assessment of leading U.S. farmland preservation programs
concluded that the agricultural economic impacts of PDR programs are unclear. Focusing
primarily on the effects of agricultural land retention on farm supplier and market infrastructure,19
he argues that the “accumulation of permanently preserved farmland through easements by itself
has had little direct impact so far on the overall condition of local agricultural economies.”
However, he qualifies this generalization with the perspectives of county and state farmland
preservation program administrators that PDR participation does benefit individual farmeconomics in the form of capital for expansion and debt retirement, infrastructure improvements,
or transition to other commodities and alternative agricultural business ventures. The extent towhich these theorized economic benefits of PDR participation are being realized by farms has
not been well studied empirically.
To the authors' knowledge, this study is the first to directly measure the impact of farmland
preservation on farm profitability, while controlling for program self-selection effects. As such,it represents a useful extension to the PDR program evaluation literature. Public expenditures on
PDR programs in the U.S. (particularly in the eastern states) have been substantial, totaling
several billion dollars. In a climate of fiscal austerity, many state programs are facing
uncertainties over future funding and increasing accountability to policy makers seeking
evidence of program impacts. Progress reports relying on farm and acreage enrollments are selfevident metrics. However, PDR program administrators faced with the need to justify further
program funding would benefit from credible information on their programs' impacts on farmlevel viability and broader economic development. Our results suggest that farmland
preservation is advancing farm economic development objectives, at least for small to mid-sizedcommercial farms. This is encouraging because of the concentration of the state's agricultural
output among commercial farms, notwithstanding the lack of statistically significant ATTs foundwithin the sub-category comprising the state's largest farms.

20
Analysis within our sample shows that the majority of farmland preserved in New Jersey isassociated with farms operated by persons for whom farming is a principal occupation (Table2).12 Our findings suggest that the profitability of "low sales" farms (farms with <$100,000 inannual sales that are operated by individuals for whom farming is a principal occupation) is
enhanced by farmland preservation. Our most conservative estimates reveal that preserved farms
in the 'small farms/low sales' cohort earn $414 to $436 more per acre in profit than theirunpreserved counterparts. This finding comports with program administrator accounts reported
by Sokolow (2006) and provides optimism that PDR participation may be a gateway throughwhich small farms may modernize, expand, or transition into more lucrative ventures. It casts an
optimistic light on long-standing, and often bleak, national dialogue on the challenges facing
small-scale farms, as encapsulated in a 1988 Council for Agricultural Science and Technology,
which notes: "[t]he future viability of the adequate size, well-managed commercial farms, andthe part-time smaller farms is not in doubt. The future is much in doubt, however, for full-time
family farms lacking a strong financial or managerial base, too small to realize economies ofsize, and too demanding of labor and management for the operator and family to earn substantial
off-farm income.”
A fundamental observation emerges from this study regarding the use of PDR funding topreserve smaller "lifestyle" farms. We find some evidence that the profitability oflifestyle/retirement farms is lower for preserved farms, relative to their unpreserved counterparts.
This may be a signal that farmland preservation, and ostensibly the infusion of capital into thefarm household, further dampens what in many cases is an already limited agricultural profit
motive. In other instances, it may reflect the use of PDR as an exit strategy for retiring farmers.
PDR easement payments, rather than being invested in the farming operation, may provide

21
financial liquidity needed for estate planning and transfer. However, New Jersey's conservation
easements are perpetual, raising an important question about the future disposition of thesepreserved farms. Who will farm them? Will they be made accessible to farmers seeking
expansion, or new farmers? From a policy perspective, this suggests the importance of having
programs aimed at facilitating farmland access, linking owners of preserved farmland withindividuals interested in farming. For example, the State Agriculture Development Committee
and the New Jersey chapter of the Northeast Organic Farming Association recently collaborated
on a farmland leasing project. Key components of the initiative include networking meetings
between owners of farmland (preserved and unpreserved) and farmers interested in leasing land,
and associated online land linking resources.
A related policy question surfaces regarding the desirability of using limited public resources
to preserve small farms presently supporting rural-residential lifestyles. Holding all else
constant, economic theory predicts (and observation supports) that per-acre easement costs will
be higher for smaller properties, relative to larger farms. Should very small acreage "lifestyle"farms be de-prioritized for preservation, allowing the reallocation of funding resources toward
larger farms? For example, the current eligibility criteria for small (under 10 acre) farms to enter
farmland preservation require documented annual sales of at least $2,500. This criterion could
be made more stringent; however, the consequences of such policy revisions need to be carefully
contemplated. A prime farmland parcel may be minimally farmed today (for purposes of
discussion, assume a purely "hobby" agricultural venture) but could be more intensively farmedin the future. Is it a viable policy alternative to preclude preserving this parcel on the basis of
low current productivity when the underlying soil resources are of high quality?

22
While ostensibly rational from a purely cost-effectiveness perspective, it may be politically
unpalatable to forego preservation of small acreage lifestyle farms (or implement a policychange, such as a per-acre easement cap, that has the practical effect of reducing the likelihoodof such farms being preserved) , which are often located in suburban or urbanizing parts of thestate. As farms in these areas become more scarce, remaining farms become more valued on thebasis of their amenity benefits (e.g., open space, access to produce at farm markets, etc.). The
retention of these urban fringe farms may be necessary for maintaining the political support
needed to secure additional statewide funding for land preservation.
Avoiding the preservation of smaller farms may also be incongruent with state farmland
preservation goals. When Governor Christine Todd Whitman signed the landmark Garden State
Preservation Trust Act into law in 1998, she articulated an ambitious (and ultimately unattained)
goal of preserving 500,000 acres of farmland within ten years. To that point, the average size offarms preserved under the state PDR program was 148 acres. In the second and third decades
since, preserved farms averaged 86 and 75 acres in size, respectively. In most preservation
project areas, early efforts focused on acquiring easements on large, keystone properties. Now
effort is shifting toward "filling in" preservation gaps to achieve the goal of protecting large,
contiguous clusters of farmland and minimizing further fragmentation of the state's farmlandbase. Comprehensive county farmland preservation plans developed in recent years target an
additional 4,314 farms and 242,000 acres of farmland for preservation. Taking a simple average,
this implies a further drop in the average size of targeted farms to 56 acres.
Lastly, another consideration that surfaces when considering the implications of preserving
low-intensity lifestyle farms is the fact that New Jersey's standard deed of easement requires that
preserved farmland remain available for agriculture, not remain in agriculture. This is a long-23
standing topic of debate in the state's agricultural policy circles. Our findings suggest that this"available for" clause allows farmland preserved at considerable public expense to be diverted
from active, more intensive farming. If strengthening the farming industry is a goal of PDR
programs, policy makers may be justified in revisiting deed of easement provisions requiring
farmland be kept in active agriculture.
It is important to conclude with limitations of this study that may warrant further academic
consideration. First, our study design defines a farm to be preserved if any of its acreage ispreserved under the state PDR program. Land tenure, particularly among very large farms, iscomplex. It is common for a large farm owner to aggregate multiple farms parcels under one
farm management unit. This raises the question whether the extent to which a farm's acreage ispreserved matters. It is interesting to consider whether there is a dose-response function evident
when farms participate in farmland preservation. In other words, does the size of treatment (i.e.,
the percentage of a farm that is preserved) influence farm profitability? Second, 2003-2007
marked a period of significant farm enrollment in the New Jersey PDR program. Data
challenges made it impractical to examine farms preserved prior to 2003 and the truncated timeframe in our analysis may not fully capture profitability impacts stemming from the sale of farmdevelopment rights. Third, it is theorized that the observed lack of statistically significant ATTs
for the commercial farms subgroup may result from larger farms operating at higher levels oftechnical efficiency than smaller farms due to greater financial liquidity or scale economies thatenable investments in equipment and technology adoption. For these operations, farm expansion
may be a more dominant strategy for achieving higher profits than production intensification(i.e., increasing profits per acre). This implies the need to examine a different outcome measure
to evaluate whether and how PDR participation affects the economics of larger farms. Finally,24
cost-related data limitations precluded examination of the potential effects of agglomeration
benefits that may accrue as large contiguous blocks of preserved farmland are created. The
incorporation of spatial data on preserved farmland contiguity into future assessments of PDR
program impacts would be valuable from a policy evaluation perspective.

