metadata: authors: 0: author: Baez Ramirez,Javier Eduardo
1: author: Celik,Cigdem
2: author: Kshirsagar,Varun Sridhar
count: Türkiye,Turkiye
docna: 0: docna: A Spatial Perspective on Booms and Busts :
            Evidence from Türkiye
docty: Policy Research Working Paper
theme: Financial Infrastructure and Access,Job Creation,Human Development and Gender,Economic Policy,Economic Growth and Planning,Financial inclusion,Finance,Private Sector Development,Labor Market Policy and Programs,Public Sector Management,Jobs,Administrative and Civil Service Reform,Spatial Growth,Public Administration,Labor Market Institutions,Structural Transformation and Economic Diversification,Inclusive Growth
abstracts: cdata!: This paper combines official subnational
            and remote-sensed data to uncover the relationships between
            business cycles in T&#xfc;rkiye and the corresponding
            changes in economic activity at lower levels of spatial
            aggregation. The objective is to document changes in the
            nature of growth within and across business cycles, with a
            focus on understanding how sectoral changes interact with
            within-country remoteness during each phase. The paper shows
            that: (i) the significant growth between 2010 and 2017 was
            bookended by recessions in which gross domestic product per
            capita fell more sharply the closer a province was to one of
            the two largest cities; (ii) the two recessions differed in
            terms of their sectoral impacts, with manufacturing declines
            inversely related to remoteness during the first recession
            and positively related during the second; (iii) there were
            large increases in the construction sector&#x2019;s
            gross value added during the post-2009
            rebound&#x2014;consistent with unprecedented increases
            in nighttime light luminosity&#x2014;with growth
            positively related to remoteness; and (iv) changes in
            nighttime light luminosity are correlated with changes in
            physical activity: a 10 percent increase in nighttime lights
            is associated with a 3.5 percent increase in construction
            output and a 1.5 percent increase in manufacturing output.
            Together, the results suggest that recessions and recoveries
            that may appear to be similar at a macroeconomic scale may
            be driven by very different changes at more disaggregated
            spatial scales and have varied impacts on regional convergence.
display_title: A Spatial Perspective on Booms and
            Busts : Evidence from Türkiye
listing_relative_url: /research/2023/08/34150166/spatial-perspective-booms-busts-evidence-türkiye
topicv3: Economic Growth m1326241 471,Macroeconomic Monitoring m1326902 440,Growth and Lagging Regions m1326921 462,Spatial Inequality m1327181 1166,Inequality and Shared Prosperity m1327655 700
publishtoextweb_dt: 2023-08-24T00:00:00Z
dois: 10.1596/1813-9450-10553
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 945620
1: author_key: 1826548
2: author_key: 810339
model_extractions: Policy Research Working Paper 10553 A Spatial Perspective on Booms and Busts Evidence from Türkiye _Javier E. Baez Cigdem Celik Varun Kshirsagar_ Poverty and Equity Global Practice August 2023
Policy Research Working Paper 10553

# **Abstract**

This paper combines official subnational and remote-sensed data to uncover the relationships between business cycles in Türkiye and the corresponding changes in economic activity at lower levels of spatial aggregation. The objective is to document changes in the nature of growth within and across business cycles, with a focus on understanding how sectoral changes interact with within-country remoteness during each phase. The paper shows that: (i) the significant growth between 2010 and 2017 was bookended by recessions in which gross domestic product per capita fell more sharply the closer a province was to one of the two largest cities; (ii) the two recessions differed in terms of their sectoral impacts, with manufacturing declines inversely related to remoteness during the first recession and positively related during the second; (iii) there were large increases in the construction sector’s gross value added during the post-2009 rebound— consistent with unprecedented increases in nighttime light luminosity—with growth positively related to remoteness; and (iv) changes in nighttime light luminosity are correlated with changes in physical activity: a 10 percent increase in nighttime lights is associated with a 3.5 percent increase in construction output and a 1.5 percent increase in manufacturing output. Together, the results suggest that recessions and recoveries that may appear to be similar at a macroeconomic scale may be driven by very different changes at more disaggregated spatial scales and have varied impacts on regional convergence.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jbaez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **A Spatial Perspective on Booms and Busts:**

**Evidence from Türkiye**<sup>**1**</sup>

**Javier E. Baez** World Bank

**Cigdem Celik** World Bank

**Varun Kshirsagar** World Bank

## **JEL CLASSIFICATION** : E01, I32, O11, O47, R11

**KEY WORDS** : Spatial Inequality, Regional Economic Activity, Measurement and Data on National Income, Macroeconomic Analyses of Economic Development, Empirical Studies of Economic Growth
# **1. Introduction**

For poorer countries, however, there are many examples of sudden, large changes in growth rates, both up and down. ... I do not see how one can look at figures like these without seeing them as representing possibilities. – Robert E. Lucas (1988) The premise of the aggregative approach to growth was that markets function well enough within countries that we can largely ignore the fact that there is inefficiency and unequal access to resources within an economy when we are interested in dynamics at the country level. The evidence suggests that this is not true ... – Abhijit Banerjee and Esther Duflo (2005) This paper documents changes in the nature of growth within and across business cycles. Our focus is on understanding how changes in sub-national economic activity interact with remoteness across different periods, and influence regional convergence processes.<sup>2</sup> Specifically, we employ a spatial lens to examine the relationship between changes in provincial economic activity across sectors and the corresponding changes in aggregate output in Türkiye during the first two decades of the twenty-first century, a period marked by many booms and busts.

While most developing economies experience aggregate economic fluctuations that overshadow their average annual growth rates (Lucas (1988), Pritchett (2000)), the relationship between the nature of these aggregate fluctuations and the corresponding changes in sub-national activity is largely understudied in developing country contexts.<sup>3</sup> At the same time, there is a growing policy emphasis on the spatial dimensions of poverty and vulnerability to shocks (World Bank (2020)) and the attendant changes in spatial inequality (World Bank (2018)).

Dollar and Kraay (2002) and Dollar, Kleineberg, and Kraay (2016) have shown that, on average, growth is good for the poor.<sup>4</sup> They also conclude that the mechanisms driving this robust relationship are unclear. Lopez (2004) provides evidence pointing to the important roles played 1 This paper reflects the views of the authors and does not reflect the official views of the World Bank, its Executive Directors, or the countries they represent. The corresponding author is Javier E. Baez (Email: jbaez@worldbank.org). We thank David Newhouse for useful comments.

2 In their recent survey of the very large growth and convergence literature, Johnson and Papageorgiou (2020) conclude that: " _A more definitive conclusion from this survey is that the process of growth and so, potentially, of convergence, is not smooth but rather start and stop, and is characterized by significant country heterogeneity_ ".

3 A substantial literature has examined the relationship between volatility and long run growth. Hnatkovska and Loayza (2005), for example, find an inverse causal relationship. This paper examines one country and abstracts from issues related to long run growth.

4 Cerra, Lama, and Loayza (2021) survey the large literature that has examined these issues.

2
by education and infrastructure in driving the growth-poverty relationship, while Loayza and Raddatz (2010) also provide evidence on the roles played by manufacturing and construction (as well as agriculture).<sup>5</sup> This paper attempts to document relationships between aggregate and sub-national growth (and regional convergence), that may serve as an intermediate step towards a more complete understanding of these issues.<sup>6</sup> This paper shows that recessions and recoveries in Türkiye that appear to be similar at a macroeconomic scale may be driven by very different changes at more disaggregate spatial scales. Moreover, the evidence shows that the nature of sector dynamics at lower geographic levels has varied implications on regional convergence and the formation of agglomeration economies.

