provinces. These declines were offset by larger relative increases in the service sector. In section 2.3 we show that public spending on defense, health and education (which are included in the service GVA) increased significantly more in the more remote provinces during the 2018/2019 recession. This may have offset the larger relative declines in construction and manufacturing in these provinces.

These findings paint a picture of a volatile economy, characterized by large booms and busts in overall GDP, and also by large changes in growth patterns across sectors. Consistent with the argument by Lucas (1988), quoted above, it is evident that there is a compelling need to identify the drivers of these short-run changes, rather than analyzing long-run average growth rates. However, this will necessarily involve a deeper analysis of the economic (e.g., Gabaix (2011), Baqaee and Farhi (2019)) and political (e.g. Alesina (1987)) drivers, both of which are outside the scope of this paper. Instead, this paper employs a spatial lens to document changes at subnational levels — which perhaps constitutes an incremental step towards gaining a better understanding of the fundamental drivers. In the next sub-section, this paper uses the official province-level data along with two measures of remoteness to document how spatial and sectoral factors interacted with each other during each phase of the business cycle.

# **2.2 Convergence in Economic Activity across Sectors and Periods**

As Figure 1 shows, Istanbul province, which is the wealthiest and best-connected province in the country, grew (in real per capita terms) by around 50 percent between 2004 and 2019. In contrast, Aardahan and Mardin, both of which are among the poorest, grew by almost 100 percent. This subsection examines absolute convergence separately by sectors and periods to document more disaggregate patterns of convergence over time and sectors. Our motivation is to understand how convergence varied across the different periods in order to shed light on how business cycles may influence spatial inequality. We begin by estimating a simple absolute convergence regression using equation 1 below for every period I, where is the log-differenced sectoral GVA or GDP in real per capita terms and l b b is the respective base year GVA or GDP in natural logs and in real capita terms. pp II ΔΔOO OOII 0 ∗l b b pp II II II Table 2, which reports results from estimating equation 1, shows that a 1 percent higher real ΔΔOO = γγ + γγ OO + εε GDP per capita in 2004 (the baseline) is associated with a 0.18 percentage point lower increase in real GDP per capita between 2004 and 2019.<sup>13</sup> Across sectors there was considerable variation in convergence around this average. There was no convergence in agricultural GVA, while manufacturing converged by half as much as GDP convergence. The service sector experienced greater than average convergence. Between 2004 and 2019, real per capita construction GVA converged by more than twice as much as overall GDP. A 1 percent higher construction GVA at the baseline is associated with a .40 percentage point decrease in real per capita construction

> 13 For completeness, refer to conditional convergence regressions in chapter 2 of World Bank (2023).

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