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The effects of moratoria on residential development: evidence from a matching approach.


by Bento, Antonio^Towe, Charles^Geoghegan, Jacqueline
American Journal of Agricultural Economics • Dec, 2007 • Adequate Public Facility Ordinance

Dehejia, R.H., and S. Wahba. 2002. "Propensity Score-Matching Methods for Nonexperimental Causal Studies." The Review of Economics and Statistics 84:151-61.

Fischel, W. A. 1990. Do Growth Controls Matter: A Review of Empirical Evidence on the Effectiveness and Efficiency of Local Government Land Use Regulation. Cambridge, MA: Lincoln Institute of Land Policy.

Frolich, M. 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators." The Review of Economics and Statistics 86:77-90.

Heckman, J.J., H. Ichimura, J.A. Smith, and P.E. Todd. 1998. "Characterizing Selection Bias Using Experimental Data." Econometrica 66:1017-98.

Heckman, J.J., H. Ichimura, and P. Todd. 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme." The Review of Economic Studies 64:605-54.

Heckman, J.J., and R. Robb. 1986. "Alternative Method for Solving the Problem of Selection Bias in Evaluating the Impact of Treatments on Outcomes." In H. Wainer, ed. Drawing Inferences from Self-Selected Samples. Berlin, Germany: Springer-Verlag, pp. 63-107.

Helsley, R.W., and W.C. Strange. 1995. "Strategic Growth Controls." Regional Science and Urban Economics 25:435-60.

Lechner, M. 2002. "Program Heterogeneity and Propensity Score Matching: An Application to the Evaluation of Active Labor Market Policies." The Review of Economics and Statistics 84:205-20.

List, J., D.L. Millimet, P.G. Fredriksson, and W.W. McHone. 2003. "Effects of Environmental Regulation on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator." The Review of Economics and Statistics 85:944-52.

Lynch, L., W. Gray, and J. Geoghegan. 2007. "Are Farmland Preservation Programs Easement Restrictions Capitalized into Farmland Prices? What Can a Propensity Score Matching Analysis Tell Us?" Review of Agricultural Economics 29 (3):502-9.

McMillen, D.P., and J.F McDonald. 2002. "Land Values in a Newly Zoned City." The Review of Economics and Statistics 84:62-72.

Rosenbaum, P.R., and D.B. Rubin. 1983. "The Central Role of the Propensity Score in Observational Studies for Causal Effects." Biometrika 70:41-55.

Sakashita, N. 1995. "An Economic Theory of Urban Growth Control." Regional Science and Urban Economics 25:427-34.

Smith, J.A., and P.E. Todd. 2005a. "Does Matching Overcome LaLonde's Critique of Nonexperimental Estimators?" Journal of Econometrics 125:305-53.

--. 2005b. "Rejoinder." Journal of Econometrics 125:365-75.

(1) We scale the neighboring school capacity using the distance in kilometers between the centroid of the observation and the actual address of the neighboring school.

Antonio Bento is Associate Professor in the Department of Applied Economics and Management, Cornell University, Charles Towe is Ph.D. Candidate in the Department of Agricultural and Resource Economics, University of Maryland and Jacqueline Geoghegan is Associate Professor in the Department of Economics, Clark University.

This article was presented in a principal paper session at the AAEA annual meeting (Portland, OR, July 2007). The articles in these sessions are not subjected to the journal's standard refereeing process. Table 1. Variable Definitions Variable Short Description Acres Acreage of census block/elementary

school district distBA Distance to Baltimore in km distDC Distance to DC in km pctSchFull Percentage of school capacity

filled--100 pctSchFullNeigh Percentage of nearest neighbor's

school capacity filled--100

(normalized by distance to the

nearest school) pctEntrantsYr Percentage of new enrollees in

elementary school pctWithDrawYr Percentage of graduates from

elementary school existingHomePr Median sales price of existing

homes/1000 newHomePremium Percentage premium for new home nonWhite Percentage non white ageLessThan5 Percentage of population less than

