metadata: authors: 0: author: Grover,Arti Goswami
1: author: Lall,Somik V.
count: World
docna: 0: docna: Does Participation in Global Value Chains
            Reduce Spatial Inequalities within Countries ?
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Common Carriers Industry,Food & Beverage Industry,Business Cycles and Stabilization Policies,Pulp & Paper Industry,Plastics & Rubber Industry,Construction Industry,General Manufacturing,Textiles, Apparel & Leather Industry,Industrial and Consumer Services and Products,Transport and Trade Logistics,Transport Services
teratopic: Macroeconomics and Economic Growth,Industry,International Economics and Trade,Transport
abstracts: cdata!: This paper examines whether a country’s
            participation in global value chains supports spatial
            convergence in the domestic economy. In theory, production
            disintegration through “unbundling” makes industrial
            development less lumpy, providing opportunities for smaller
            cities to plug and play in niche spaces while not having to
            fight the agglomeration economies offered by large
            metropolitan areas. Using data on the size distribution of
            cities within countries and the Organisation for Economic
            Co-operation and Development’s Trade in Value Added
            database, the paper finds that integration in global value
            chains is strongly associated with greater concentration in
            large urban agglomerations, not less. A unit standard
            deviation increase in domestic value added in exports of
            intermediate products is associated with a decline of 0.1
            standard deviation in the Zipf coefficient, an index
            measuring spatial dispersion. Spatial concentration is
            strongest for global value chains involving
            knowledge-intensive business services and high-technology manufacturing.
repnme: repnme: Does Participation in Global Value Chains
            Reduce Spatial Inequalities within Countries ?
display_title: Does Participation in Global Value
            Chains Reduce Spatial Inequalities within Countries
listing_relative_url: /research/2021/04/32986587/participation-global-value-chains-reduce-spatial-inequalities-within-countries
topicv3: Industrial Clusters and Value Chains
            m1326062 1226,Global Value Chains(GVC) m1326472 767,Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698
publishtoextweb_dt: 2021-04-09T00:00:00Z
dois: 10.1596/1813-9450-9619
historic_topic: Macroeconomics and Economic Growth,Industry,International Economics and Trade,Transport
origu: URL Global Director (SURDR)
no_of_pages: 24
author_keys: 0: author_key: 1066720
1: author_key: 124534
model_extractions: Policy Research Working Paper 9619 Does Participation in Global Value Chains Reduce Spatial Inequalities within Countries?

_Arti Grover Somik V. Lall_ Urban, Disaster Risk Management, Resilience and Land Global Practice & Finance, Competitiveness and Innovation Global Practice April 2021
Policy Research Working Paper 9619

# **Abstract**

This paper examines whether a country’s participation in global value chains supports spatial convergence in the domestic economy. In theory, production disintegration through “unbundling” makes industrial development less lumpy, providing opportunities for smaller cities to plug and play in niche spaces while not having to fight the agglomeration economies offered by large metropolitan areas. Using data on the size distribution of cities within countries and the Organisation for Economic Co-operation and Development’s Trade in Value Added database, the paper finds that integration in global value chains is strongly associated with greater concentration in large urban agglomerations, not less. A unit standard deviation increase in domestic value added in exports of intermediate products is associated with a decline of 0.1 standard deviation in the Zipf coefficient, an index measuring spatial dispersion. Spatial concentration is strongest for global value chains involving knowledge-intensive business services and high-technology manufacturing.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice and the Finance, Competitiveness and Innovation 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 agrover1@worldbank.org and slall1@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
# **Does Participation in Global Value Chains Reduce Spatial Inequalities within Countries?**

Arti Grover and Somik V. Lall World Bank JEL Classification: F6; O18; R12 Keywords: Zipf’s law; City size distribution; Urban system; Global value chains; Offshoring

**Acknowledgments:** The authors thank the UK FCDO for generous financial assistance through the World Bank MDTF on sustainable urbanization. They also thank William Maloney, Daria Taglioni, Gilles Duranton and Vernon Henderson for helpful discussions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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.
# **1. Introduction**