25
Appendix: Theoretical Framework of Landowners' Participation Decision
In the framework, we assume that a landowner has two options. First, a landowner may
choose to farm until an optimal farm sale date, at which time all rights to the farmland are sold ina market transaction. Alternatively, the landowner may select to participate in a PDR program,selling only rights to develop the land for non-agricultural purposes in exchange for an easement
payment. In the latter case, the landowner may continue to farm or sell the deed-restrictedfarmland at an optimal date for a net payment equivalent to the sum of the future stream of net
agricultural returns and residual non-agricultural consumptive value (i.e., utility derived fromliving in a rural residence), which is the difference between the preserved farm sales price and its
agricultural value.
Let the participation decision of landowner i be represented by φ. A decision to participate ina PDR program is denoted by φ = 1 (φ = 0 if the landowner does not participate). We assume
that the participation decision, φ, maximizes the present value of his/her utility over the planninghorizon given the discount rate r and the landowner i’s time preference, ρ. Allowing, Xi, torepresent landowner and farm characteristics in each period  , the utility of the landowner i canbe modeled as a function of: the net agricultural returns per acres, Riag ( X i , ) ; off-farm income,Rioff ( X i , ) ; the non-consumptive value of participation, Rinonconsum ( X i , ) ; the net payment fromselling farmland in the market for the developed use at optimal date t1* , Vi dev ( X i , t1* ) ; the net
easement payment from selling non-agricultural development rights at the beginning period,

Ei ( X i ,0) ; and the residual value of preserved farmland sold at optimal date t 2* , Vi resid ( X i , t 2* ) 13
(Equation A1). A landowner will participate in the PDR program if the present value of utility
derived from participation is greater than the present value of utility associated with farming
until the optimal sales date and then selling their land.

26

 t1
Vi = Max (1 −  )  U i Riag (X i , τ), Rioff (X i , τ), Rinonconsum (X i , τ) e − ρτ dτ

 0


+  U i Rioff (X i , τ), rVi dev (X i ,t 1* ) e − ρτ dτ 

t1*
*
t 2
ag
off
nonconsum(X i , τ), rE i (X i ,0) )e −ρτ dτ+    U i (R i (X i , τ), R i (X i , τ), R i
 0
*

(A1)

(

(

)

)


+  U i Riag (X i , τ) + rVi resid (X i ,t *2 ), Rioff (X i , τ), Rinonconsum (X i , τ) e − ρτ dτ 

t*2


(

)

27
Acknowledgments
Portions of this research were supported by a grant from the Agriculture and Food Research
Initiative (Competitive Grant No. 2010-85211-20515) from the USDA National Institute of Foodand Agriculture. Gratitude is expressed to the New Jersey Field Office of the National
Agricultural Statistics Service for assistance with the construction of data resources used in thisresearch. We thank Dr. Ximing Wu in the Department of Agricultural Economics at Texas
A&M University, and Dr. Yanhong Jin in the Department of Agricultural, Food and Resource
Economics at Rutgers University, for helpful comments and suggestions. We also appreciatecomments received from attendees at the 2012 Northeastern Agricultural and Resource
Economics Association (NAREA) annual meeting in Lowell, MA, USA. The valuablecomments of two anonymous reviewers are greatly appreciated. The authors are responsible forany errors.

28
References
Abadie, A., Imbens, G.W. 2008. On the failure of the bootstrap for matching estimators.
Econometrica 76 (6), 1537-1557.
———. 2006. Large sample properties of matching estimators for average treatment effects.
Econometrica 74 (1), 235-267.
Adelaja, A., Sullivan, K., Hailu, Y. 2011. Endogenizing the planning horizon in urban fringe
agriculture. Land Use Policy 28 (1), 66-75.
American Farmland Trust. 2012. Status of state PACE programs. Farmland Information Center,MA.
———. 2011b. Fact sheet status of local PACE programs. Farmland Information Center, MA.
Anderson, K., Weinhold, D. 2008. Valuing future development rights: The costs of conservation
easements. Ecological Economics 68 (1), 437-446.
Becker, S.O., Ichino, A. 2002. Estimation of average treatment effects based on propensity
scores. The Stata Journal 2 (4), 358-377.
Bergstrom, J., Russell, R., Ready, R. 2009. What have we learned from over 20 years offarmland amenity valuation research in North America? Applied Economic Perspectives
and Policy 31 (1), 21-49.
Berry, D. 1978. Effects of suburbanization on agricultural activities. Growth and Change 9 (3),28.
Bromley, D.W., Hodge, I. 1990. Private property rights and presumptive policy entitlements:
Reconsidering the premises of rural policy. European Review of Agricultural Economics 17(2), 197.
Burchell, R., Downs, A., McCann, B., Mukherji, S. 2005. Sprawl costs: Economic impacts ofunchecked development. Washington, DC: Island Press.
Caliendo, M., Kopeinig, S. 2008. Some practical guidance for the implementation of propensity
score matching. Journal of Economic Surveys 22 (1), 31-72.
Council for Agricultural Science and Technology. 1988. Long-term viability of U.S.
agriculture. The Ohio State University.
Crump, R.K., Hotz, V.J., Imbens, G.W., Mitnik, O.A. 2009. Dealing with limited overlap in
estimation of average treatment effects. Biometrika 96 (1), 187-199.
Danborn, D. 1996. Why Americans value rural life. Rural Development Perspectives 12 (1), 1518.
Daniels, T., Lapping, M. 2001. Farmland preservation in America and the issue of critical mass.
Paper presented at the American Farmland Trust National Conference, Chicago, IL.
Daniels, T., Bowers, D. 1997. Holding our ground: Protecting America’s farms and farmland.
Washington, DC: Island Press.
Dehejia, R.H., Wahba, S. 2002. Propensity score-matching methods for nonexperimental causal
studies. Review of Economics and Statistics 84 (1), 151-161.

