Impact of Designated Preservation Areas on Rate of Preservation

and Rate of Conversion: Preliminary Evidence

Lori Lynch and Xiangping Liu

Department of Agricultural and Resource Economics

2200 Symons Hall

University of Maryland College Park, MD 20742

USA 301-405-1264 (tel.)

llynch@arec.umd.edu

American Journal of Agricultural Economics, December 2007 Keywords: agricultural preservation programs, farmland, propensity score, farmland conversion, urban-rural interface Authors are Graduate Student and Associate Professor in the Department of Agricultural and Resource Economics, University of Maryland. Support was provided by the Maryland Center for Agro-Ecology, Inc. and the UMD Center for Smart Growth. We thank Karen Palm, Jay Harvard, Sabrina Lovell and Seth Wechsler for their research assistance. We also thank Virginia McConnell, Elizabeth Kopits and Margaret Walls for assistance with Calvert’s TDR data. Any errors remain the authors’ responsibility.

Impact of Designated Preservation Areas

Concerns about farmland loss and suburban sprawl motivated 124 government entities to

institute preservation programs at an overall cost of $3.723 billion (American Farmland

Trust 2005a, 2005b). In addition, 38 states have implemented land conservation

programs for ecological reasons. Critics suggest these programs do not prevent land

conversion,1 do not maximize social benefits, and do not prevent land fragmentation

(Pfeffer and Lapping 1996, Daniels and Lapping 2001, Lynch and Musser 2001).

Concentrated preservation may provide greater benefits if threshold impacts or

economies of scale exist. For example, a critical mass of contiguous farms may be

needed due to economies of scale in support industries (Lynch and Carpenter 2003) and

to avoid conflicts with non-farm neighbors. Large blocks of undeveloped land may be

needed for ecosystem services provision such as wildlife habitat and water quality.

Although Maryland has a plethora of land preservation programs, contiguity of

preserved parcels has not been achieved. To prioritize contiguity, Maryland introduced

the Rural Legacy (RL) program in 1997. The cornerstone of the RL program is to

designate RL Areas, which receive special funding to preserve farm, forest, and

ecologically important resource lands in a contiguous fashion. The special funding

should result in more preservation in these areas but also might interact with the existing

preservation programs. Thus, we examine the following preservation outcomes. 1) The

rate of preservation in the RL area increases due to the extra funding itself and to its

catalyst effect on the other preservation programs. If economies of scale or threshold

2

impacts exist, other programs may receive higher benefits from each additional acre

preserved in the RL area relative to a non-RL parcel. 2) The RL program’s effort crowds

out other programs’ preservation. As competition between programs increases the cost of

preserving in the RL area, other programs may select parcels outside the RL area.2 We

test to see if the rate of preservation measured as both number of parcels and number of

acres has increased within the RL areas since 1997.

We also examine if the RL area designation affects the rate of conversion.

Geoghegan, Lynch, and Bucholtz (2003) and Roe, Irwin, and Morrow-Jones (2004) find

that preservation efforts can generate positive amenities for adjacent homeowners in the

form of guaranteed open-space. Increased demand for housing near preserved parcels

can create higher returns for housing construction and thus accelerate the rate of

conversion. We test to see if the rate of conversion, measured as the construction of new

houses or new subdivisions, is lower within the RL areas since 1997.

Background on Rural Legacy Program

Maryland has been a leader in agricultural preservation in terms of acres preserved and

innovative programs but has had little success in preserving large contiguous blocks of

land. The Rural Legacy Program requires contiguous tracts of resource land be identified

for preservation and provides extra funding for these areas. In addition, the program

permits point-based payments to landowners that reward ecological and other attributes

such as large parcels, wetlands, endangered species and other wildlife habitat, and

swamps, which are often discounted in an appraisal approach. State-wide, the program

has encumbered $156 million through 2006 and preserved almost 52,000 acres. RL areas

3

are approved based on the agricultural, forestry, and ecological values (including

economic values) and the level of conversion pressure.