An additional contribution of this paper is to document relationships that involve the triangulation of official administrative and remote-sensed data when estimating changes in subnational economic activity over the business cycle. Overall, we find that changes in nighttime light luminosity are correlated with changes in physical activity — a 10 percent increase in nighttime lights is associated with a 3.5 percent increase in real construction output, a 1.5 percent increase in real manufacturing output, and a 0.8 percent increase in real gross domestic product.<sup>7</sup> Since the focus of this paper is on understanding the relationship between shocks to sub-national activity and the corresponding aggregate fluctuations, we adjust for unobserved differences in levels and trends across the 81 provinces analyzed when estimating the relationships between sub-national changes in economic activity and changes in nighttime light luminosity.

It is worth noting that part of the association may come from capturing business cycle effects and other changes that are common to all provinces in a given year. When we remove the effect of these annual changes (by using year fixed effects), we find that idiosyncratic province-level shocks to construction and manufacturing activity are still associated with changes in (subnational) nighttime lights, in contrast to shocks to the service and agriculture sectors.<sup>8</sup> This study also sheds light on how agglomeration dynamics (as estimated by changes in the rate of growth in nighttime luminosity in urban cores and their peripheries) relate to aggregate economic expansions and contractions. We show that, in Türkiye, peripheral areas typically 5 Lopez (2004) also shows that financial crises may contribute to the growth-poverty relationship. Related, Davalos et al. (2015) show that regional convergence in Mexico was driven by both the growth of poorer municipalities and the more pronounced impacts of recessions — engendered by external factors like the 2008/09 global financial crisis — in the more developed areas.

6 A sub-national analysis may provide insights into how aggregate national growth relates to both poverty and inequality.

> 7 This paper use temporal deflators, while no spatial deflators were used in the GDP or GVA calculations.

8 It is possible that changes in nighttime lights capture co-movement in sectoral activity between provinces. Indeed, the results in Table 4, taken together, are consistent with this hypothesis.

3
grew faster than their respective urban cores during expansions, while the reverse was true during recessions. Further, the differences in growth between peripheries and their respective urban cores during expansions were more pronounced in the developed provinces because their urban cores grew more slowly.

The following characteristics of Türkiye’s economy lend themselves to examining business cycles from a spatial, and more granular, perspective.<sup>9</sup> First, it is a diverse country. Provinces in the West, closer to Western Europe, are more developed economically and have stronger historical and cultural ties to Western Europe. Provinces in the East either have stronger ties to Asia or to the countries around the Black Sea. Second, Türkiye has experienced booms and busts in the first two decades of the twenty-first century that are pronounced, and not too dissimilar to other middle-income countries (Kose et al. (2021)).<sup>10</sup> Third, Türkiye has simultaneously undergone a process of rapid structural transformation, with the attendant urbanization of many provinces across the country. Fourth, precisely because it straddles Western Europe, the Black Sea region and Asia, economic changes _within_ Türkiye are important from a geopolitical perspective.

This paper provides evidence regarding the nature of two pivotal growth spurts. Acemoglu and Ucer (2015) suggest that Türkiye’s growth between 2002 and 2006 was fundamentally different than the growth that followed the 2008/09 global recession. While we do not attempt to evaluate these periods in a normative sense, we provide evidence in support of the view that the nature of the rebounds differed at both the scale of provinces and urban aggregations.

Related, this paper documents a construction boom that was part of the rebound from the 2008/09 crisis. This may be interpreted as constituting a "big push" in complementary infrastructure investments (Rosenstein-Rodan (1943)). However, issues related to the sustainability of such a push remain relevant across a broad group of countries (Kose et al. (2021)). This paper does not address these issues directly, but documents (spatial) patterns in the sectoral changes at meso-level scales. We find significant differences in the patterns of growth when viewing these booms through a spatial lens. These patterns are often ignored when monitoring growth solely at the national level and serve to reveal changes within subnational economies that are relevant to policy design.

The rest of the paper proceeds as follows. Section 2 describes analysis of Türkiye’s booms and busts using official province-level data. It begins by providing the relevant macroeconomic context. It then documents changes in absolute convergence across sectors and time periods. Finally, it describes results that provide a spatial perspective on sectoral growth rates across the business cycle. Section 3 shows that the changes in nighttime light luminosity are useful signals

> 9 Zürcher (2017) and Pamuk (2018) provide geographic, cultural, historical, political, and economic context.

> 10 We do not examine the external causes that may be common to many booms and busts experienced by middle-income countries. These include, for instance, the related shocks to global savings imbalances and liquidity, which in turn, are related to the monetary policies of the United States and the European Union (e.g., Rajan (2015), Kalemli-Ozcan (2019), Bräuning and Ivashina (2020)).

4
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
geographic reasons, but these groupings do not correspond to official administrative divisions (World Bank (2023)).<sup>11</sup> In this subsection, we use one coarse sub-national classification. Provinces are grouped into three categories: developed, transition, and developing. These are categorized based on real GDP per capita in 2004 and coarsely capture the degree of remoteness, with the developed provinces closest to Western Europe, the provinces categorized as undergoing transition in the middle, and the provinces in Eastern Türkiye classified as being least developed.<sup>12</sup> We delineate years as being part of rebounds, expansions, and recessions based on Zürcher (2017, pg. 352) and World Bank (2019, pg. 23). We begin with official national accounts data from the Turkish Statistical Institute because they are the most consistent and systematic source of economic activity. Our objective is not to delineate and label these macroeconomic events, but rather to document contemporaneous changes across sectors and broad development categories.

Table 1 summarizes average growth rates in real per capita sectoral gross value added (GVA) and gross domestic product (GDP) for the period classification discussed above. We draw five conclusions that provide the macroeconomic context against which a reader may interpret results reported in the rest of the paper. First, our classification of periods in terms of rebounds, expansions, and recessions is consistent with the sectoral and aggregate GDP data. Second, over the course of 2004-2019 manufacturing and construction grew more quickly than agriculture and services across all levels of development. This is consistent with an overall trend towards greater urbanization.

Third, the construction sector was particularly sensitive to the business cycle, with pronounced pro-cyclical booms and busts in its gross value added (GVA). Fourth, the service sector grew by more than overall GDP during the recessions and by less during the expansions. Fifth, the two recessions involved smaller growth rates (or larger declines) for the developed provinces. The large decline in 2008/09 may be explained by the severe global recession. However, the 2018/19 recession involved larger declines in manufacturing and construction outside the developed 11 As a candidate country to join the European Union, Türkiye adopted the Nomenclature of Territorial Units for Statistics (NUTS) in its classification of geographic regions for statistical purposes (World Bank (2023). For that purpose, the country is divided into three regional levels. NUTS 1 divides the country into 12 regions. The 12 NUTS 1 regions are broken down into 26 smaller regions, the NUTS 2 regions.

12 All the regions were ranked according to their GDP per capita in 2004 by World Bank (2023). This regional development classification grouped provinces into three categories according to the following criteria. Less developed/lagging regions have a GDP per capita of less than 60 percent of the country average. Transition regions have a GDP per capita between 60 percent and 90 percent of the country average. More developed/leading regions have a GDP per capita of more than 90 percent of the country average. This regional classification, based on baseline GDP per capita, captures the west–east divide that characterizes the geographic distribution of development disparities in Türkiye. NUTS 1 regions in Northeast Anatolia, Central East Anatolia, and Southeast Anatolia are classified as lagging (less developed) regions in the typology described above ,while most of the NUTS 1 regions in western Türkiye are classified as leading (i.e., more developed).

6
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).

7
growth. Therefore, for anyone trying to gain a better understanding of the pattern exhibited in Figure 1, a closer examination of the construction and service sectors is warranted.

Across the business cycle, Table 2 shows that GDP converged during the 2008/09 recession, the 2010/11 rebound, and the following 2013-2017 expansion. However, as Table 1 indicates (see also the next subsection), the convergence during the 2008/09 recession likely followed from the fact that the recession affected the more connected (and wealthier) provinces disproportionately. Therefore, it is likely that the 2010/11 rebound and to a lesser extent the 2013-2017 expansion drove overall convergence during 2004-2019.