29———. 1999. Causal effects in nonexperimental studies: Reevaluating the evaluation of training
programs. Journal of the American Statistical Association 94 (448), 1053-1062.
Dimitri, C., Effland, A., Conklin, N. 2005. The 20th century transformation of U.S. agriculture
and farm policy. USDA-ERS Economic Information Bulletin No. 3.
Duke, J.M., Ilvento, T.W. 2004a. A conjoint analysis of public preferences for agricultural landpreservation. Agricultural and Resource Economics Review 33 (2), 209-219.
Duke, J.M., Ilvento, T.W. 2004b. Supplying preservation: Landowner behavior and the Delaware
agricultural lands preservation program. Department of Food and Resource Economics,
Research Report RR04-01, University of Delaware.
Echeverria, J. 2005. Regulating versus paying land owners to protect the environment. Journalof Land, Resources and Environmental Law 26 (1), 1-46.
Esseks, J.D., Schilling, B. 2013. Impacts of the Federal Farm and Ranch Lands Protection
Program: an assessment based on interviews with participating landowners. American
Farmland Trust and The Center for Great Plains Studies University of Nebraska-Lincoln.
Fischel, W. 1982. The urbanization of agricultural land: A review of the National AgriculturalLands Study. Land Economics 58 (2), 236-259.
Fodor, E. 1999. Better, not bigger. Gabriola Island, British Columbia: New Catalyst Books.
Gardner, B.D. 1977. The economics of agricultural land preservation. American Journal of
Agricultural Economics 59 (5), 1027-1036.
Ghelfi, L., McGranahan, D. 2004. One in five rural counties depends on farming. Amber Waves
2 (3), 11.
Gottlieb, P.D. 2002. Growth without growth: An alternative economic development goal for
metropolitan areas. Washington, DC: Brookings Institution, Metropolitan Policy Program,Statistical Survey Series.
Green, D. M., and J. A. Swets. 1966. Signal detection theory and psychophysics. Vol. 1974
Wiley New York.
Heckman, J.J, Ichimura, H., Smith, J., Todd, P. 1998. Characterizing selection bias using
experimental data. Econometrica, 1017-1098.
Heckman, J.J., Ichimura, H., Todd, P.E. 1997. Matching as an econometric evaluation estimator:
Evidence from evaluating a job training programme. Review of Economic Studies, 605-654.
Heimlich, R., Anderson, W.D. 2001. Development at the urban fringe and beyond: Impacts on
agriculture and rural land. In Economic Research Service Agricultural Economic Report No.
803. Washington, DC: Government Printing Office.
Hellerstein, D., Nickerson, C.J., Feather, P., Gadsby, D., Mullarkey, D., Tegene, A., Barnard, C.
2002. Farmland protection: The role of public preferences for rural amenities. Agricultural
Economic Report No. AER815. Washington, DC: United States Department of Agriculture,
Economic Research Service.
Heckman, J.J., LaLonde, R.J., Smith, J.A. 1999. The economics and econometrics of active labormarket programs. Handbook of Labor Economics 3, 1865-2097.

30
Ichino, A., Mealli, F., Nannicini, T. 2008. From temporary help jobs to permanent employment:
What can we learn from matching estimators and their sensitivity? Journal of Applied
Econometrics 23 (3), 305-327.
Jackson-Smith, D., Jensen, E. 2009. Agricultural importance versus farm dependence: A new
typology. Rural Sociology 74(1), 37-55.
Jackson-Smith, D., Sharp, J. 2008. Farming in the urban shadow: Supporting agriculture at therural-urban interface. Rural Realities 2 (4), 1-12.
Kline, J., Wichelns, D. 1996. Public preferences regarding the goals of farmland preservation
programs. Land Economics 72 (4), 538-549.
Libby, L. 1974. Land use policy: Implications for commercial agriculture. American Journal of
Agricultural Economics 55 (1), 1143-1152.
Libby, L. 2005. Rural land use problems and policy options: Overview from a U.S. perspective.
In Land Use Problems and Conflicts: Causes, Consequences, and Solutions., eds. S. Goetz,
J. Shortle, and J. Bergstrom, Routledge, New York.
Lisansky, J. 1986. Farming in an urbanizing environment: Agricultural land use conflicts andright to farm. Human Organization 45 (4), 363-371.
Liu, X., Lynch, L. 2011. Do agricultural land preservation programs reduce farmland loss?
evidence from a propensity score matching estimator. Land Economics 87 (2), 183-201.
———. 2011. Do zoning regulations rob rural landowners' equity? American Journal of
Agricultural Economics 93 (1), 1-25.
Lobao, L., Meyer, K. 2001. The great agricultural transition: Crisis, change and social
consequences for twentieth century U.S. farming. The Annual Review of Sociology 27, 103124.
Lopez, R.A., Adelaja, A., Andrews, M. 1988. The effects of suburbanization on agriculture.
American Journal of Agricultural Economics 70 (2), 346-358.
Lopez, R.A., Shah, F.A., Altobello, M.A. 1994. Amenity benefits and the optimal allocation ofland. Land Economics, 53-62.
Lynch, L. 2007. Economic benefits of farmland preservation. In The economic benefits of landconservation., ed. Constance T. F. de Brun, 13-23. The Trust for Public Land, San
Francisco, CA.
Lynch, L., Carpenter, J. 2003. Is there evidence of a critical mass in the Mid-Atlantic agricultural
sector between 1949 and 1997? Agricultural and Resource Economics Review 32, 116-128.
Lynch, L., Duke, J.M. 2007. Economic benefits of farmland preservation: Evidence from theUnited States. Working Papers.
Lynch, L., Gray, W., Geoghegan, J. 2010. An evaluation of working land and open spacepreservation programs in Maryland: Are they paying too much? In New perspectives on
agri-environmental policies: A multidisciplinary and transatlantic approach., eds. Stephan
Goetz, Floor Brouwer. New York: Routledge.