Our 3-county study area in Southern Maryland has five approved RL areas for a

total of 84,102 acres. Three of the five are used in this analysis; two did not have data

available because they were only recently approved. The Calvert Creeks area (20,527

acres) seeks to protect water quality and wetland habitat. The RL has protected 1,660

acres at a cost of $6.8 million with an average price of $4,003 per acre. The Huntersville

area (8,357 acres) in St. Mary’s County includes shoreline with endangered species

habitat, wetlands, historic structures, agricultural and forest land, and archeological sites.

This RL program has preserved 2,720 acres at a cost of $8.8 million; average cost was

$2,693 per acre. The Zekiah Watershed area (31,000 acres) in Charles protects farms,

forests, wetlands, and historic and archeological sites. This RL program has preserved

2,007 acres at a cost of $6.5 million at an average cost of $3,082 per acre.

The state program, Maryland Agricultural Land Preservation Foundation

(MALPF), has preserved agricultural land in all 3 counties. The program has protected

4,263 acre in Calvert, 5,872 in St. Mary’s, and 3,474 in Charles. Payments in 2002 were

$7,714 per acre in Calvert, $2,480 in St. Mary’s, and $3,024 in Charles. Transfer of

development rights (TDR) programs are also available. Calvert has protected almost

13,000 acres through its TDR program; St. Mary’s has 221 acres, and Charles has 1,554

acres. Private conservation groups have also preserved land.

Almost 30 percent of the RL areas have been preserved compared to only 5.6% of

the non-RL areas. Clearly, there exists a predisposition to preserve in the RL area

4

without considering parcel characteristics. Figure 1 delineates the RL areas and the level

of preservation under the different programs geographically.

Since 1997, significant preservation activity has occurred from all the

preservation programs. Rural Legacy has preserved almost 6,500 acres in these 3

counties since 1997. The MALPF program has preserved almost half of its acreage since

1997. The TDR programs in Calvert and Charles have preserved 40% of their acres since

1997. The raw data suggest a crowding out phenomena: the TDR programs had

preserved 42% of its acreage within the RL area pre-1998; but after 1997, the TDR

program preserved only 30%. Similarly, MALPF preserved 28% of its acreage in RL

areas before designation but only 4.5% after 1997.

Methodology

If parcels in RL areas have characteristics that make them less likely to be developed

and/or more likely to be preserved, then assigning the impact to the designation of a

preservation area would be incorrect. One cannot construct the proper counterfactual, i.e.

one does not know if a particular parcel would have been preserved and/or developed if

not included because it can not be in the two states simultaneously, i.e. cannot be both

within and outside of a designated preservation areas at the same time. To overcome this,

a propensity score matching (PSM) developed by Rosenbaum and Rubin (1983) is used

to estimate the treatment effect, i.e. does being targeted for preservation and within the

designated area change a parcel’s likelihood of being preserved?. We assume a

conditional mean independence (CMI) criteria

5

]|[],0|[],1|[ 000 XYEXDYEXDYE ==== )1,0()|1( ∈= XDP to estimate the

average treatment effect on the treated (ATT) (Heckman et al. 1997).

A propensity score is computed for each parcel as to its likelihood of being within

a RL area and then is used to explicitly match treatment parcels (those within the RL

areas) with control properties (those outside the RL areas). Benefits of PSM include that

the matching protocol ensures comparison of RL parcels with the most similar non-RL

parcels in terms of characteristics. Parcels with different characteristics and outliers will

have no/little influence. In addition, PSM does not impose a specific functional form.

Data Sources and Included Variables

Data was collected on agricultural and forest parcels greater than 3 acres in 3 Maryland

counties. We collected information on variables that affect RL area designation and

preservation outcomes. The primary data set, the MDPropertyView 2002 Database,

provided parcel level information such as parcel size, zoning density, waterfront access,

public sewer availability, housing construction date, subdivision designation, and

geographic coordinates. Geographic Information Systems (GIS) were used to extract

1997 land use data from satellite and aerial photography, and the percent of cropland,

pasture, forest, and wetlands were computed for each parcel. Soil data were extracted

and grouped by slow versus medium or rapid permeability, flat versus medium or greater

slope, and susceptibility to flooding (Maryland Department of State Planning, 1973).

