Measuring the Impacts of Community Development Initiatives:

Measuring the Impacts of Community Development Initiatives: A New Application of the Adjusted Interrupted Time Series Method We attempt in this article to contribute to the development of empirical methods for measuring the impacts of place-based local development strategies. Previous approaches typically founder against the challenge of estimating the counterfactual of how the outcome indicator of interest would have performed in the impact neighborhood in the absence of the intervention. We introduce a method, labeled an Adjusted Interrupted Time Series, which advances the precision of this counterfactual and offers a stronger basis for drawing causal inferences. In particular, it avoids the neighborhood selection bias by using the idiosyncratic, pre-intervention level and slope of the indicator in the impact neighborhood as the basis for estimating the counterfactual in the postintervention period. We applied the AITS approach in three case studies of large-scale community development initiatives using single-family home prices as the outcome indicator. We found the method capable of measuring impacts that manifested themselves either as a change in the base level of prices or in the rate of price appreciation, an important distinction that prior approaches typically blur. We also found, however, a situation in which the method appears unreliable. The AITS method benefits from more recurrent data on outcomes during the preand post-intervention periods, and an inter-temporal pattern in such data that avoids great volatility. We conclude that the AITS approach to measuring effects of community development initiatives holds strong promise, with caveats.