31———. 2007. Are farmland preservation program easement restrictions capitalized into
farmland prices? what can a propensity score matching analysis tell us? Applied Economic
Perspectives and Policy 29 (3), 502-509.
Lynch, L., Liu, X. 2007. Impact of designated preservation areas on rate of preservation and rateof conversion: Preliminary evidence. American Journal of Agricultural Economics 89 (5),1205-1210.
Lynch, L., Lovell, S.J. 2003. Combining spatial and survey data to explain participation inagricultural land reservation programs. Land Economics 79 (2), 259-276.
Nickerson, C.J., Hellerstein, D. 2003. Protecting rural amenities through farmland preservation
programs. Agricultural and Resource Economics Review 32 (1), 129-144.
Nickerson, C.J., Lynch, L. 2001. The effect of farmland preservation programs on farmland
prices. American Journal of Agricultural Economics, 341-351.
Oberholtzer, L., Clancy, K., Esseks, J. 2010. The future of farming on the urban edge: Insightsfrom fifteen U.S. counties about farmland protection and farm viability. Journal of
Agriculture, Food Systems and Community Development 1 (2), 59-76.
Rosenbaum, P.R. 2002. Observational studies. Springer Verlag.
———. 1987. Sensitivity analysis for certain permutation inferences in matched observational
studies. Biometrika 74 (1), 13-26.
Rosenbaum, P.R., Rubin, D.B. 1983a. The central role of the propensity score in observational
studies for causal effects. Biometrika 70 (1), 41-55.
———. 1983b. Assessing sensitivity to an unobserved binary covariate in an observational study
with binary outcome. Journal of the Royal Statistical Society.Series B (Methodological):212-218.
Rudel, T., O'Neill, K., Gottlieb, P., McDermott, M., Hatfield, C. 2011. From middle to upperclass sprawl? Land use controls and changing patterns of real estate development innorthern New Jersey. Annals of the Association of American Geographers 101 (3), 1-16.
SADC, "New Jersey Farmland Preservation Program: State, County and Municipally Preserved
Farmland" State of New Jersey, Department of Agriculture, NJ, 2012),
http://www.nj.gov/agriculture/sadc/farmpreserve/progress/preserved/preservedfarmslist.pdf(accessed May 4, 2012).
Schilling, B., Sullivan, K., Duke, J. 2013. Do residual development options increase preserved
farmland values? Journal of Agricultural and Resource Economics 38 (3), 327-343.
Schilling, B.J. 2009. Local planning for agriculture: A new charge to the planning profession.
Ph.D. Dissertation. Rutgers, the State University of New Jersey-New Brunswick.
Silverman, B.W. 1986. Density estimation for statistics and data analysis. Chapman and Hall,
London.
Smith, J.A., Todd, P.E. 2005. Does matching overcome LaLonde's critique of nonexperimental
estimators? Journal of Econometrics 125 (1), 305-353.

32
Sokolow, A. 2006. A national view of agricultural easement programs: measuring success inprotecting farmland - report 4. American Farmland Trust and Agricultural Issues Center,
University of California.
Sorensen, A., Greene, R., Russ, K. 1997. Farming on the edge. American Farmland Trust, Center
for Agriculture in the Environment.
Uematsu, H., Mishra, A. 2012. Organic farmers or conventional farmers: Where's the money?
Ecological Economics 78 (C), 55-62.
USDA-NASS. 2009. 2007 Census of Agriculture, State-level data. Washington, DC.
USDA. 2009. Summary report: 2007 National Resources Inventory, Natural Resources
Conservation Service, Washington, DC, and Center for Survey Statistics and Methodology,
Iowa State University, Ames, Iowa.

33
Table 1. Description of variables
Variables
Description
Potential Outcome and Treatment
PROFIT
Profit per acre derived as total sales minus total farm expenses per acre
PRESERVED
Whether any portion of the farm was preserved between 2003-2007 (equal
to 1 if preserved)
Farm and Landowner Characteristics
GENDER
Gender of the principal operator (equal to 1 if male)AGE
Age of the principal operator
EXPERIENCE
Number of years the principal operator operated on the farm
HEIR
Whether the principal operator has an heir to continue farming (equal to 1
if yes)
OFF-FARM
Whether the principal operator worked off farm more than 100 days
(equal to 1 if yes)
RENTAL_INC
Whether the largest source of farm income was rental income (equal to 1
if yes)
ACRES
Total acres of farmland operated
Agricultural Returns
DIRECT_SALES
Whether the farm has revenue from direct sales (equal to 1 if yes)
CROP_PRICES
Prices received by each farm from selling crops in $/acre
CATTLE_PRICES
Prices received by each farm from selling cattle including their dairy
products in $/head
POULTRY_PRICES Prices received by each farm from selling poultry in $/head
LABOR_COSTS
Labor cost per acre
PRIME
Percent of farm acreage with soils classified as “prime”
TEMPERATURE
Municipality-level average growing seasonal temperature (°F) from April
to September
PRECIPITATION
Municipality-level total annual precipitation (inches)
NO_FARMS
Number of farms in the municipality
CHG_AGLAND
Percent change in agricultural land area for municipality in which farm islocated from 1986 to 2007
Development Potential of Land
POP_DENSITY
Population density per square mile for municipality in which farm islocated
CHG_POPDEN
Percent change in population density per square mile for municipality inwhich farm is located from 1987 to 2007
DISTANCE
Euclidian distance, in miles, of the farm to the nearest city (New York
City or Philadelphia)
RESIDUAL_VALUE Average percent difference between preserved farm sales price and aftervalue for municipality in which farm is located
HOUSINGVALMedian housing value of municipality in which farm is located34
Table 2. Summary statistics of preserved and unpreserved New Jersey farms
Full Sample
Residential/Retirement
Small Farms with Low Sales
Preserved
Unpreserved
Preserved
Unpreserved
Preserved
Unpreserved
Mean Std. D. Mean Std. D. Mean Std. D. Mean Std. D. Mean Std. D. Mean Std. D.

Commercial
Preserved
Unpreserved
Mean Std. D. Mean Std. D.