During the rebound and expansion that followed the 2008/09 recession, it was the construction sector that experienced a large convergence in real per capita gross value added. While service sector GVA also converged across provinces, this was more muted. Together, Table 1 and Table 2 suggest that Türkiye had a significant construction boom between 2010 and 2017. In what follows, we show that this boom was more pronounced in the lagging and more remote provinces. More broadly, the next subsection documents the relationships between changes in sectoral gross value added and remoteness across the business cycle.

# **2.3 Sub-National Growth and Remoteness across the Business Cycle**

In what follows, we use two simple measures to proxy for remoteness from an economic perspective. First, we compute the shortest distance to a major city. Türkiye has two very large cities Istanbul and Ankara (Figures 4 and 5). In 2004, the two provinces that contain these cities accounted for 38.9 percent of national GDP. The shortest distance to either of these provinces is therefore a reasonable measure of distance from sources of domestic demand. Second, we compute the shortest distance to a major port, which is inversely related to sources of external demand. We restrict our analysis to the 4 provinces that contain the major ports that trade with the West — Istanbul, Izmir, Bursa, and Mersin. As may be expected these measures are correlated, but imperfectly (the R-squared from regressing one against the other 0.74). Figure 2 shows the strong relationship between distance to a port and GDP per capita in 2004 (the R- squared is 0.51).

As may be expected from examining Figures 1 and 2 together, Figure 3 shows that remote provinces grew faster during the 2004-2019 period. In this subsection, we document how changes across sectors and business cycles combine to produce this overall pattern. Table 3a shows the relationship between sectoral growth rates ( ), which are computed as log pp differences of the sectoral GVA’s or GDP, and the time periods discussed above. We allow the tt ΔΔOO coefficient on each time period to vary by distance ( ) from (the closest) large city (see equation 2 below). pp DD By log differencing, we are controlling for time-invariant level differences between provinces, and by employing province fixed effects ( ) we are controlling for time invariant trend differences between provinces. An alternative approach involves estimating a specification in pp μμ which the dependent variable is in natural logs. In which case, the province fixed effect would eliminate the province-level time-invariant differences, and the province-specific trends would 8
not be averaged out of the analysis. We choose to remove province-specific trends because our focus is on understanding how short-run changes in sub-national economic activity drive aggregate fluctuations. 1 p p p 2 p p p pp pp tt pp pp tt Four patterns are worth noting. First, manufacturing declines were much more pronounced in ΔΔOO = γγ ∗II + γγ ∗II ∗DD + μμ + εε the better-connected provinces during the 2008/09 recession. This is intuitive because this recession was precipitated by the global financial crisis and following recession. In contrast, manufacturing declines were more pronounced in the remote provinces during the 2018/19 recession. This suggests a decline in domestic demand during 2018/19. Table 3b provides magnitudes for these changes. In 2008/09 the manufacturing decline in the largest cities was 12.3 percent, while it was 7.1 percent at the 90th percentile (i.e., around 800 km from a large city). In 2018/19 manufacturing declined by 1 percent in a large city (not statistically significant) but by 10.3 percent in a remote province (at the 90th percentile).<sup>14</sup> Second, during both recessions there were sharp declines in construction activity. While these declines were more muted in remote provinces, the differences in magnitudes were small, and in the case of the 2018/19 recession, not statistically significant. Given the large declines in manufacturing and construction activity in 2018/19 in the remote provinces, it may be expected that GDP per capita fell disproportionately in these provinces. However, Tables 3a and 3b show that this was not the case. Once again, as in 2008/09, the declines in GDP per capita were more muted in the remote provinces.

Third, Tables 3a and 3b show that the large (absolute and relative) declines in manufacturing and construction during 2018/19 were offset by increases in agriculture and the muted relative declines in services in the remote provinces. The relatively smaller decline in services in the remote provinces is explained by tables A2 and A3 in the appendix, which report results involving more disaggregated changes in the service sector. The 2018/19 recession saw unprecedented increases in one service sector category — public spending on health, defense, and education (also refer to Appendix Table A1). While we do not have the data to disaggregate this category further, this suggests a role played by Keynesian anti-cyclical expenditures.<sup>15</sup> 14 Appendix Figure A1 uses a local polynomial fit to show the relationship between changes in manufacturing GVA and the distance to a large city. While this does not control for province level trends, it shows that manufacturing activity declined by more in the better-connected provinces in 2008/09, and in contrast, declined by more in the more remote provinces in 2018/19. Figure A2 shows that the construction activity grew more quickly in the more remote provinces during the 2010/11 rebound. Finally, Figure A3 shows that GDP per capita declined (or stagnated) in provinces close to the large cities during both the 2008/09 and 2018/19 recessions. Further, these figures provide support for our use of a linear specification to estimate the relationship between changes in sectoral activity and the distance to a large city during the different periods.

15 We remind the reader that political economy considerations are outside the scope of this paper.

9
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
version of the VIIRS data (Elvidge et al. (2021)). Both data are constructed at an annual scale and are adjusted for clouds, flares, and other sources of noise. However, the two measures are not fully consistent with each other (Elvidge et al. (2013)). Therefore, we perform and report separate analysis for each sensor.<sup>16</sup>

# **3.1 Nighttime Light Luminosity and Sectoral Economic Activity**

Data on nighttime light luminosity is publicly available and reported to be a useful measure of urban economic activity. When one flies over a country by plane or views the many images available from satellites (e.g., Figure 4), it seems likely that these data capture the location of cities. Indeed, a large literature has confirmed this to be true across countries and time (e.g., Donaldson and Storeygard (2016)). What we are less confident about is whether changes in nighttime light luminosity are related to sub-national changes in economic activity.<sup>17</sup> More specifically, in our context, how do changes in nighttime lights correlate with changes in sectoral economic activity in Türkiye? This subsection shows that changes in nighttime lights are, in fact, correlated with changes in sectoral GVA. However, part of the association may come from capturing business cycle effects and other changes that are common to all provinces in a given year. When we remove the effect of these annual changes (by using year fixed effects), we find that (idiosyncratic) changes in construction and manufacturing are still associated with changes in nighttime lights, but the service and agriculture sectors are not.

Nighttime light emissions are more useful indicators of economic growth in urban areas. Therefore, we use the Global Human Settlement Layer (GHSL) constructed by Melchiorri et al. (2019) data to identify raster grid cells that are likely to be part of urban areas.<sup>18</sup> These data provide a lens through which we can better interpret changes in nighttime light emissions. In panels A-D in Table 4 we incorporate this data in different ways. Across all panels, we exclude rural provinces — provinces which contain no urban grid cells based on the 2000 GHLS data. Panel A in Table 4 sets a useful benchmark and is estimated by equation 3. We regress logdifferenced sectoral GVA or GDP ( ) on log differenced nighttime light luminosity (Δ ), pp pp which as in equation 2 also involves controlling for unobserved province level effects, province tt tt ΔΔOO ΔΔLL trends and has the standard errors clustered by province. = Δ pp pp pp tt tt pp tt ΔΔOO ΔΔLL + μμ + εε 16 Table 4 uses both measures in the same regression but includes year effects (which eliminates mean differences since the sensor measures are annual) and does not include changes in 2013 (which would have involved two different sensor measures).

17 Gibson and Boe-Gibson (2021) show that NTL changes are weakly correlated with GDP changes, controlling for year fixed effects, for counties in the United States. Their specification is in levels, while our specification uses log-differences (i.e., growth rates). Further, the US has a very large service sector; therefore, a greater fraction of economic activity is intangible, but it is does have better quality data. We are unsure how this result would transfer to countries that are less developed than the US.

18 https://ghsl.jrc.ec.europa.eu/download.php.