Economic activity is highly concentrated. Coastal areas have always been favored for regional and international commerce, and the centripetal forces of agglomeration economies drive a virtuous cycle of economic concentration and higher productivity (Duranton and Puga 2020). Economic activity increases with population density. Even in the poorest countries, industry and services tend to be concentrated in dense metropolitan areas and productivity rises with the density of economic activity. Grover, Lall, and Maloney (2021) show that night light intensity, a measure of economic activity, is on average 2.5 times higher in cities of more than 5 million people compared to cities with populations between 500,000 and 1 million.

Along with high spatial inequalities in living standards and slugging domestic labor mobility, the spatial concentration of economic activity has raised increasing concern among policy makers about “places left behind” (Grover, Lall and Maloney 2021; Rodriguez-Pose 2018; World Bank 2008). In this context, policy makers may find global value chain (GVC) integration appealing as production disintegration through “unbundling” of tasks and products makes industrial development less lumpy (Baldwin 2011a), providing opportunities for smaller cities to plug and play in niche spaces while not having to fight agglomeration economies offered by large metropolitan areas.

Some studies suggest that cross-country cooperation in GVCs could provide opportunities for secondary cities and help mitigate spatial inequalities. For example, cooperation between Singapore and secondary cities in Malaysia (Johor Baru) and Indonesia (Bintan Batam) have shown that collaboration on integrated supply chains in manufacturing, where each city is assigned a specialized role in production, could boost the global competitive advantage of secondary cities (Toh Mun 2006).<sup>1</sup> In this paper, we examine the impact of a country’s participation in GVCs on spatial inequality, measured by the domestic size distribution of cities. For example, the assembly of parts through the maquiladora program boosted Mexico’s participation in global production networks that accounted for half of Mexico's exports in 2006; yet most exporting firms remain clustered around the US-Mexico border (Mia and Austin, 2008; Hanson, 1996). Vietnam’s participation in GVCs is driven by firms located around Ho Chi Minh City that account for nearly 83 percent of apparel and textile exports. Even after several decades following its establishment, these activities of global production networks are confined to the city and its contiguous agglomerations (Pham et al., 2013). Further, clustering is more likely when production on integrated networks is intensive in technology, knowledge and skills -- if a country plugs into the higher end of the value chain, agglomeration forces are likely stronger.<sup>2</sup> We organize our analysis in two stages. We first estimate a measure of city size distribution for each country and year using demographic data on urban agglomerations. By regressing the log rank of each urban agglomeration in a given country on the log of the agglomeration’s population size, we estimate the country’s _Zipf_ coefficient, a parameter generally used as a summary statistic of city size distribution. Next, we examine the determinants of city size distributions, with participation in GVCs being the focus of our analysis.

> 1 The three city triangle is known for GVCs related to production of chemicals and the manufacture of technology-based equipment. 2 Duranton and Puga (2001) and Desmet and Rossi-Hansberg (2009) argue that knowledge spillovers in new industries (such as business services) motivates clustering  in city centers, while more mature industries such as standardized or low-tech manufacturing tends to relocate to less expensive places (e.g. urban periphery or secondary cities).

2
GVC participation data are from the OECD’s Trade in Value Added database (TiVA). We find that a unit standard deviation (SD) increase in domestic value added (DVA) in exports of intermediate products (an index of participation in GVCs) is associated with a 0.1 SD decline in the _Zipf_ coefficient. Spatial concentration is strongly and significantly correlated with GVCs involving knowledge-intensive business services and high-tech manufacturing. Even with low-tech manufacturing, the correlation with spatial dispersion is negative, albeit not statistically significant, implying that participation in GVCs does not help with the spatial dispersion of economic activity. The findings on the service sector are particularly important for public policy given their rising shares in GVCs.<sup>3</sup> These findings are consistent with alternative measures of participation in GVCs (DVA in foreign exports; ratio of forward to backward linkages) and spatial concentration (e.g. inverse of the Gini coefficient).