Variables
Profitability Outcome
PROFIT ($1,000)
0.54
4.55
0.20 8.06 -0.20 0.87 -0.01
4.01 -0.13 0.54 -0.79
3.95
1.91 7.49
3.53 21.20
Farm and Landowner Characteristics
GENDER
0.89
0.31
0.80 0.40 0.87 0.34
0.83
0.37 0.86 0.35
0.69
0.46
0.93 0.26
0.95
0.22
AGE
59.08 12.80 58.38 12.62 60.87 13.41 59.76 12.48 57.14 12.19 55.68 12.03 57.58 11.88 56.37 11.61
EXPERIENCE
25.56 15.66 21.76 14.63 22.24 16.08 21.38 14.43 24.00 14.62 20.28 14.63 29.29 13.69 27.65 13.93
HEIR
0.11
0.32
0.07 0.26 0.07 0.25
0.06
0.24 0.07 0.26
0.08
0.27
0.20 0.40
0.15
0.36
OFF_FARM
0.52
0.50
0.48 0.50 0.49 0.50
0.49
0.50 0.47 0.50
0.47
0.50
0.56 0.50
0.46
0.50
RENTAL_INC
0.02
0.15
0.01 0.09 0.05 0.22
0.01
0.10 0.02 0.13
0.01
0.09
0.00 0.00
0.00
0.00
ACRES
317.56 472.26 79.35 183.76 96.84 113.47 43.95 73.12 176.78 177.67 69.25 116.36 661.54 639.73 257.28 370.44
Agricultural Returns
DIRECT_SALES
0.16
0.37
0.18 0.39 0.18 0.39
0.20
0.40 0.14 0.35
0.18
0.39
0.13 0.34
0.14
0.35
CROP_PRICES ($1,000)
5.89 27.39
6.27 52.98 2.36 14.36
2.80 17.21 10.90 50.97
3.94 24.91
8.57 27.41 29.09 145.53
CATTLE_PRICES ($1,000)
3.02
1.78
2.89 1.13 2.63 1.19
2.81
0.86 3.16 3.21
2.86
0.79
3.40 1.52
3.39
2.24
POULTRY_PRICES
43.08
7.65 44.08 27.30 43.35 9.46 44.23 26.74 42.32 7.01 46.09 45.74 43.43 5.85 42.85
5.18
LABOR_COSTS ($1,000)
0.44
1.47
0.73 7.84 0.05 0.20
0.15
1.37 0.12 0.61
0.34
2.19
0.99 2.24
4.28 22.35
PRIME
32.88 17.61 27.85 16.33 32.15 16.45 27.28 16.31 33.05 18.24 28.70 16.26 35.81 17.76 29.22 17.15
TEMPERATURE
66.42
1.69 66.17 1.75 66.24 1.78 66.00
1.74 66.37 1.67 66.24
1.73 66.72 1.54 66.91
1.63
PRECIPITATION
48.86
6.02 49.08 6.27 49.30 6.16 49.67
6.23 49.75 5.78 49.06
6.09 47.69 5.87 46.19
5.72
NO_FARMS
84.60 51.35 74.52 54.04 85.12 49.27 74.12 52.10 83.55 51.62 76.79 53.85 84.58 51.92 75.60 61.61
CHG_AGLAND-27.57 16.64 -29.51 35.28 -27.48 16.26 -29.37 44.23 -27.73 16.80 -29.42 18.23 -28.87 16.68 -28.13 18.82
Development Potentials
of Land
POP_DENSITY (1,000)
0.62
1.40
1.26 5.82 0.52 0.95
1.44
6.97 0.48 0.54
1.21
5.23
0.67 1.31
0.92
2.64
CHG_POPDEN
41.01 45.90 32.05 34.03 42.64 46.92 33.30 35.17 44.20 47.79 30.03 29.98 41.51 48.45 27.11 34.96
DISTANCE
36.29 10.43 37.07 11.07 37.87 11.45 37.53 11.08 35.32 9.42 36.99 10.62 35.05 9.15 34.33 11.36
RESIDUAL_VALUE
66.27 66.30 59.34 64.74 59.50 49.85 55.81 59.27 73.83 88.95 59.75 68.87 69.53 76.51 73.76 79.24
HOUSINGVAL ($1,000)
200.00 86.87 190.00 87.16 192.63 71.61 196.03 87.13 217.24 110.66 191.48 83.01 185.60 85.28 153.26 74.35
No. Observations
372
3,657
138
2,052
58
580
127
410

35
Table 3. Estimated coefficients from probit models
Full
Residential/ Retire.
Coeff.
Std. Err.
Coeff.
Std. Err.

Low Sales
Coeff.
Std. Err.

Commercial
Coeff.
Std. Err.

Farm and Landowner Characteristics
GENDER

0.2318**0.10100.06000.12840.3895*0.2403-0.6118**

0.2681AGE-4.0E-05

0.00330.00200.00510.00690.0082-0.00100.0078EXPERIENCE

0.00040.0028-0.0243*** 0.0090-0.0032

0.0073-0.00070.0067--0.0003**0.0002

-

-

-

-

HEIR

0.07920.1008-0.16020.1868-0.1560

0.34330.28240.1840

OFF_FARM

0.08220.06010.00100.08990.24660.16850.14490.1353RENTAL_INC

0.6845***0.2332

1.0087***0.26230.9556

0.6236

-

-

ACRES

0.0035***0.00070.0080***0.00110.0077***0.00130.0017***0.0003

ACRES ^2

-6.9E-07*** 1.3E-07 -1.0E-05*** 2.4E-06 -7.1E-06*** 1.8E-06 -3.4E-07*** 9.0E-08

EXPERIENCE^2

GENDER*ACRES-0.00120.0007

-

-

-

-

-

-

0.1926

Agricultural Returns
DIRECT_SALES

0.06750.0791-0.0060

0.11470.22440.25400.2102

2.1E-06** 1.0E-06

1.3E-06

2.0E-06 1.6E-05*** 5.7E-06

2.4E-06

2.1E-06

CROP_PRICES^2

-

-

-

-

-2.8E-11** 1.5E-11--

CATTLE_PRICES-4.3E-06

2.4E-05-4.8E-05

0.00010.0001*** 4.8E-05-1.8E-05

2.6E-05

POULTRY_PRICES-0.00170.0016-0.00100.0018-0.0155

0.01110.01120.0131

CROP_PRICES-0.0001

0.0001-0.00020.0001-0.0001** 2.4E-05

PRIME

0.00420.00270.0087**0.0037-0.0038

0.00750.0138**0.0060TEMPERATURE

0.0947**0.04000.05240.05720.3644***0.12480.07390.0994PRECIPITATION-0.0082

0.0119-0.0051

0.0166-0.0127

0.0390-0.0226

0.0350

NO_FARMS

0.0077***0.00230.0092**0.00360.00870.00610.0026**0.0013

NO_FARMS^2-2.9E-05*** 1.1E-05-3.6E-05** 1.7E-05-0.0001*

2.7E-05---0.00250.0054-0.0059

0.0059-0.0001

0.0001-1.4E-05

2.7E-05

LABOR_COSTS

CHG_AGLAND-2.8E-05*** 1.1E-05

0.00060.00050.00050.0005Development Potential of Land
POP_DENSITY-1.9E-05* 1.1E-05

CHG_POPDEN

0.0019**0.00090.00130.00120.0097***0.0027-0.0006

0.0021DISTANCE-0.0297

0.0192-0.0507*

0.0266-0.01890.0122-0.0071

0.0122DISTANCE^20.0004*0.00020.0006**0.0003----RESIDUAL_VALUE

0.0029***0.00100.0026*0.00150.00130.0010-0.00020.0008RESIDUAL_VALUE^2-4.6E-06** 1.9E-06

-

-

-

-

HOUSINGVAL

4.5E-07

4.8E-07

HOUSINGVAL^2--

Constant
Pseudo R2
Hit Rate% Correct Predict
Area under ROC curve-8.2448*** 2.7883
0.15800.564791.04
0.7744-4.5E-05** 2.1E-05-7.2E-06* 4.1E-06

3.7E-06 -1.4E-05** 6.0E-06

1.4E-05*** 5.4E-06-1.2E-11* 6.9E-12 2.3E-11** 9.1E-12

5.2E-06-2.1E-11** 8.7E-12-5.3159
4.0615 -25.4600*** 8.1412
0.13500.26450.73740.750093.74
92.48
0.77050.8518-7.6038
6.97130.24010.718382.12
0.8179Note: ***, **, * are significant at the 1, 5, and 10 percent level, respectively. Robust standard errors arein parentheses. Models also include fixed effect dummy variables of six regions in New Jersey including

36
Delaware River, Gateway, Great Atlantic, Shore, Skylands and South Shore. Moreover, for the fullsample model, dummy variables of farm types discussed in the methodology are also included. “AreaROC” captures the area under the ROC curve. A model with no predictive power has area 0.5, while aperfect model has area 1 (see Green and Swets 1966).