11
We compare annual changes (log differenced) in total provincial GVA with changes in nighttime lights (NTL) across sectors. We employ province fixed effects, which means we not only remove unobserved time-invariant differences by log-differencing, but also adjust for different province-level trends. A 1 percent increase in NTL is associated with a 0.08 percent increase in real GDP. When disaggregated by sector, as may be expected, the physical subsectors (construction and manufacturing) drive this relationship.

In Panel B we report results that adjust for differences across years by including year fixed effects as in equation 4 below. = Δ pp pp pp tt tt yyppyypp pp tt This means that we remove the average effect of aggregate (country-wide) business cycles. ΔΔOO ΔΔLL + II + μμ + εε Idiosyncratic changes in construction and manufacturing are still associated with changes in NTL, but the coefficients for the other sectors are no longer significant. In panel C, we report results using changes in NTL from grid cells that are contained in urban districts, within the urban provinces. Urban districts are defined to be those that contain urban agglomeration grid cells. Overall, we view urban districts as containing a core (comprising the urban grid cells) and a periphery, other grid cells in the district. There is an increase in the magnitudes of the construction and manufacturing coefficients, as well as the associated t-statistics, when restricting the NTL averages to urban districts.

Finally, in panel D, we compute changes in the NTL averages based exclusively on the urban grid cells in each province. The results show a large increase in the magnitudes of both coefficients, although for manufacturing, the standard error is higher (and the t-statistic falls to below a significant threshold). For construction, however, limiting the NTL averages to just the urban grid cells results in a sharper signal. A 1 percent increase in NTL is associated with a 0.26 percent increase in construction GVA, controlling for time-invariant unobserved province level effects, province trends, and year effects.<sup>19</sup> Together, Table 4 provides evidence in support of the idea that changes in nighttime light luminosity are useful signals of changes in economic activity across most sectors (panel A). Further, panels B-D show that even after controlling for aggregate effects (such as those associated with business cycles), nighttime light changes are associated with changes in real construction and manufacturing gross-value added. This in turn suggests that changes in nighttime lights capture economically meaningful changes within and across urban economies.<sup>20</sup> 19 We control for the different sensor (DMSP or VIIRS) average effects with the year effect because our data is annual, and we use DMSP until 2012 and VIIRS thereafter. We exclude 2013 from the analysis because we do not want to combine the DMSP and VIIRS measures.

20 In Appendix Table A6, we report results using placebo variables for changes in NTL that correspond to Panels A and C in Table 4. Table A6 shows that NTL changes from the previous year or the following year have coefficients that are close to zero and not statistically significant.

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# **3.2 The Construction of a Core-Periphery Typology**

This section describes the construction of a typology that we use to interpret changes in nighttime light emissions, so that we may estimate changes in economic activity within and across urban agglomerations. Our objective is to use a methodology that is easily interpretable and able to delineate urban agglomerations and their respective peripheries.

A traditional core-periphery model used by economists (e.g., Krugman (1991)) assumes a monocentric urban core and a surrounding periphery. However, urban agglomerations are often polycentric (e.g., Kloosterman and Musterd (2001)) with a given agglomeration able to benefit from the sizes of nearby agglomerations (e.g., Burger et al. (2015)). Further, the use of existing district boundaries to delineate urban areas suffers from two additional problems. First, as Fotheringham and Wong (1991) have shown, the modifiable areal unit problem makes statistical analysis of areal data dependent on the particular boundaries used. Second, urban boundaries are not consistent with administrative boundaries.

To our knowledge there is no standard method that is used to construct polycentric agglomerations. We employ the following two steps that combine the GHSL data and a clustering methodology to delineate urban agglomerations that are polycentric. First, starting with the individual raster pixels identified as being high-density (i.e., urban) by the GHSL for 2000, we construct a distance matrix, where the distance is computed using the cosine formula (to account for the Earth’s curvature). While the GHSL data is available every 5 years, we use the same typology for the entire 2000-2019 period, so that our results may be more easily interpreted.

Second, we employ hierarchical clustering methodology (Friedman, Hastie, and Tibshirani (2001)) to group the grid cells into urban agglomerations. The single linkage method we employ involves starting with every grid cell being marked as being in its own cluster. It then proceeds sequentially to combine clusters and stops when the nearest neighbor distance between a cluster and any other grid cell is greater than a parameter we define (discussed below). There are two advantages to using this approach. First, this allows the data to determine the number of clusters (i.e., urban agglomerations). Second, the approach and the results are easily interpretable because two different agglomerations are grouped based on a cut-off distance between the nearest neighbors between the groups. This is preferable to other approaches in our context, because agglomerations often cluster along highways or coasts.<sup>21</sup> Therefore, we view the requirement to select a parameter – the distance between the nearest neighbor grid cells across clusters — as a strength which allows us to make the analysis more interpretable. We chose 50 km., motivated by the idea that this distance corresponds to a relatively accessible commute, but one may imagine other choices. For Türkiye, a 25 km cut-off

> 21 In other contexts, compactness of clusters is desirable (e.g., Friedman, Hastie, and Tibshirani (2001)), and the possibility of forming a chain is seen as a weakness. But chains are useful in our context (i.e., we allow for urban corridors).

13
would group, for example, Istanbul with Kocaeli and Mersin with Adana and result in 109 agglomerations. While a 50 km cut-off would include Bursa in the first group and Hatay in the second group and result in 44 agglomerations (larger than 10 grid cells). We report our results for the larger agglomerations (Figure 5).<sup>22</sup> As with the construction of urban agglomerations, there is no standard approach to delineating peripheral areas. For this paper, we define peripheral areas as being in the same district as an agglomeration but not classified as being a high-density urban area by the GHSL in 2000 (see Figure 6). Once again, there are other choices that may be made. Our choice is based on the reasoning that buffers around urban agglomerations that are not contiguous are challenging to both conceive of in theory and construct in practice. In contrast, classifying grid cells that are not high-density, but in the same district as an agglomeration as peripheral, is both simpler to conceive of and to construct. In the next section, we examine changes in nighttime light emissions across periods using this typology.

# **3.3 Booms and Busts within and across Urban Agglomerations**

In what we follow we restrict our analysis to comparing estimates of Night-Time Light Luminosity (NTL) using one sensor (DMSP) or both (DMSP and VIIRS) but being careful to not compare changes using two different sensors. Table 5 shows average changes in growth rates of NTL by period employing the core-periphery delineation discussed above. We do not include the periods after 2012 (when the DMSP is not available) but include the 2000-2002 recession and the 2008-2009 recession. We find a clear pattern. NTL falls during the two recessions, rises slowly during the 2004-2006 expansion, and rises rapidly during the two rebounds. This confirms (part of) our classification of time periods.

Table 5 also shows that the 2010-2011 rebound was much more pronounced (in terms of increases in NTL) than the 2002-2004 rebound. On average in the urban cores, NTL increased by 5.6 percent during the 2002-2004 rebound and by 11.6 percent during the 2010-2011 rebound. In the peripheries, NTL increased by 10.9 percent during the 2002-2004 rebound and 16.3 percent during the 2010-2011 rebound. Further, relative changes in NTL growth differed across cores and peripheries and across development levels. During the 2002-2004 rebound, across all development levels the peripheral areas grew faster than the urban cores. In contrast, during the 2010-2011 rebound the urban cores in the least developed provinces grew almost as fast as the peripheries. While in the developed provinces, the cores grew less fast. Our main takeaway from Table 5 is that the 2010-2011 rebound was associated with exceptionally large increases in

> 22 Some economic mechanisms may be more relevant for shorter distances (e.g., education spillovers, labor markets for less skilled works), while others for longer distances (within-city trade, labor markets for higher skilled workers). We leave further analysis of this idea to future research.

