A key challenge in ex-post policy evaluation is obtaining data in the ideal time frame and level of aggregation for the analyses. In this study, we propose using new granular data as a measure of local economic activity. Specifically, we use number of cars counts in each 500*500-meter mesh as identified from a machine learning algorithm from satellite images. Using the opening of the new international terminal of the Cebu International Airport in the Philippines in 2018 as a quasi-experiment, we estimate the impact of the new terminal on the local economy of Metropolitan Cebu using the new data as the measure of the local economic activity. The results of the DID analysis show that the new airport terminal increased the volume of vehicle traffic in Cebu City. The effect decays with distance from the airport, is stronger in areas where hotels are located, and is most pronounced in the peak months for international tourists. These imply that the opening of the new international terminal promoting the economy in Cebu through increasing international tourists.

JEL codes: R11