Distance to Washington, D.C. in miles was computed using U.S. Census Bureau road

networks. For the ecological values of each parcel, we computed the percent of

Maryland’s Sensitive Species Project Review Areas, which include rare, threatened, and

6

endangered species and rare natural community types (Maryland DNR 2003), and Non-

tidal Wetlands of Special State Concern (Department of the Environment and DNR 1998).

The percent of the parcel in estuarine wetland status was also extracted from the

Maryland Wetland Map (Maryland DNR 1998).3

Preservation data was collected from the state-wide preservation program

MALPF parcels through 2005, from the Maryland Environmental Trust through 2004,

from the Calvert Transfer of Development Rights program through 2004, and from

private conservation groups (Nature conservancy, private land trusts) through 2005.

Maryland DNR provided information on RL area designation and approved landowners

who were matched to specific parcels. The number of preserved acres within a 1 mile

radius was also extracted for each parcel.

Propensity Score Estimation, Matching Methods, and Balancing Test

Propensity scores are estimated with a Logit model using Stata software. Table 1 reports

the estimated coefficients for predicting a parcel’s inclusion in a RL Area. Having a

large number of preserved acres nearby, having waterfront property, having a high

percent of estuarine area, having a higher percent of a floodplain, and land-uses of

cropland and pasture all increased a parcel’s likelihood of being within a RL area.

Parcels with zoning densities permitting more houses per acre were less likely to be

included within the area.

Two matching methods were used: kernel and local linear techniques. The ATT

varied very little by matching technique. When comparing preservation outcomes, we

restrict the sample to parcels greater than 10 acres since these are the most likely to be

7

preserved. When evaluating conversion outcomes, we match treatment parcels to control

parcels within the full sample of all parcels 3 or more acres.

Balance tests are conducted using the standardized difference test, a t-test for

equality of the variable means in the matched treated and control groups. For the

preservation outcomes, we find that the treatment and control groups (10+ acres) have

equal means except for the permeability and waterfront variables. Surprisingly, the

matched RL parcels have a higher mean permeability (64% compared to 58%) and a

higher likelihood of having waterfront property (7% compared to 5%) than non-RL

parcels. Theoretically, parcels with higher permeability levels and waterfront would be

more likely to be developed rather than preserved. The quality of the waterfront may

vary between parcels, for example one may have an inaccessible area, another have a

picturesque riverbank. We also find permeability does not balance for the full sample

(3+ acres) with RL parcels having slightly lower mean values (56% compared to 59%).

Results

Our matching estimates of the impact of the RL Areas appear in Table 2. These include

the bias-corrected average treatment effect (BC ATT) and the bootstrap standard errors

computed from 500 independent draws. We report BC ATT for both time periods: Post-

1997 (post-RL beginning) and Pre-1998 (pre-RL).

Two different outcome variables are used to determine whether the RL

designation affects the rate of preservation: number of acres (breadth of outcome) and

number of parcels (decision-making unit). The number of acres preserved in the RL area

is greater than outside the designated area following RL designation. In essence, the

8

results show that each parcel preserved has 8.7 to 8.9 more acres on average if within the

RL.4 Before 1997, acres within and without were equally likely to be preserved.

Therefore, the RL program has had a positive impact on the number of acres retained. In

addition, parcels within the RL area were more likely to be preserved than those outside

RL areas. In this case, we find that this was true even before RL designation. However,

the rate of preservation increases 50% from 4.7% to 7.1%. This indicates that the

designation is having an impact on preservation outcomes on two levels: a higher percent

of parcels and more acres/larger parcels.

Similarly, we look at two outcome variables to evaluate the impact on conversion:

the number of acres and parcels that have a new house or a new subdivision within the

RL area compared to those outside the RL area. We find that RL designation has no

impact on the number of acres or the number of parcels being converted. Conversion is

occurring on approximately 20% of the parcels both within and without the RL areas.