Table 4. Effect of PDR program participation on farm’s profit per acre (ATTs)
Matching Algorithms
NN1
NN10
KernelLocal
RadiusRadiusLinear0.02
0.05
Full Sample
220
-35
87
79
67
107
Common Support
(268)
(285)
(262)
(273)
(267)
(243)
213
47
94
82
64
107
Thick Support
(278)
(289)
(267)
(503)
(286)
(282)
Residential Lifestyle/Retirement Subsample
-228
-133
-200*
-196*
-198*
-202*
Common Support
(184)
(87)
(113)
(118)
(117)
(113)
-213
-77
-132
-190
-135
-131
Thick Support
(187)
(89)
(114)
(146)
(122)
(122)
Small Farms with Low Sales Subsample
425*
414**
313**
298*
322*
266**
Common Support
(232)
(174)
(132)
(165)
(179)
(129)
453*
436**
334**
318**
322*
279**
Thick Support
(246)
(181)
(144)
(154)
(197)
(132)
Commercial Farm Subsample
986
-53
-160
-43
57
-8
Common Support(1291)
(881)
(912)
(902)
(1029)
(953)
946
-105
-131
-7
-42
19
Thick Support(1305)
(888)
(876)
(936)
(1101)(946)
Note: ***, **, * are significant at the 1, 5, and 10 percent level, respectively. Standard errors arereported in parentheses. The standard errors for all matching algorithms are estimated usingbootstrapping with 1,000 replications, except for the nearest neighbor (NN1) and oversampling(NN10) in which we use the analytical standard error suggested by Abadie and Imbens (2006).

37
Fig. 1. Geographic context map of New Jersey's agricultural lands.

38
Fig. 2. Regional distribution of preserved farmland in New Jersey.

39
Fig. 3. Distributions of estimated propensity scores

Full Sample

Residential/Lifestyle & Retirement
Unpreserved Farms

15

Preserved Farms

Preserved Farms

0

0

5

5

10

10

Unpreserved Farms

0

.2

.4

.6

.8

1 0

.2

.4

.6

.8

1

0

.2

.4

.6

Propensity Score

Small Family Farms with Low Sales

1 0

.2

.4

.6

.8

1

Commercial

Preserved Farms

Unpreserved Farms

Preserved Farms

0

0

1

5

2

3

10

4

15

Unpreserved Farms.8

Propensity Score

0

.2

.4

.6

.8

1 0

.2

Propensity Score

.4

.6

.8

1

0

.2

.4

.6

.8

1 0

.2

Propensity Score

.4

.6

.8

1

40
Table S1. Matching quality indicators with imposition of common support

NN1
NN10
KernelLocal Linear
Radius 0.02
Radius 0.05
NN1
NN10
KernelLocal Linear
Radius 0.02
Radius 0.05
NN1
NN10
KernelLocal Linear
Radius 0.02
Radius 0.05
NN1
NN10
KernelLocal Linear
Radius 0.02
Radius 0.05

Before Matching
After Matching
Mean Pseudo
Chi2%Mean Bias
%Chi2
% Pseudo R2
Bias
R2
Reduction Reduction
Reduction
Full Sample
15.26
0.16
392.07-66.06-72.15-88.6615.26
0.16
392.07-86.19-95.57-98.1415.26
0.16
392.07-85.61-95.57-98.1515.26
0.16
392.07-84.41-94.30-97.7815.26
0.16
392.07-88.20-96.20-98.5415.26
0.16
392.07-83.02-94.94-97.96
Residential/Lifestyle and Retirement Subsample
11.50
0.14
139.01-37.22-31.85-75.2011.50
0.14
139.01-79.77-94.81-98.0611.50
0.14
139.01-83.90-97.04-98.9111.50
0.14
139.01-87.13-97.04-98.9611.50
0.14
139.01-83.56-97.04-98.9011.50
0.14
139.01-76.88-92.59-97.19Small Farm with Low Sales Subsample
18.47
0.26
102.81-39.22-41.67-77.5918.47
0.26
102.81-66.43-87.88-95.1318.47
0.26
102.81-62.46-87.88-95.1618.47
0.26
102.81-53.72-86.74-94.7618.47
0.26
102.81-54.74-77.27-91.4118.47
0.26
102.81-57.26-82.58-93.15
Commercial Farm Subsample
19.73
0.24
141.04-63.98-67.92-82.4019.73
0.24
141.04-78.94-90.83-95.0819.73
0.24
141.04-80.82-92.08-95.5519.73
0.24
141.04-81.01-90.42-94.7519.73
0.24
141.04-75.02-86.67-93.1719.73
0.24
141.04-78.91-87.50-93.17

Note: Kernel function for kernel and local linear matching is Gaussian. Optimal bandwidth associated with thekernel function in each sample is obtained using the rule of thumb suggested by Silverman (1986). Results withthick support are very similar. The mean standardized bias (SB) before matching is given by
X1 − X 0
and the SB after matching is given bySBbefore = 100 
0.5  (V1 ( X ) + V0 ( X ))SBafter = 100 

X 1M − X 0 M

where X1 (V1) is the mean (variance) in the treatment group before
0.5  (V1M ( X ) + V0 M ( X ))matching and X0 (V0) the analogue for the control group. X1M (V1M) and X0M (V0M) are the corresponding values forthe matched samples.