An important econometric challenge is to address the potential endogeneity of GVCs with the urban size distribution. The point here is that changes to the urban system come from unobserved sources which are associated with country level decisions to integrate in global supply chains. Consider “infrastructure readiness”, which is a key driver of participation in GVCs. In China, the government pumped four trillion yuan (about US$ 520 billion) in 2008 towards promoting the development of inland regions through rural infrastructure projects including construction of roads, railroads and airports, as well as the supply of affordable housing (WTO, 2011). Such investments that deeply alter the spatial allocation of domestic economic activity also make global production more attractive in these locations, suggesting the possibility of reverse causality. Using real effective exchange rates and net FDI inflows as instruments to address the endogeneity between GVCs and the urban size distribution, our results confirm the strong and positive relationship between spatial concentration and GVC participation.

Our findings are consistent with an emerging body of evidence showing that economic integration across borders is associated with greater spatial concentration within national borders (Dagupta and Grover, 2021; Redding and Fagjelbaum, 2018; Cosar and Fagjelbaum, 2016). As the nature of linkages are upgraded from assembly tasks to adding greater value in manufacturing and services, spatial concentration is likely to rise. Policy makers may well want to reorient their efforts to enabling a lot more people with the necessary skills needed to move and find opportunities in urban agglomerations; the cities themselves may need more attention to relieve congestion in land, housing and commuting.

The rest of this paper is organized as follows. We discuss the relevant literature on trade and cities in section 2. Section 3 describes the empirical strategy and Section 4 provides the main empirical findings. Section 5 concludes.

# **2. Cities, GVCs and trade in tasks**

The focus of our investigation is on global value chains and its implications for the system of cities/ urban system in countries. The neoclassical urban systems theory introduces agglomeration as an outcome of the tension between the centripetal force arising from pure external economies and the centrifugal force arising from urban congestion costs (e.g. Henderson, 1974, 1977, and 1988). Because spillovers are assumed to be industry specific, the neoclassical theory predicts the emergence of specialized cities. This approach explains the existence of an urban system with different city sizes because the relative strength of external economies is presumably stronger in some industries vis-à-vis others.

The neoclassical urban systems theory is, however, aspatial and does not provide information on the location of cities. This shortcoming is addressed by the “New Economic Geography” (NEG) framework (Krugman, 1991a, b, 1993; Fujita et al. 1999). The NEG literature opened up the black box of agglomeration economies by deriving these externalities from an interaction of economies of scale at the plant level with

> 3 Over 40% of the world’s cross-border trade in value added terms comes from services. In some countries such as Singapore, Iceland, India and the EU27, the services content of exports is above 50% (Lanz and Maurer, 2015).

3
transportation costs and factor mobility. An interaction of demand for product variety and scale economies in their production motivate agglomeration, while the demand for consumption goods produced by dispersed agricultural populations induces the dispersion of industries. The tension between these forces produces a hierarchical system of cities where the city at the top produces a broad range of urban products and lower order cities successively fewer products.

A further body of literature on city size distribution models urban system formation as a random process. Starting with Zipf (1949) this approach has hypothesized a smooth mathematical relationship between the rank of a city in the urban hierarchy and the size of the city. A number of studies have found evidence in support of such a regularity in city-size distribution for many countries (e.g. Gabaix, 1999; Ioannides and Overman,2003), while others have explored the improved fits to city size distribution beyond Zipf’s original Pareto distribution function (Giesen et al., 2010). Finally, there is ample evidence to support systematic departures from this law, especially pointing that the law, at best, holds only in the tail (e.g. Rozenfeld et al., 2011; Ioannides and Skouras, 2013) or adopts a different functional form (e.g. Nitsch, 2005; Eeckhout, 2004).<sup>4</sup> Recent research explores how, rather than seeing these approaches as in competition, the NEG literature may help explain the statistical relationships found by the likes of Zipf. The NEG literature has attempted to explain variations in the spatial distribution through several factors. For instance, the impact of increasing returns in encouraging greater agglomeration constitutes the so-called ‘folk theorem of spatial economics’ since, by definition, they stimulate economic production to cluster in space. Thus, larger economies of scale are associated with a more uneven distribution of city sizes. The impact of trade and transport cost is predictably ambiguous. On the one hand, a decline in these costs encourages dispersion because the benefits from clustering are lower (e.g. Baldwin, 2011b; Krugman, 1996). However, if industries are knowledge or interaction intensive it encourages greater clustering (Michaels et al. 2013; Fujita et al., 1999). Finally the share of manufacturing also plays a role in explaining the city size distribution. A smaller share of manufacturing induces a more even urban structure as the desire to serve the agricultural periphery, or economize on congestion costs induces firms to locate away from existing cities.

