across agglomerations after the 2009 recession. Together, our results suggest that similar recessions and recoveries at a macroeconomic scale may be driven by very different changes at more disaggregate spatial scales. This has a bearing on the attendant changes in spatial disparities within a country and the policies designed to remedy them.

This analysis also suggests that remote-sensed data on nighttime luminosity may improve our understanding of changes in sub-national economic activity across the business cycle. While these changes in luminosity are correlated with changes in manufacturing and construction activity, they are less useful for estimating changes in the service and agriculture sectors. However, our analysis demonstrates the usefulness of remote-sensed nighttime lights data when estimating the sub-national impacts of business cycles in rapidly urbanizing economies.

The results highlight one major challenge with conducting any empirical analysis involving sub-national changes in economic activity. We have shown that there is considerable heterogeneity across locations and time. Further, this heterogeneity is not random, but clustered across both dimensions. Two promising types of empirical analysis of sub-national economic changes are especially challenging when dealing with heterogenous clusters within a country. First, as Sun and Shapiro (2022) note, traditional approaches to estimating causal relationships between aggregate shocks and sub-national economic outcomes incorrectly assume that the baseline exposure to an event (such as baseline oil production intensity or suitability of land for potato cultivation) is exogenous. They conjecture that it is likely that the magnitudes of the impacts themselves are heterogenous and correlated with the exposure variables that are typically used, and will, therefore, result in biased estimates.<sup>24</sup> Second, although remote-sensed data are large, machine learning approaches to estimating meso-level changes also need to allow for clustering in the changes in economic activity over time and location. Khachiyan et al. (2022) develop an approach to estimating local economic activity using machine learning that is both novel and has considerable potential to guide policies. However, their approach does not incorporate the fact that modern economies consist of heterogenous spatial clusters of economic activity. Further, the R-squared associated with training the model on data from the 2007 boom economy to predict the more fragile 2017 economy is zero. As they acknowledge, their approach may not be suitable to predicting the impacts of business cycles on local economic activity.<sup>25</sup> In this context, it is also worth noting 24 More broadly our results suggest that sub-national units are affected in many different ways (i.e., across different sectors) by aggregate events in a manner that depends on their location.

25 This paper’s focus is not prediction, and we have not attempted to construct a machine learning model that predicts sub-national economic activity in Türkiye. However, the results are relevant to machine learning approaches because they show that clustering over time and space are important characteristics associated with sub-national changes in economic activity.

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