of changes in construction and manufacturing activity. It then develops a data-driven coreperiphery framework that allows for the delineation of polycentric cities as well as urban corridors. Finally, it uses the core-periphery framework to estimate changes within and across urban agglomerations. The last section summarizes our main results and provides concluding thoughts.

# **2. Booms and Busts: Analysis of Official Province-Level Data**

We begin by analyzing booms and busts using official sub-national data disaggregated by sector. We use administrative data because they are consistent with gross domestic product figures and match provincial boundaries. Our objective is to document changes in growth across the business cycle with a focus on understanding how sectoral changes interact with remoteness. To proxy for remoteness, we employ two intuitive measures. The first is the shortest distance from a province (centroid) to one of the two largest cities (Istanbul or Ankara). The second is the closest distance to a province (centroid) that has a major international port (Istanbul, Izmir, Bursa, or Mersin). While both measures are coarse, they capture proximity to domestic and external sources of demand respectively.

We show that the significant growth between 2010 and 2017 was bookended by recessions in which GDP per capita fell more sharply the closer a province was to one of the two largest cities. While, as may be expected, the declines in the manufacturing sector were more pronounced in the better-connected provinces during the 2008/09 recession (induced by the global financial crisis), declines in manufacturing were more pronounced in the remote provinces in 2018/19 (that may have been precipitated by decreases in domestic demand). However, the declines in the remote provinces were offset by a large increase in public spending (on defense, health, and education) during the 2018/19 recession.

This section is divided into three subsections. The first provides macroeconomic context and a broad overview of the changes in sectoral growth by period. The second documents the extent to which the less developed provinces grew more quickly than the other provinces, in each sector and within each period. To do this, we estimate absolute convergence at different periods across the business cycle. The third section documents the relationship between sectoral growth and remoteness during different periods. Together, this section demonstrates that employing a spatial perspective to analyze official sub-national data provides insight into the relationship between business cycles and spatial inequality in Türkiye during the first two decades of the twenty-first century.

## **2.1 The Macroeconomic Context**

In what follows, we use official government data to document the relationships between macroeconomic events and changes in economic activity across provinces. Türkiye is governed as a single entity by a central government, but it is divided into 81 provinces for administrative purposes. Each province is divided into districts, totaling 922. Below districts are towns and villages. The provinces are often grouped into 7 regions and 21 subregions for economic and 5