41
Table S2. Balancing test for mean difference – before and after matching
Variable
GENDER*ACRESGENDER
AGE
EXPERIENCE
EXPERIENCE^2HEIR
OFF_FARM
RENTAL_INC
ACRES
ACRES^2
DIRECT_SALESCROP_PRICESCROP_PRICES^2
CATTLE_PRICESPOULTRY_PRICESLABOR_COSTSPRIMETEMPERATUREPRECIPITATIONNO_FARMS
NO_FARMS^2
CHG_AGLAND

SampleUnmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
MatchedFull
233.7550***-2.7700
0.0872***0.00470.7000-0.1860
3.8000***-0.11200.0385***0.00850.0410-0.00930.0130**-0.0020238.2120***-4.0500
2.8E+05***-2.0E+04-0.0173-0.0120-377.0000-737.9000
123.6000*
4.3000-1.0090-0.0750-291.2200-17.3400
5.0250***0.75300.2550***0.0410-0.2210-0.1300
10.0760***
1.30901313.4000**-4.0000
1.94000.1360Resident&
Retire0.03770.0051
1.1130-0.18100.85800.042086.1500-5.7700
0.00190.00800.00200.00680.0415***-0.0004
52.8920***
1.55201.5E+04***
1.2E+03-0.0201
0.0113-440.5000
452.8000-174.1000**-28.1000-0.8810-0.1350-97.0980-10.5350
4.8630***0.65400.23500.0510-0.37900.006010.9930**
1.28901447.1000
112.7000
1.8960-0.0520

Low Sales
0.1759***0.00901.4540-2.03103.7240*-1.1930-0.0069-0.0117-0.0035
0.03650.00860.0057107.5320***-17.5900
4.4E+04***-7.6E+03-0.04310.02506958.9000*
4103.5000
2.1E+09
1.2E+09300.2000*
179.4000-3.7710-0.9510-225.8700
6.52004.3470*-0.92800.13600.00400.6920-0.0600
6.76605.2770808.5000
1133.6000
1.6910-0.0840

Commercial-0.0221-0.0098
1.2120-0.8760
1.6370-1.2500
0.0432-0.0089
0.1005**0.0176404.2600***
27.6900
6.4E+05***
5.0E+04-0.0100-0.0267-20514.9000-3972.7000
13.6000
75.9000
0.58200.1370-3292.3900*-439.2000
6.5870***0.2380-0.19200.05501.5010**-0.23908.9810-0.4370-0.73900.197042
Table S2. Continued
Resident&
SampleFull
Low Sales
Commercial
RetireUnmatched-640.7300**-911.7300-731.6200-243.3100
Matched-70.5700-128.8100-111.6900-75.2900
CHG_POPDEN
Unmatched
8.9610***9.3450***14.1630***
14.3950***
Matched-0.6320
0.12404.28500.2580DISTANCE
Unmatched-0.7800
0.3410-1.67200.7160Matched
0.0160-0.0300-0.8700
0.2620DISTANCE^2
Unmatched-71.2000
33.2000
Matched-8.2000-6.8000RESIDUAL_VALUE
Unmatched
6.9270**3.684014.0790-4.2280
Matched1.55800.54407.71307.5510RESIDUAL_VALUE^2 Unmatched
1063.3000-619.4000
Matched629.8000-7.8000
HOUSINGVAL
Unmatched
1.0E+04-1.0E+04
3.0E+04**
4.0E+04***
Matched
0.0E+00
0.0E+00
1.0E+04
1.0E+04
HOUSINGVAL^2
Unmatched-4.0E+09
1.5E+10**
1.3E+10***
Matched
1.0E+09
9.0E+09
2.0E+09
RETIREMENT
Unmatched-0.0367*
Matched0.0006RESIDENT/RETIRE
Unmatched-0.1535***-0.0967**
Matched
0.00350.0018LOWSALES
Unmatched-0.0027
Matched0.0047
HIGHSALES
Unmatched
0.0532***
Matched0.0008LARGE
Unmatched
0.0446***-0.0527
Matched-0.0004-0.0080
VERYLARGE
Unmatched
0.1315***0.1099**Matched-0.00150.0242NONFAMILY
Unmatched
0.0040Matched-0.0096GATEWAY
Unmatched
0.00860.01130.0207-0.0276
Matched-0.0166-0.00450.0216-0.0037
GREATALTANTIC
Unmatched
0.0046-0.0162-0.0147
Matched0.0084-0.00180.0054
SHORE
Unmatched
0.01040.0073-0.02930.0631**
Matched-0.00280.00040.00430.0108
SKYLANDS
Unmatched-0.0285-0.0304
0.07760.1047**
Matched
0.01440.0054-0.00140.0110SOUTHSHORE
Unmatched-0.02190.0013-0.0792**
Matched-0.0048-0.0012-0.0081Note: Matching algorithm used for the balancing test in each sample is the one that provides the best
matching quality (See Table S1). ***, **, * are significant at the 1, 5, and 10 percent level, respectively.
RETIREMENT, RESIDENT/RETIRE, LOWSALES, HIGHSALES, LARGE, VERYLARGE, andNONFAMILY are dummy variables capturing farm types according to the ERS typology. GATEWAY,
GREATALTANTIC, SHORE, SKYLANDS, and SOUTHSHORE are fixed effect dummy variables
capturing regions in New Jersey.
Variable
POP_DENSITY

43
Detailed explanation of sensitivity analysis for Tables S3 and S4
Each row of the first four columns of Table S3 contains parameters pij characterizing the
distribution of the unobserved binary variable U. Controlling for the observable covariates X, theestimated Γ provides an indication of the “outcome effect” (Out. Eff.) of U (the effect of U on
the profit per acre of unpreserved farms) and the estimated Λ measures the “selection effect”(Sel. Eff.) of U (the effect of U on the decision to participate in the PDR programs). The firstrow shows the baseline ATT estimate obtained with no confounder. The second row of eachsubgroup reveals the ATT estimate obtained with a neutral confounder, where Γ and Λ are equal
to one. The other rows of each subgroup of Table S3 report variations in the baseline estimate
when the binary confounding factor U is calibrated to mimic different observable covariates.
Overall, the baseline estimate changes only slightly, affirming that our simulated results arerobust to unobserved factors.
Since the above findings may be driven by the behavior of the covariates, we search for thecharacteristics of Us (i.e., “killer” confounding factors) that would have to exist to render the
point estimate of the ATT close to zero. We simulate the distribution of U associated with thevalues of d (the difference in the binary confounding factor U among unpreserved farms that did
and did not have profit per acre above the mean value) and s (the difference in the binary
confounding factor U between preserved and unpreserved farms) designed to drive down theestimate of ATT to zero. We find the baseline ATT to be robust. For the highest values of theselection effect (s=0.5) and the outcome effect (d=0.5), the point estimate obtained when U is
included in the matching set is still positive (Table S4).1
1 As discussed in Nannicini (2007), the sensitivity conclusions should be drawn more in terms ofthe comparison of the point estimates than in terms of the significance of the simulated ATT.

