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

Dataset and Overview of the Trends in Moratoria Adoption

To implement the analysis, we compiled the most detailed data available on school district boundaries, moratoria designations for elementary schools in 1994, new residential development, and other relevant determinants of the adoption of moratoria and residential development in Howard County, Maryland. This section describes the data sources and presents basic summary statistics.

School District Boundaries, Moratorium, and Other School-Related Data

There are 32 school districts in Howard County. Information regarding school district boundaries and designations of elementary school moratoria in 1993 were obtained from Howard County Planning Office. For each of the school districts, we have also collected information on the capacity of the different schools and percentage of new enrollees and graduates from elementary schools. We use the percentage of overcapacity for the own school district as well as the percentage of overcapacity for the nearest neighboring school district in the model. (1)

Residential Development Data

We supplement the data on school districts and moratoria designations, with data on new residential development between 1994 and 1997 from the Maryland Property View dataset and the Howard County Tax and Assessment data. These data are disaggregated at the parcel level. To capture development pressure three key variables were constructed for each observation: the number of potential lots from undeveloped land based on zoning laws; the percentage of potential lots subdivided in the previous 3 years; and the percentage of subdivided lots that became sold homes in the last year. The subdivision data were obtained from the actual subdivision database used by the county to track subdivisions from the planning stage to approval.

Census Data, Community Characteristics, and Other Determinants of Development

We have matched the school district data and the parcel-level data on residential development with several census variables measured at the census block. This process consisted of overlapping the census blocks with the school district maps. Whenever a census block belonged to two school districts, we assigned the acreage of the census block that corresponds to each district and allocated the census variables accordingly.

We used the census data to create several variables. These variables aim to capture the population composition as well as the average characteristics of the housing stock. These include: percentage of nonwhite, percentage of children less than five years old, percentage of household income more than 100K, percentage of college educated and the percentage of housing stock valued more than 300K. Finally, we calculated the distances of the centroids of each census block to Washington, D.C. and Baltimore, MD, the two major employment centers for the county.

Table 1 presents a short description of the different variables. Because of the different spatial units of our datasets--school districts, parcels, and census block--we have chosen the final unit of observation for the study to be the census block, as this will capture all the relevant spatial variation on key variables within school districts.

Trends in Moratoria Adoption

Figure 1 presents a map of the school district boundaries highlighting those that were under moratoria in 1994. Of the 32 school districts, 8 school districts were under moratoria. Interestingly, there appears to be a spatial pattern in the adoption of moratoria, with most of these school districts located in the eastern part of Howard County.

Table 2 presents summary statistics for the full sample and a breakdown of census blocks belonging to school districts that were under moratoria in 1994 and those that were not. When comparing census blocks that belong to school districts under moratoria against those that do not, we notice the following important trends. Not surprisingly, the percentage of school overcapacity in treated census blocks (those under moratoria) is distinctly higher than the untreated census blocks (those not under moriatoria): 23% versus 9%, as is the percentage of school overcapacity in neighboring school district: 11% versus 6%. Second, it appears that the untreated census blocks, are wealthier--measured both by the value of existing homes and the percentage of high-income households.

Results

We begin our discussion of results by reexamining the characteristics of the treated versus untreated census blocks after the matching to illustrate the advantages of the matching techniques. We then display the propensity scores in a map to visualize the matching process. Finally, we present the average treatment effects [[DELTA].sup.TT]. We consider the effects of the 1994 moratoria on 1994-1997 new residential development.

Summary Statistics After Matching

By constructing a counter-factual that looks identical to the treated in observable covariates, the matching approach essentially eliminates "outliers" from the original dataset. Table 2 illustrates this point clearly. First, comparing the "treated" versus the "matched treated" columns, we note that the number of treated observations drop from 42 to 31. That is, 11 observations were considered "off support," as there were no untreated observations in the control group with "close enough" propensity scores to these 11 observations. Second, we note that the differences in some of the variables in the treated and untreated groups are reduced substantially. For example, the differences in the percent of school capacity filled and the percent of nearest neighbor's school capacity filled are now much smaller, illustrating that the matching method removes from the dataset treated observations that do not have comparable untreated matches.

[FIGURE 1 OMITTED]

Spatial Distribution of Propensity Scores

Figure 2 displays the map of Howard County and the propensity scores for the different census blocks. The figure reveals some interesting spatial patterns. First, most of the matched census blocks--that is untreated census blocks with relatively higher probability of being under moratoria--seem to be located in the eastern part of the county and right next to census blocks that were treated, so neighboring census blocks have similar characteristics for predicting the probability of adoption of a moratorium. Second, of the 238 census blocks, 157 had a probability greater that 0.01 of being treated, while only 86 have a probability greater than 0.26.

[FIGURE 2 OMITTED]

Effects on New Residential Development

Table 3 presents the results from the propensity score matching model. We evaluate the effects of the 1994 moratoria policy on new residential development in the year of enactment and three subsequent years. The table presents the unmatched mean differences between treated and controls as well as the mean differences after matching, using the Epanechnikov kernel-matching estimator ("Epan" in table 3). Bootstrapped standard errors were calculated and all results are derived from propensity score regressions that pass strict regression based balancing tests as described in Smith and Todd (2005a).

The table highlights the following key results. First, the 1994 moratorium does indeed reduce new residential development. Second, the effects are significant for the two years immediately after the policy is enacted. In 1994, the effect of the policy is to reduce new home construction by five units in the census blocks that were treated, despite the fact that all these census blocks have a similar stock of approved subdivisions. This implies a total county reduction of 155 units or approximately 7% of the projected growth for 1994 based on the county's General Plan. The effect of the policy is even stronger one-year after, perhaps a result of the supply restriction induced by the moratoria in the previous year, i.e., there are no new subdivisions approvals from which homes can be built. In 1995, the effect of the policy is to reduce new development by approximately 202 units in the county, corresponding to 9%. After two years, the policy no longer produces any statistically significant effect.

Conclusions

This paper applied modern matching techniques to evaluate the effects of APFOs on new residential development in Howard County, Maryland. Our results suggest that the policy indeed slowed new development in the two years after it has been enacted. The total reduction in new development during this two-year period corresponded to approximately 355 new housing units, 8 percent of the projected county growth for these two years.

We thank Nancy Bockstael for providing some of the data used, Jeff Bronow and Sharon Melis from the Howard County Planning Department for additional data and policy information, and Joel Landry for outstanding research assistance.

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