14
NTL in both the urban cores and peripheries, particularly in the agglomeration areas in the least developed provinces.<sup>23</sup> Table 6 reports results on convergence _across_ agglomeration areas. For the 2000-2012 period, the largest convergence in NTL occurred during the 2010-2011 rebound. Convergence across agglomeration areas during 2002-2004 rebound was slightly more than half as large (-18.5 percentage points vs -33.1 percentage points). While part of this pattern may be explained by top coding of the DMSP measures, in Table A5 in the appendix we show that the results are similar after removing the developed provinces (i.e., excluding the Istanbul-Bursa, Ankara, Izmir, and Antalya agglomeration areas). Convergence of NTL across agglomeration areas is estimated to have a smaller magnitude and is not statistically significant during both the 20002002 and 2008-2009 recessions as well as the 2004-2006 expansion. Together, Table 6 suggests that convergence _across_ agglomerations was most pronounced during the 2010-2011 rebound and the subsequent 2013-2017 expansion.

Table 7 estimates changes in luminosity emitted by the peripheral areas relative to their respective urban cores. Across the country, there is a clear pattern — during recessions, peripheries grew more slowly than their cores, while the reverse is true for expansions. During the 2010-2011 rebound, peripheries grew relatively faster than their urban cores in agglomeration areas located in the developed provinces. This may be explained by the fact that both cores and peripheries grew very fast in the least developed areas, while the urban cores in the developed areas grew more slowly.

Our analysis of changes in nighttime light luminosity between and within agglomeration areas shows that urbanization accelerated rapidly during the rebounds and expansions, with urbanization during the 2010-2011 rebound occurring at an unprecedent rate. Future work could extend this analysis by examining how the location and structure of urban agglomerations may engender spillovers between provinces, especially in terms of the impacts of both positive and negative shocks.

# **4. Conclusions**

We document four important facts that relate business cycles to regional inequality in Türkiye. First, real GDP per capita declines were more pronounced in the provinces closer to the largest cities during both the 2008/09 and 2018/19 recessions. Second, these recessions had different sectoral impacts. Manufacturing declined in the better-connected areas during the 2008/09 recession, while it declined in the more remote provinces in 2018/09. Third, while the post-2009 recovery was characterized by large increases in construction activity across the country, these increases were much more pronounced in areas that were further from large cities and major ports. Fourth, there was an unprecedented degree of convergence in nighttime luminosity

> 23 These averages hide differences across agglomeration areas. An interested reader may turn to Tables A4aA4c for analogous results for each agglomeration area.

15
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.

16
that shocks engender _changes_ to granular units that may not be aggregated in a simple manner (Acemoglu, Akcigit, and Kerr (2016), Baqaee and Farhi (2019)).<sup>26</sup> Perhaps because of these challenges, economists studying developing economies have traditionally focussed their attention on analyses that either involve disaggregate units (households, firms, villages) or units that are very large (states, countries). In contrast, economywide empirical analysis at intermediate levels of aggregation is far less common.<sup>27</sup> Yet, there is a growing consensus regarding the importance of understanding broad changes in economic activity at meso-level scales. This paper’s objective is to contribute to the development of simple and transparent approaches that estimate changes in sub-national economic activity across sectors and business cycles. The results suggest that changes in nighttime light emissions may complement more traditional types of economic analysis, providing a spatial perspective on business cycles in near-real time.

26 There are two alternative approaches that ideally would be used together in a complementary manner. First, model and estimate the aggregation pathways using microeconomic data on household and firms (c.f. Browning, Hansen, and Heckman (1999)). Second, directly estimate changes at meso-levels of interest. While the latter cannot provide counterfactuals and is purely descriptive, it is considerably easier, and much more amenable to being conducted in scarce data contexts typical of developing countries. This is what we have attempted to do in this paper.

27 Analysis at intermediate levels has traditionally focussed on sectoral or specific concerns: e.g., poverty maps, remoteness and transport accessability, urban structure and its relationship to congestion and agglomeration, or agricultural productivity.

17
**Table 1: Average annual changes in real per capita Gross Value Added and GDP by** **<u>period</u>**

||**Mining **|**Manufacturing **|**Construction**|**Taxes**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|---|---|
||||**2004-2006**|**Expansion**||||
|Less Developed|4.4|4.9|16.4|6.7|3.1|4.4|5.3|
|Transition|6.6|11.0|17.0|7.5|4.5|3.9|6.2|
|More Developed|7.0|7.3|16.7|7.7|2.6|4.8|6.5|
|National|6.0|7.7|16.7|7.3|3.4|4.3|6.0|
||||**2008-2009**|**Recession**||||
|Less Developed|-6.0|-4.5|-10.5|-5.1|1.6|-2.0|-2.8|
|Transition|-2.4|-5.2|-11.0|-5.0|2.3|-1.7|-2.5|
|More Developed|-4.6|-6.0|-13.0|-6.3|4.1|-1.9|-3.8|
|National|-4.3|-5.2|-11.5|-5.5|2.7|-1.9|-3.0|
||||**2010-2011**|**Rebound**||||
|Less Developed|6.5|12.5|24.8|14.7|9.4|5.2|9.0|
|Transition|9.6|14.2|15.7|12.8|2.9|5.4|7.5|
|More Developed|6.4|11.5|16.5|11.4|2.7|5.2|7.7|
|National|7.5|12.7|19.0|13.0|5.0|5.3|8.1|
||||**2014-2017**|**Expansion**||||
|Less Developed|3.9|7.4|3.4|4.7|2.7|4.9|4.6|
|Transition|6.5|6.0|5.0|4.2|1.6|3.9|4.0|
|More Developed|3.2|4.9|6.6|4.9|0.9|4.5|4.6|
|National|4.5|6.1|5.0|4.6|1.7|4.4|4.4|
||||**2018-2019**|**Recession**||||
|Less Developed|11.9|-1.2|-10.2|-1.3|-0.9|2.9|1.1|
|Transition|4.7|-4.7|-6.7|-0.6|2.4|3.0|1.0|
|More Developed|-0.1|-1.2|-6.5|-1.5|1.8|2.1|0.4|
|National|5.5|-2.4|-7.8|-1.2|1.1|2.7|0.8|
||||**Full Sample**|**: 2004-2019**||||
|Less Developed|4.1|4.6|4.5|4.1|3.1|3.5|3.7|
|Transition|5.3|4.7|4.2|3.9|2.5|3.1|3.4|
|More Developed|2.6|3.7|4.7|3.6|2.1|3.3|3.4|
|**National**|4.0|4.3|4.4|3.9|2.5|3.3|3.5|

Note: Mining is short for Mining and Utilities, which includes mining and quarrying, electricity, gas, steam and air conditioning supply, and water supply, sewage, and waste management.

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**Table 2: Convergence across Provinces in real per capita sectoral GVA and GDP**