Thus, the RL designation and special funding does not appear to be having an impact on

housing construction or subdivision creation as measured. Due to time lags in

construction, some of these homes may have been planned before the RL program began.

Therefore, a re-evaluation at a later date may prove otherwise.

Conclusions

Many counties designate farmland retention areas, but these areas are often more than 50

percent of a county’s land and too large for the financial resources available. Programs

may also have equity concerns, specifically in regards to spreading the budgeted

resources over the entire state or geographic area. Both these may result in inefficient

9

outcomes especially if economies of scale and/or threshold levels exist. For example,

Wu and Skelton-Groth (2002) show that parcel selection based on political equity

concerns may lead to the lowest possible benefits in the presence of threshold impacts.

In an attempt to encourage more contiguous preservation, Maryland introduced

the Rural Legacy program to preserve large contiguous blocks of land with high social

value. Local entities designate preservation areas and become eligible for special funding.

Overall, the designation appears to have a positive impact on acres retained and on

probability of preservation for the identified area. The increased preservation appears to

be primarily due to the new RL funding rather than from attracting more preservation

from other programs. While the raw data suggests crowding out, no evidence of it was

found. The program has enrolled more acres and larger parcels due to the extra funding

and the new payment schemes based less on market appraisals. In Charles County, the

RL designation resulted in preservation of a completely new area.

RL designation, however, seems to have done little to discourage land conversion

relative to non-RL areas. We do not find any impact on the rate of conversion or on the

number of acres converted in the RL area. On average, 20% of parcels were converted

post-1997. However, more time may be needed for a conclusive test of the effectiveness

of targeted preservation area on preventing conversion.

This analysis examines a limited number of questions and focuses on the initial

years of the program. Further evaluation is needed to determine the impact of and the

overall benefits of targeted preservation areas. County-level analysis may be illuminating

given differences in local preservation programs and local planning but technically this

10

may prove challenging. Future work will examine how the preservation and conversion

patterns have impacted fragmentation and/or contiguity issues. The targeted RL areas are

of different sizes – one has only 8,000 acres; another 30,000 acres. What is sufficient?

What is the trade-off between size of area and contiguity of preservation assuming

limited resources? Does the spatial configuration of the area matter? In addition,

understanding the threshold levels for different objectives, i.e. how much area of forest or

farmland is needed for habitat purposes or at what percent of impervious surface area

(roads, houses, other structures) does water quality impacts begin, would be of interest.

Further exploration of the change in the cost per acre of preservation will also be

conducted to determine if crowding out is an issue. Currently, the RL funds are shared

between all the RL areas within the states based on submitted proposals of willing

landowners within each areas. .Another key question is whether the state would be better

off preserving all of one RL area before sharing resources to another RL area?

11

References American Farmland Trust. 2005a. Fact Sheet Status of Local PACE Programs. American

Farmland Trust Farmland Information Center. Northampton, MA. American Farmland Trust. 2005b. Fact Sheet Status of State PACE Programs. American

Farmland Trust Farmland Information Center. Northampton, MA. Daniels, T. and M. Lapping. 2005. “Land Preservation: An Essential Ingredient in Smart

Growth.” Journal of Planning Literature 19(3):316-329. Geoghegan, J., L. Lynch, and S. Bucholtz. 2003. “Capitalization of Open Spaces.”

Agricultural and Resource Economics Review 32(1):33-45. Heckman, J., H. Ichimura, and P. Todd. 1997. “Matching as an Econometric Evaluation

Estimator. Review of Economic Studies 64: 605-654. Lynch, L. and J.E. Carpenter. 2003. “Is There Evidence of a Critical Mass in the

Mid-Atlantic Agricultural Sector Between 1949 and 1997?” Agricultural and Resource Economics Review 32(1):116-28.

Lynch, L. and W. N. Musser. 2001. “A Relative Efficiency Analysis of Farmland

Preservation Programs.” Land Economics 77(4):577-94. Lynch, L., K. Palm, S.J. Lovell, and J. Harvard. 2007. Expected Cost of Tripling

Maryland’s Preserved Acres. Center for Agroecology. Wye Mills, MD. Pfeffer, M.J., and M.B. Lapping. 1995. “Prospects for a Sustainable Agriculture in the

Northeast's Rural/Urban Fringe.” Research in Rural Sociology and Development 6:67-93.

