Tables 3c and 3d report results in which remoteness is proxied by the distance to a major port. We find results that are broadly similar to Tables 3a and 3b, with one noteworthy exception. Remoteness is not related to changes in the service sector and overall GDP during the 2018/19 recession. This difference suggests that the changes during the 2018/19 recession were related to factors involving domestic demand, and it was the remoteness from the largest cities rather than the major ports that was associated with increased (service sector) public spending (Appendix Tables A1-A3).

Fourth, tables 3a-3d show that the large boom in construction activity during 2010-2011 was increasing in remoteness (for both measures). During 2010/2011, real construction sector per capita GVA grew by 2.9 percent (not significant) in the largest city, while it grew by more than 18 percent in provinces at the 90th percentile (i.e., more than 800 km away from the closest large city). Similarly, construction activity grew by 4.8 percent in provinces with a major port, to 15.6 percent in provinces that were at the 90th percentile of remoteness. These results use 2007 and 2012 as base years, and control for differential trends at the province level. A similar pattern emerges by examining simple averages (Table 1). During the 2010/2011 rebound, construction activity increased at an annual rate of 24 percent in the least developed provinces. This rate of increase is unique (in terms of its magnitude) across periods, levels of development, or sectors.

This section has reported results based on an analysis of province-level data. What emerges from this analysis is that Türkiye’s recessions and recoveries have been accompanied by very different changes at the provincial spatial scale. Further, Türkiye’s economy is rapidly urbanizing. This suggests that an analysis of economic activity within and across urban agglomerations may complement the analysis of traditional administrative units (i.e., provinces) conducted above. Such an analysis is presented below.

# **3. Booms and Busts: Analysis of Nighttime Lights**

This section complements the previous one by using remote-sensed nighttime lights data to analyze Türkiye’s business cycles. We do this for five reasons. First, as mentioned above, and particularly relevant to rapidly urbanizing economies like Türkiye, there is a misalignment between administrative delineations and economically meaningful units like systems of cities and urban corridors. Second, the analysis of province-level data does not capture changes at more granular spatial scales. Third, as Henderson, Storeygard, and Weil (2012) highlight, administrative data may be measured with error or may have other biases. Fourth, we are interested in understanding changes before 2004 (when our province level data begins) — particularly during the 2000-2002 recession and during the following 2002-2004 rebound. Fifth, data on nighttime light luminosity and other imagery is available in near-real time, which would allow us to more accurately monitor economies at meso-level scales and inform policy responses when they are most needed.

We use two different sets of measures (based on different satellites and sensors) that estimate nighttime light emissions. For the 2000-2012 period we use a time-consistent version of the DMSP-OLS data (Zhang, Pandey, and Seto (2016)), while for 2013-2019 we use a time-consistent 10