||**Manufacturing**|**Construction**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|
||Dep. Var:%Ch<br>|ange between 2<br>|004 and 2006<br>|<br>||
|Ln Value in 2004|-1.55**|-4.55**|-2.97|-1.77**|-0.05|
||[-2.19]|[-2.13]|[-0.55]|[-2.07]|[-0.04]|
|Constant|27.23***|55.39***|28.55|29.48***|12.82|
||[6.17]|[4.23]|[0.73]|[4.07]|[1.07]|
|R-squared|0.04|0.06|0.01|0.05|0.00|
||Dep. Var:%Ch|ange between 2|007 and 2009|||
|Ln Value in 2007|-3.10***|-0.94|-0.30|-3.51***|-5.55***|
||[-4.09]|[-0.69]|[-0.08]|[-3.21]|[-5.08]|
|Constant|10.49*|-18.85**|7.62|28.27***|47.03***|
||[1.95]|[-2.17]|[0.27]|[3.01]|[4.62]|
|R-squared|0.15|0.00|0.00|0.14|0.22|
||Dep. Var:%Ch|ange between 2|009 and 2011|||
|Ln Value in 2009|-1.59|-27.87***|-0.36|-4.19***|-8.29***|
||[-1.24]|[-6.59]|[-0.16]|[-2.95]|[-5.32]|
|Constant|37.85***|218.66***|12.00|45.12***|94.46***|
||[4.32]|[8.30]|[0.72]|[3.74]|[6.50]|
|R-squared|0.01|0.33|0.00|0.10|0.25|
||Dep. Var:%Ch|ange between 2|013 and 2017|||
|Ln Value in 2013|-2.22|-27.85***|0.32|-8.87***|-4.86**|
||[-0.80]|[-3.91]|[0.13]|[-3.96]|[-2.16]|
|Constant|44.09**|211.40***|5.50|93.67***|61.68***|
||[2.07]|[4.31]|[0.31]|[4.79]|[2.87]|
|R-squared|0.01|0.18|0.00|0.20|0.05|
||Dep. Var:%Ch|ange between 2|017 and 2019|||
|Ln Value in 2017|3.31|3.02|0.27|-0.04|-1.36|
||[1.38]|[0.36]|[0.17]|[-0.02]|[-0.79]|
|Constant|-26.49|-68.18|0.35|5.75|14.38|
||[-1.39]|[-1.14]|[0.03]|[0.31]|[0.86]|
|R-squared|005|000|000|000|001|
||.<br>Dep. Var:%Ch|.<br>ange between 2|.<br>004 and 2019|.<br>|.|
|Ln Value in 2004|-9.27***|-40.22***|4.62|-22.77***|-18.70***|
||[-2.77]|[-4.99]|[0.55]|[-5.54]|[-5.75]|
|Constant|138.08***|300.64***|-1.66|250.57***|225.37***|
||[5.71]|[6.13]|[-0.03]|[7.27]|[7.46]|
|R-squared|0.11|0.30|0.01|0.39|0.32|

Notes: t-statistics based on robust standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.1. All regressions use data on all 81 provinces.

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**Table 3a: Growth in real per capita sectoral GVA and GDP by distance to major city**

||**Manufacturing**|**Construction**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|
|2004-2006 Expansion|2.98**|-1.44|7.85***|1.49***|2.11***|
||[2.44]|[-0.81]|[2.94]|[3.51]|[2.91]|
|2008-2009 Recession|-12.30***|-28.83***|10.48***|-7.62***|-6.80***|
||[-11.87]|[-18.09]|[2.97]|[-18.85]|[-11.20]|
|2010-2011 Rebound|9.39***|2.95|8.24***|-2.61***|3.63***|
||[4.79]|[1.16]|[3.37]|[-6.24]|[4.94]|
|2013-2017 Expansion|2.76**|-6.30***|7.61***|-2.63***|0.37|
||[2.43]|[-2.93]|[3.52]|[-7.58]|[0.75]|
|2018-2019 Recession|-1.09|-40.19***|7.74***|-5.27***|-4.58***|
||[-0.67]|[-13.51]|[3.44]|[-9.11]|[-7.80]|
|2004-2006 Expansion X Distance|0.24|0.58|0.45|-0.09|0.09|
||[1.05]|[1.21]|[0.76]|[-1.09]|[0.71]|
|2008-2009 Recession X Distance|0.60**|0.80*|-0.41|0.13*|0.24*|
||[2.43]|[1.75]|[-0.57]|[1.84]|[1.79]|
|2010-2011 Rebound X Distance|-0.07|1.77***|0.60|0.25**|0.44**|
||[-0.16]|[3.03]|[1.04]|[2.41]|[2.55]|
|2013-2017 Expansion X Distance|0.09|-0.03|0.09|0.15*|0.03|
||[0.27]|[-0.05]|[0.20]|[1.88]|[0.33]|
|2018-2019 Recession X Distance|-1.09**|0.89|-0.12|0.42***|0.37***|
||[-2.22]|[1.23]|[-0.27]|[3.13]|[2.91]|
|Constant|4.54***|13.08***|-6.04***|6.22***|3.66***|
||[9.75]|[16.54]|[-5.08]|[42.07]|[14.06]|
|Observations|1,134|1,134|1,134|1,134|1,134|
|R-squared|0.23|0.47|0.04|0.42|0.39|
|Number ofprovinces|81|81|81|81|81|

Notes: t-statistics based on robust standard errors (clustered by province) in parenthesis *** p<0.01,

** p<0.05, * p<0.1. All regressions use data on all 81 provinces and employ province fixed effects. Distance to major city is computed as the shortest distance between the province centroid and the centroids of the provinces with the two largest cities (Istanbul and Ankara) using the Vincenty method. This is measured in units of 100 km.

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**Table 3b: Growth in real per capita sectoral GVA and GDP by distance to major city**

|**Distance to Major City**|**Manufacturing **|**Construction**|**Services**|**GDP**|
|---|---|---|---|---|
|**2004-2006 Expansion**|||||
|Large City|2.98**|-1.44|1.49***|2.11***|
|25th Percentile|3.44***|-0.3|1.32***|2.29***|
|50th Percentile|3.81***|0.6|1.18***|2.44***|
|75th Percentile|4.42***|2.0|0.95***|2.68***|
|90th Percentile|5.03***|3.5|0.73*|2.92***|
|||**2008-2009 Rec**|**ession**||
|Large City|-12.30***|-28.83***|-7.62***|-6.80***|
|25th Percentile|-11.12***|-27.28***|-7.38***|-6.34***|
|50th Percentile|-10.21***|-26.07***|-7.18***|-5.98***|
|75th Percentile|-8.67***|-24.05***|-6.87***|-5.38***|
|90th Percentile|-7.15***|-22.02***|-6.55***|-4.78***|
|||**2010-2011 Reb**|**ound**||
|Large City|9.39***|2.95|-2.61***|3.63***|
|25th Percentile|9.25***|6.38***|-2.13***|4.48***|
|50th Percentile|9.14***|9.06***|-1.75***|5.14***|
|75th Percentile|8.96***|13.57***|-1.11***|6.25***|
|90th Percentile|8.78***|18.06***|-.48|7.36***|
|||**2013-2017 Exp**|**ansion**||
|Large City|2.76**|-6.30***|-2.63***|0.37|
|25th Percentile|2.93***|-6.36***|-2.34***|0.44|
|50th Percentile|3.06***|-6.39***|-2.11***|0.49|
|75th Percentile|3.28***|-6.47***|-1.73***|0.58|
|90thpercentile|3.50***|-6.54*|-1.36***|0.67|
|||**2018-2019 Rec**|**ession**||
|Large City|-1.09|-40.19***|-5.27***|-4.58***|
|25th Percentile|-3.19***|-38.46***|-4.45***|-3.85***|
|50th Percentile|-4.85***|-37.11***|-3.81***|-3.29***|
|75th Percentile|-7.61***|-34.84***|-2.74***|-3.24***|
|90thpercentile|-10.38***|-32.59***|-1.67*|-1.39*|

Notes: F-stat significance based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated combining major city (which corresponds to the main effect) and distance parameters (for each sector and time period separately) estimated in Table 3a. All regressions use data on all 81 provinces and employ province fixed effects.