Roe, B., E.G. Irwin, and H.A. Morrow-Jones. 2004. “The Effects of Farmland, Farmland

Preservation, and Other Neighborhood Amenities on Housing Values and Residential Growth.” Land Economics 80(1):55-75.

Wu, J., and K. Skelton-Groth. 2002. “Targeting Conservation Efforts in the

Presence of Threshold Effects and Ecosystem Linkages.” Ecological Economics 42(1): 313-331.

12

Figure 1. Rural Legacy areas and preserved parcels by program type

13

Table 1. Estimated Coefficient Estimates for the Propensity Score Logistic Regression Pseudo R2 =0.4643 Log likelihood = -3979.79 Dependent Variable In/out of Rural Legacy Area Independent Variables Coef. Estimates Std. Err

Acres 0.0048 0.0043

Waterfront 0.8542*** 0.1544

On Public sewer 0.2139 0.3596

Miles to Washington D.C. -0.0002 0.0252

Zoning density -1.3419*** 0.1743

%cropland_1997 1.1939*** 0.2030

%pasture_1997 3.2922*** 0.8071

%forest_1997 0.2419 0.1619

%wetland_1997 -0.7950 0.5333

Soil flood susceptibility 1.3973*** 0.4598

Soil slope medium+ 0.1998 0.3555

Soil permeability medium+ 0.2050 0.4243

% special habitat areas 0.2051 0.8455

% estuarine 4.5068** 1.8402

Preserved land within 1 mile index

0.9822*** 0.3519

Acre*preserved -0.0008 0.0008

Acre*waterfront -0.0011 0.0015

Acre*Wash. miles -0.00006 0.0001

Acres2 (squared) 0.000002 0.000006

Wash D.C.miles2 -0.0002 0.0003

Preserved within 1mile2 0.1555** 0.0667

% estuarine2 -6.8795 4.1750

Zoningdensity2 0.1247 0.0825

%cropland_19972 0.0707 0.2248

%pasture_19972 -1.6596 1.0676

%forest_19972 0.8342*** 0.1590

Soil flood2 -4.4510*** 0.5627

Soil slope medium+2 -1.1905*** 0.3745

% special habitat areas2 0.0125 0.8485

Constant -5.3717*** 0.6824

Number of obs 26,048

Note: ** indicates statistical significance at the 0.05 level, and *** indicates statistical significance at the 0.01 level.

14

Table 2. Effect of Rural Legacy Designation on the Number of Acres Preserved, Rate of Preservation, and Number of Acres Conversion and Rate of Conversion Kernal Normal Local Linear

Biweight Outcome Variable

Pre or Post RL designation

# Treated obs./ # Control obs.

BC ATT (std. err.)

BC ATT (std.err)

Preserved Acres Post-1997 696/6851 8.73** (2.38)

8.97** (2.39)

Preserved Acres Pre-1998 683/6850 3.60 (2.79)

3.52 (2.74)

Preservation Rate Post-1997 696/6851 0.071** (0.018)

0.072** (0.017)

Preservation Rate Pre-1998 683/6850 0.047** (0.016)

0.045** (0.016)

Converted Acres Post-1997 2153/23895 0.019 (0.28)

0.032 (0.27)

Conversion Rate Post-1997 2153/23895 -0.010 (0.013)

-0.008 (0.012)

Note: ** indicates statistical significance at the 0.05 level.

15

1 Recent evidence finds that preservation programs decrease the 5-year rate of county farmland loss by 3 percentage points; a 43% decrease from the average 5-year rate of 7.31% (Liu and Lynch 2006).

2 Data limitations precluded an analysis of the programs’ payments over this

period.

3 More details on data are available in Lynch, Palm, Lovell and Harvard 2007.

4 Liken this to each worker earning an average of $2.00 more per hour after a training program

16