44

Table S3. Sensitivity analysis of profitability outcome for small farm with low sales subsample:
Effect of “calibrated” confounders

No confounder
Neutral confounder
Confounder-like
GENDERHEIROFF FARM
RENTAL_INC
DIRECT_SALES

Fraction U=1 by
Treatment/Outcome
Out. Eff.
p11
p10
p01
p00
(Γ)
0.00 0.00 0.00 0.00
0.50 0.50 0.50 0.50
1.00
0.87
0.07
0.44
0.02
0.15

0.67
0.00
1.00
0.00
0.00

0.77
0.07
0.45
0.01
0.23

0.48
0.10
0.51
0.01
0.07

4.10
0.71
0.79
272.51
4.95

Sel. Eff.
(Λ)
1.00

ATT

SE

313
328

132
212

3.21
1.07
1.08
7.35
0.66

277
320
330
318
339

212
215
212
216
215

Note: U is a binary confounding factor. pij is the probability that U=1 defined by treatment and outcome status
where i  {0,1} indicates treatment status with i=1 as enrollment in the PDR program and j  {0,1} indicatesoutcome status with j=1 if the value of the profitability outcome (profit per acre) is greater than its mean value. On
the basis of these parameters, a value of U is imputed and the ATT is estimated by kernel matching with U in the setof matching variables. A gaussian kernel function is used; its associated bandwidth is equal to 0.039. The process isrepeated 500 times. “SE” is the standard error. Outcome effect (Γ) is the average estimated odds ratio of U in thelogit model of Pr(Y=1|T=0, U, X) and selection effect (Λ) is the average estimated odds ratio of U in the logit modelof Pr(T=1|U, X). “ATT” is the average of the simulated ATTs. In the ‘confounder-like’ rows, U has been calibrated
to match the distribution of the corresponding covariate.

45
Table S4. Sensitivity analysis of profitability outcome for small farm with low sales subsample:
Characterizing “killer” confounders
s=0.1
s=0.2
s=0.3
s=0.4
s=0.5
Λ
Λ
Λ
Λ
Λ
[1.34, 1.61] [2.22, 2.75] [3.92, 4.72] [8.81, 11.46] [42.37, 47.62]
d=0.1
325
315
302
281
237
Γ  [1.57, 1.62]
(212)
(217)
(223)
(225)
(243)
d=0.2
313
301
268
242
181
Γ  [2.41 2.50]
(218)
(216)
(209)
(222)
(246)
d=0.3
307
280
245
193
128
Γ  [4.03, 4.10]
(215)
(212)
(208)
(208)
(231)
d=0.4
304
263
213
161
78
Γ  [6.89, 7.79]
(213)
(205)
(202)
(196)
(212)
d=0.5
304
249
189
118
22
Γ  [13.95, 17.81]
(216)
(203)
(197)
(187)
(201)
Note: We assume that Pr(U=1)=0.50 and that p11-p10=0. s =p1.-p0.captures the difference in the binary
confounding factor U between farms that enrolled and did not enroll in the PDR program. d = p01 –p00
captures the difference in the binary confounding factor U among unpreserved farms that did and did nothave profit per acre higher than the mean. Standard errors are in parentheses.2
All ATTs are averaged over 500 iterations. Γ is the average estimated odds ratio of U in the logit modelof Pr(Y=1|T=0, U, X) and Λ is the average estimated odds ratio of U in the logit model of Pr(T=1|U, X).
The baseline estimate without the confounder is equal to $313.

2 With different predetermined values of Pr(U=1) and p =p , we can still obtain similar results
11 10

qualitatively.

46
References
Nannicini, T. 2007. Simulation-based sensitivity analysis for matching estimators. Stata Journal
7 (3), 334.

1

A PDR program imposes a negative easement on an enrolled property that "runs with the land"and prohibits non-agricultural development in perpetuity, or a specified period of time. Unlike
fee simple acquisition, whereby full interest of land is conveyed to a purchaser, PDR programs
establish a non-possessory interest in land.
2This test involves comparison of the characteristics of preserved and unpreserved farms before

matching and evaluation of whether any significant differences in the characteristics of the two
farm groups remain after matching.
3

Capital asset pricing theory predicts that the sale price of a preserved farm will be a function of

only the present value of the net returns in an agricultural use, if the restrictions imposed on the
farmland by the sale of development rights are fully capitalized into the farmland values (Lynch,Gray, and Geoghegan 2007).
4

These data contain some duplicative entries. For example, a few dozen farms have acreage in

two or more municipalities. In such instances, acreage preserved in each municipality isrecorded, resulting in multiple closing entries for each farm.
5

For example, assume a 300-acre farm that is enumerated in the Census comprises three 100-acre tax lots. Assume also that the farm owner chooses to preserve one tax lot (100 acres) ineach of three separate years. These will be reflected as three different preserved farms in the

47

SADC dataset, since the closings occurred separately. In some instances, one SADC closing
record matched perfectly with a respective Census record. In others, there were multiple SADC
closings that were combined to match a Census record.
6

To qualify for state funding for farmland preservation, a farm must be eligible for the state's

differential agricultural assessment program, be located in an agricultural development area (anarea deemed by a county agriculture development board as one in which farming is viable in thelong-term), targeted for preservation in a county comprehensive farmland preservation plan, andexhibit development potential based on zoning and environmental conditions. Additional
eligibility criteria are as follows. A farm 10 or less acres in size must earn $2,500 in annual sales
and 75% of its area (or a minimum of 5 acres) must be tillable and comprise soils capable ofsupporting agricultural or horticultural production. For a farm greater than 10 acres in size atleast 75% of its area (or a minimum of 25 acres, whichever is less) must be tillable and comprisesoils capable of supporting agricultural or horticultural production. Due to data limitations, a
simplified set of eligibility screening criteria were used in our study. For farms 10 acres or lessin size, a farm was deemed eligible if it sold at least $2,500 worth of farm products and was atleast 75% tillable (or 5 acres were tillable, whichever is less). Farms that were at least 10 acres insize were considered eligible if at least 50% of the land (or 25 acres, whichever is less) was
tillable.
7

The basic idea of the radius matching is to use not only the nearest neighbor within each caliper(propensity range), but all of the unpreserved farms within the caliper. A benefit of thisapproach is that it uses only as many unpreserved farms as are available within thecaliper and therefore allows for usage of extra (fewer) units when good matches are(not) available (Caliendo and Kopeinig 2008).

48

8

Kernel matching and local linear matching are nonparametric matching estimators that use

weighted averages of (nearly) all farms in the unpreserved farm group to construct thecounterfactual outcome. Thus, one major advantage of these approaches is the lower variance.
9

We also estimate kernel and local linear matching algorithms using the Epanechnikov kernel

function and find slightly different results.
10

We trim any observations with a propensity score below 0.034 in the full sample, 0.027 in theresidential/lifestyle and retirement farm subsample, 0.015 in the small family farm with low
sales, and 0.077 in the commercial farm subsample.
11

The method simulates the binary variable U (“the confounder”) from the data, which is used as

an additional matching variable to estimate the propensity score and the ATT. A comparison of
the estimates obtained with and without matching on this simulated variable demonstrates theextent to which the estimator is robust to the unobserved factors.
12

Roughly 84,016 acres of preserved land is associated with the "commercial" farms (averagefarm size in the subgroup is 661.54 acres * 127 farms). This equates to 78% of the acreage
summed across the three farm subgroups. The respective sample acreages for residential
lifestyle/retirement farms and low sales farms are 13,364 acres and 10,253 acres, respectively.
13Vi resid ( X i , t 2* ) = Vi presold ( X i , t 2* ) −  Riag ( X i ,  )e −  d where Vit 2*

preserved farm sold at optimal date t 2* .

presold

is the actual price of49