Distance to major city is computed as the shortest distance between the province centroid and the centroids of two provinces that have the two major cities (Istanbul and Ankara) 21
**Table 3c: Growth in real per capita sectoral GVA and GDP by distance to major port**

||**Manufacturing**|**Construction**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|
|2004-2006 Expansion|2.92***|-2.68|7.35***|1.71***|2.05**|
||[2.82]|[-1.60]|[2.70]|[4.64]|[2.60]|
|2008-2009 Recession|-12.68***|-29.38***|8.99***|-7.82***|-7.05***|
||[-12.63]|[-21.31]|[2.99]|[-18.75]|[-11.63]|
|2010-2011 Rebound|10.06***|4.87**|7.30***|-2.47***|3.71***|
||[6.03]|[2.03]|[2.70]|[-5.88]|[4.56]|
|2013-2017 Expansion|3.53***|-6.15***|6.89***|-2.26***|0.69|
||[3.38]|[-3.09]|[3.20]|[-6.37]|[1.17]|
|2018-2019 Recession|-0.97|-38.98***|7.30***|-4.46***|-3.76***|
||[-0.63]|[-14.23]|[3.39]|[-7.10]|[-5.34]|
|2004-2006 Expansion X Distance|0.27|0.93*|0.60|-0.15*|0.12|
||[1.32]|[1.91]|[0.96]|[-1.95]|[0.76]|
|2008-2009 Recession X Distance|0.73***|0.98**|-0.05|0.18**|0.31**|
||[3.11]|[2.28]|[-0.08]|[2.53]|[2.18]|
|2010-2011 Rebound X Distance|-0.24|1.38**|0.87|0.23**|0.44**|
||[-0.62]|[2.35]|[1.41]|[2.18]|[2.34]|
|2013-2017 Expansion X Distance|-0.10|-0.07|0.28|0.06|-0.04|
||[-0.33]|[-0.12]|[0.59]|[0.76]|[-0.34]|
|2018-2019 Recession X Distance|-1.18**|0.63|-0.02|0.24|0.19|
||[-2.57]|[0.90]|[-0.04]|[1.63]|[1.25]|
|Constant|4.54***|13.08***|-6.04***|6.22***|3.66***|
||[9.77]|[16.49]|[-5.10]|[40.80]|[13.94]|
|Observations|1,134|1,134|1,134|1,134|1,134|
|R-squared|0.24|0.47|0.04|0.41|0.40|
|Number ofprovinces|81|81|81|81|81|

Notes: t-statistics based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. All regressions use data on all 81 provinces and employ province fixed effects. Distance to major a port is computed as the shortest distance between the province centroid and the centroids of four provinces that have major ports (Istanbul, Izmir, Bursa and Mersin). This is measured in units of 100 km.

22
**Table 3d: Growth in real per capita sectoral GVA and GDP by distance to major port**

|**Distance to Major Port**|**Manufacturing **|**Construction**|**Services**|**GDP**|
|---|---|---|---|---|
|**2004-2006 Expansion**|||||
|Port Province|2.92***|-2.68|1.71***|2.05**|
|25th Percentile|3.41***|-0.97|1.43***|2.26***|
|50th Percentile|3.75***|0.204|1.24***|2.41***|
|75th Percentile|4.53***|2.86*|0.81***|2.75***|
|90th Percentile|5.03***|4.59*|0.53|2.96***|
|||<br>**2008-2009 Rec**|**ession**||
|Port Province|-12.68***|-29.38***|-7.82***|-7.05***|
|25th Percentile|-11.33***|-27.57***|-7.48***|-6.47***|
|50th Percentile|-10.39***|-26.33***|-7.25***|-6.07***|
|75th Percentile|-8.29***|-23.52***|-6.73***|-5.17***|
|90th Percentile|-6.92***|-21.69***|-6.39***|-4.59***|
|||**2010-2011 Re**|**bound**||
|Port Province|10.06***|4.87**|-2.47***|2.05**|
|25th Percentile|9.60***|7.40***|-2.05***|4.52***|
|50th Percentile|9.29***|9.15***|-1.76***|5.08***|
|75th Percentile|8.59***|13.11***|-1.11***|6.34***|
|90th Percentile|8.14***|15.68***|-0.68|7.16***|
|||**2013-2017 Exp**|**ansion**||
|Port Province|3.53***|-6.15***|-2.26***|0.69|
|25th Percentile|3.34***|-6.27***|-2.14***|0.61|
|50th Percentile|3.20***|-6.36***|-2.06***|0.55|
|75th Percentile|2.91**|-6.55***|-1.88***|0.43|
|90thpercentile|2.71|-6.68***|-1.77***|0.35|
|||**2018-2019 Rec**|**ession**||
|Port Province|-0.97|-38.98***|-4.46***|-3.76***|
|25th Percentile|-3.14***|-37.81***|-4.02***|-3.41***|
|50th Percentile|-4.63***|-37.01***|-3.72***|-3.18***|
|75th Percentile|-8.01***|-35.20***|-3.04***|-2.65***|
|90thpercentile|-10.20***|-34.03***|-2.59***|-2.30***|

Notes: F-stat significance based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated combining port province and distance parameters (for each sector and time period separately) estimated in Table 3c. All regressions use data on all 81 provinces and employ province fixed effects. Distance to a major port is computed as the shortest distance between the province centroid and the centroids of four provinces that have major ports (Istanbul, Izmir, Bursa and Mersin).

23
**Table 4: Relationship between Nighttime Light Luminosity and Sectoral GVA and GDP**

||**Mining** **Co**<br>D|**nstruction**<br>**M**<br>ep. Var: % Annu|**anufacturing**<br>**A: Province Fix**<br>al Change in Me|**Services**<br><br>**ed Effects**<br>asure of Econ|**Agriculture**<br>omic Activit|**GDP**<br>y|
|---|---|---|---|---|---|---|
|% Ch. NTL|0.08***|0.35***|0.15***|0.03***|-0.01|0.08***|
||[5.05]|[8.35]|[6.35]|[7.82]|[-0.37]|[7.55]|
|Constant|0.06***|0.02***|0.05***|0.05***|0.03***|0.04***|
||[58.85]|[7.80]|[33.76]|[175.27]|[19.18]|[62.57]|
|Obs.|798|798|798|798|798|798|
|R-squared|0.02|0.09|0.05|0.03|0.00|0.08|
|**B: Province a**|**nd Year Fixed**|**Effects**|||||
||De|p. Var: % Annua|l Change in Mea|sure of Econo|mic Activity||
|% Ch. NTL|-0.02|0.10**|0.05**|0.00|0.02|0.02*|
||[-1.28]|[2.48]|[2.26]|[0.52]|[0.54]|[1.90]|
|Constant|0.09***|0.13***|0.12***|0.08***|0.08***|0.09***|
||[12.33]|[10.83]|[13.97]|[31.97]|[4.38]|[18.68]|
|Obs.|798|798|798|798|798|798|
|R-squared|0.18|0.61|0.35|0.55|0.10|0.48|
||**C: **<br>De|**Province and Ye**<br>p. Var: % Annu|**ar Fixed Effects:**<br>al Change in Me|**NTL from U**<br>asure of Econ|**rban District**<br>omic Activity|**s**<br>|
|% Ch. NTL|-0.03|0.13**|0.07***|-0.00|0.01|0.02|
||[-1.10]|[2.63]|[2.76]|[-0.44]|[0.30]|[1.51]|
|Constant|0.09***|0.13***|0.12***|0.08***|0.08***|0.09***|
||[12.95]|[10.78]|[13.48]|[31.07]|[4.52]|[18.52]|
|Obs.|798|798|798|798|798|798|
|R-squared|0.18|0.61|0.35|0.55|0.10|0.48|
||**D: Pro**<br>De|**vince and Year**<br>p. Var: % Annu|**Fixed Effects: NT**<br>al Change in Me|**L from Urba**<br>asure of Econ|**n Core Grid**<br>omic Activity|**Cells**<br>|
|% Ch. NTL|-0.06|0.26***|0.11|-0.01|-0.11|-0.00|
||[-1.02]|[2.82]|[1.57]|[-0.65]|[-1.39]|[-0.10]|
|Constant|0.09***|0.14***|0.12***|0.08***|0.07***|0.08***|
||[10.55]|[10.40]|[10.91]|[24.29]|[4.16]|[17.43]|
|Obs.|798|798|798|798|798|798|
|R-squared|0.18|0.61|0.35|0.56|0.10|0.48|

Notes: Robust t-statistics in brackets. *** p<0.01, ** p<0.05, * p<0.1. All regressions use province fixed effects and report t-stats consistent with robust standard errors clustered by province. All regressions analyze 57 urban provinces. A province is defined to be urban if it contains any part of an urban agglomeration. Panel C uses data exclusively from urban districts in a province, which are defined to be districts that contain any part of an urban agglomeration. Panel D uses data from grid cells that were categorized by the GHLS in 2000 to be urban.