5 years of age highIncome Percentage of household incomes

> 100k collegeEduc Percentage college educated expHouse Percentage of housing stock

valued >300k potentialSubdivUnits Number of potential lots from

undeveloped land based on

zoning regulations recentSubdivRate Percentage of potential lots

subdivided in the previous 3 years pctOfSubdivBlt Percentage of subdivided lots which

became sold homes in the last year Table 2. Summary Statistics (Before Matching and After Matching)

Full Sample Treated

Mean Std Dev. Mean Std Dev. acres 651.472 1205.021 359.713 289.913 distBA 23.875 5.772 23.985 4.406 distDC 38.459 5.162 35.666 7.018 pctSchFull 12.386 25.315 22.771 17.379 pctSchFullNeigh 6.83 12.201 10.642 10.084 pctEntrantsYr 8.053 3.652 8.029 3.149 pctWithDrawYr 6.778 3.811 8.048 3.351 existingHomcPr 276.495 373.588 260.463 371.031 newHomePremium 19.979 46.612 19.520 61.986 nonWhite 19.844 11.826 18.894 8.573 ageLessThan5 9.055 2.375 10.134 2.553 highlncome 22.651 13.782 18.281 12.647 collegeEduc 72.735 14.638 70.823 13.847 expHouse 14.212 18.303 10.413 14.329 potentialSubdivUnits 321.960 432.378 356.261 400.237 recentSubdivRate 4.589 10.143 5.995 14.119 pctOfSubdivBlt 56.228 298.391 11.512 32.370 No. of observations 198 42

Untreated Matched Treated

Mean Std Dev. Mean Std Dev. acres 739.767 1354.845 351.352 313.411 distBA 23.841 6.077 22.878 4.527 distDC 39.304 4.122 37.681 7.188 pctSchFull 9.243 26.519 24.775 17.315 pctSchFullNeigh 5.677 12.576 10.904 9.835 pctEntrantsYr 8.059 3.800 7.455 3.006 pctWithDrawYr 6.394 3.869 6.894 2.818 existingHomcPr 281.347 375.443 296.882 448.050 newHomePremium 20.118 41.102 26.551 74.454 nonWhite 20.104 12.660 19.161 9.583 ageLessThan5 8.728 2.226 9.525 2.645 highlncome 23.974 13.876 20.278 12.58 collegeEduc 73.313 14.864 72.480 13.003 expHouse 15.362 19.239 10.323 12.522 potentialSubdivUnits 311.579 442.377 284.581 303.809 recentSubdivRate 4.164 8.604 5.320 14.904 pctOfSubdivBlt 69.760 339.198 12.858 36.639 No. of observations 156 31

Matched Untreated

Mean Std Dev. acres 360.597 366.092 distBA 22.549 3.533 distDC 38.561 4.057 pctSchFull 27.728 13.567 pctSchFullNeigh 8.271 15.476 pctEntrantsYr 7.629 3.423 pctWithDrawYr 6.871 3.628 existingHomcPr 310.585 487.726 newHomePremium 30.087 58.957 nonWhite 18.898 8.207 ageLessThan5 9.508 2.157 highlncome 211.201 11.898 collegeEduc 73.371 14.335 expHouse 10.444 13.330 potentialSubdivUnits 318.179 298.354 recentSubdivRate 5.171 9.968 pctOfSubdivBlt 8.647 71.784 No. of observations 31 Table 3. Propensity Score Matching Results

On Support Off Support Outcome-Number of Houses

Built 1994 # Control 152 0 # Treated 31 11 Outcome-Number of Houses

Built 1995 # Control 152 0 # Treated 31 11 Outcome-Number of Houses

Built 1996 # Control 152 0 # Treated 31 11 Outcome-Number of Houses

Built 1997 # Control 152 0 # Treated 31 13

Differences in Outcome Means

Unmatched Epan kernel Outcome-Number of Houses

Built 1994 # Control 11.95 7.74 # Treated 6.23 2.74 Outcome-Number of Houses [[DELTA].sub. -500 *


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COPYRIGHT 2007 American Agricultural Economics Association Reproduced with permission of the copyright holder. Further reproduction or distribution is prohibited without permission.
Copyright 2007, Gale Group. All rights reserved. Gale Group is a Thomson Corporation Company.
NOTE: All illustrations and photos have been removed from this article.


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