24
**Table 5: Average annual changes in Nighttime Light Luminosity**

||**2000-200**<br>Recessio|**2**<br>n|**2**<br>Re|**002-2004**<br>bound|**200**<br>Exp|**4-2006**<br>ansion|**2008-2009**<br>Recession||**20**<br>Re|**10-2011**<br>bound|
|---|---|---|---|---|---|---|---|---|---|---|
||**Core**|**Peri**|**Core**|**Peri**|**Core**|**Peri**|**Core**|**Peri**|**Core**|**Peri**|
||||Urban<br>|Agglomera<br>|tions in t<br>|he Least D<br>|eveloped P<br>|rovinces<br>|||
|Median|-3.1|-6.7|6.3|13.5|0.0|4.8|-7.5|-7.8|18.0|21.5|
|Mean|-3.8|-10.5|10.7|16.2|-0.8|3.9|-7.7|-7.0|18.8|21.7|
||||Ur<br>|ban Agglom<br>|erations i<br>|n the Tra<br>|nsition Prov<br>|inces<br>|||
|Median|-3.7|-4.1|2.9|7.3|2.2|5.3|-1.4|-1.1|8.4|13.9|
|Mean|-2.5|-5.5|3.2|7.9|1.5|5.4|-1.1|-0.5|9.9|14.5|
||||Ur<br>|ban Agglom<br>|erations i<br>|n the Dev<br>|eloped Prov<br>|inces<br>|||
|Median|-2.1|-7.6|2.7|8.9|2.4|12.6|-1.1|-1.4|2.3|10.8|
|Mean|-2.4|-6.9|2.4|8.4|3.4|13.0|-1.8|-1.4|2.4|11.0|
|||||Urban Agg|lomerati|ons in all t|he Provinc|es|||
|Median|-3.3|-5.6|4.1|10.2|2.2|5.6|-1.7|-2.1|8.6|16.2|
|Mean|-3.0|-7.4|5.6|10.9|1.0|6.1|-3.3|-2.8|11.6|16.3|

Notes: The average growth rates are based on average DMSP values emitted by the core and periphery grid cells defined by the methodology described in section 3.2. See Figure 6 for a map showing the coreperiphery delineations across the country.

**Table 6: Absolute convergence in NTL across periods**

||**2000-2002**|**2002-2004**|**2004-200**|**6 2008-2009**|**2010-2011**|**2013-2017**|**2017-2019**|
|---|---|---|---|---|---|---|---|
||Recession|Rebound|Expansion|Recession|Rebound|Expansion|Recession|
|Ln NTL Base|||**Dep Var: Perc**|**ent Change i**|**n Period NTL**|||
|Year|2.00|-18.56*|-7.91|-0.44|-33.15***|-31.81***|-12.22***|
||[0.29]|[-1.99]|[-1.43]|[-0.05]|[-3.87]|[-3.18]|[-2.77]|
|Constant|-19.39|162.28**|67.74|0.31|291.20***|143.07***|56.29***|
||[-0.33]|[2.05]|[1.44]|[0.00]|[3.97]|[3.83]|[3.27]|
|Observations|44|44|44|44|44|44|44|
|R-squared|0.00|0.20|0.06|0.00|0.46|0.35|0.16|
|NTL Data|DMSP|DMSP|DMSP|DMSP|DMSP|VIIRS|VIIRS|

Notes: The average annual DMSP or VIIRS values are emitted by the core and periphery grid cells defined by the methodology described in section 3.2. See Figure 6 for a map showing the core-periphery delineations across the country. The change (dependent variable) is based on the value in the last year of a period and the value in the year before the period started (which is designated the base year). For example, for 2010-2011, the percentage change is between 2011 and 2009 and the base year is 2009.

25
**Table 7: Relative Changes in the Growth of Urban Peripheries across periods**

||**2000-2002**|**2002-2004**|**2004-2006**|**2008-2009**|**2010-2011**|**2013-2017**|**2017-2019**|
|---|---|---|---|---|---|---|---|
|VARIABLES|Recession|Rebound|Expansion|Recession|Rebound|Expansion|Recession|
|||Agglomeratio<br>|ns in the Least<br>|Developed Pr<br>Effects<br>|ovinces with P<br>|rovince Fixed<br>||
|Periphery|-4.61***|8.00***|4.60***|-0.44|4.79*|2.50|5.24**|
||[-3.00]|[6.68]|[3.56]|[-0.23]|[1.85]|[0.94]|[2.18]|
|Constant|-17.57***|16.15***|-3.63***|-18.11***|21.33***|37.23***|-7.91***|
||[-11.68]|[13.77]|[-2.86]|[-9.72]|[8.38]|[14.67]|[-3.44]|
|Observations|22,380|23,015|24,513|24,513|24,513|11,900|11,900|
|R-squared|0.20|0.11|0.12|0.07|0.09|0.07|0.14|
|||Agglomeration|s in the Transi|tion Provinces|with Provinc|e Fixed Effects||
|Periphery|-0.98|3.67***|6.21**|-3.03*|6.34***|9.59***|2.38**|
||[-1.38]|[3.86]|[2.41]|[-1.81]|[3.94]|[3.91]|[2.56]|
|Constant|-0.52|-2.79***|-3.35|10.93***|-6.11***|24.92***|11.02***|
||[-0.75]|[-3.02]|[-1.34]|[6.71]|[-3.91]|[10.86]|[12.68]|
|Observations|36,950|37,531|39,106|39,106|39,106|20,765|20,765|
|R-squared|009|011|011|006|014|006|005|
||.|.<br>Agglomeration|.<br>s in the Develo|.<br> ped Province|.<br>s with Provinc|.<br>e Fixed Effects|.|
|Periphery|-3.75*|7.23***|6.38|-0.41|7.64***|11.28***|2.38*|
||[-1.90]|[7.44]|[1.26]|[-0.24]|[6.40]|[12.48]|[1.91]|
|Constant|-8.11***|5.11***|-3.32***|-5.86***|11.05***|18.95***|11.73***|
||[-1.80e+12]|[4.11e+11]|[-3.66e+11]|[-1.0e+12]|[2.71e+12]|[1.49e+13]|[1.05e+13]|
|Observations|26,493|26,845|27,423|27,423|27,423|16,880|16,880|
|R-squared|0.05|0.06|0.13|0.03|0.09|0.03|0.03|
|||All A|gglomeration|Areas with Pro|vince Fixed E|ffects||
|Periphery|-3.11***|6.44***|5.95**|-1.24|7.03***|9.72***|2.83***|
||[-2.97]|[7.46]|[2.14]|[-1.18]|[8.49]|[8.15]|[3.49]|
|Constant|-8.11***|5.11***|-3.32***|-5.86***|11.05***|18.95***|11.73***|
||[-6.40e+11]|[5.90e+11]|[-1.41e+11]|[-3.30e+11]|[4.83e+11]|[1.47e+12]|[3.15e+12]|
|Observations|85,823|87,391|91,042|91,042|91,042|49,545|49,545|
|R-squared|0.11|0.12|0.11|0.06|0.14|0.09|0.08|
|NTL Data|DMSP|DMSP|DMSP|DMSP|DMSP|VIIRS|VIIRS|

Notes: The average values are based on average DMSP/ VIIRS values emitted by the core and periphery grid cells defined by the methodology described in section 3.2. See Figure 6 for a map showing the coreperiphery delineations across the country. The growth rates are computed by comparing the value in the final year of a period with the value in the year before the period began. For example, the growth during the 2010-2011 rebound is computed as the difference (with both variables in natural logs) of NTL in 2011 and NTL in 2009.

26