Recent research has attempted to use the NEG models to explain variations in the Zipf’s exponent in a cross-country framework. Cross-country variation in the Zipf’s coefficient is associated with per capita GDP, total population and railroad density (Rosen and Resnick, 1980; Mills and Becker, 1986), government share of GDP, the share of manufacturing value added in GDP (Alperovich, 1993), institutions and the degree of democratization (Henderson and Wang, 2007). Other studies have explained the variation in Zipf’s coefficient via recent changes in technology and use of internet or ICT (Ioannides et al., 2008).

We focus on examining the size distribution of cities through the lens of GVC participation. Although trade in intermediates is not a new phenomenon, there are no studies linking globalized production and the urban system. Trade in parts and components has been long recognized in empirical as well as theoretical work. Since the nature, extent and complexity of unbundling of production have evolved over the last few decades, it has been recognized with a different terminology each time. In the 1980s, the phenomenon was called “trade in middle products” (Sanyal and Jones, 1982) while in the 1990s the geographic separation of production was referred to as “fragmentation” (Deardorff, 1998; Venables, 1999) or “production sharing” (Yeats, 1998). In the 2000s it was called “vertical specialization” (Hummels et al., 2001) or “offshoring” and “trade in tasks” (Grossman and Rossi-Hansberg 2008), while in the most recent decade it is termed “trade in value added” or “global value chains” (Baldwin, 2011a, 2011b).

> 4 Zipf’s law is usually referred to as the rank-size rule because the Pareto relationship with parameter 1 is approximated by a regression of city rank on its size (Gabaix and Ioannides, 2004). However, the rank-size rule approximates Zipf’s law well only when large cities are taken into account, but not the smallest ones. A related concept to Zipf’s law is Gibrat’s law or the proportionate growth process, which postulates that urban growth is independent of city size (Dobkins and Ioannides, 2000; Gabaix and Ioannides, 2004). There is mixed evidence in support of Gibrat’s law as well (Black and Henderson, 2003; Córdoba, 2008).

4
Developing countries that hope to transform their economies structurally by anchoring themselves to GVCs are often disappointed by the quality of jobs offshored. The so-called ‘smile curve’ logic asserts that developing countries are mainly being offshored low value added per worker jobs while those with high intellectual and value added content are retained in the industrialized and advanced countries.<sup>5</sup> Thus, there is a rising concern among developing nations that not only are the ‘good jobs’ retained in advanced economies while the ‘bad jobs’ are shuffled to developing countries, but also that this trend is deepening (Baldwin et al., 2014). For example, in the well documented case study for Apple iPod, of a total value of USD 150, only USD 4 can be attributed to local producers in China while the rest of the value accrues to the United States, Japan and the Republic of Korea (Dedrick et al. 2010). Recent work using the OECD Trade in Value Added (TiVA) database suggests that 67% of VA trade accrues to OECD countries while other countries contribute minimally (11% Asian newly industrialized countries such as Singapore; Hong Kong SAR, China; Taiwan, China; Korea; Malaysia; Thailand; the Philippines; 9% for China; 5% for the remaining BRICS countries and only 8% for all other developing economies covered by the database) (e.g. Banga, 2013).

Baldwin (2012) summarizes the location decision of firms in a supply chain context as a trade-off between dispersion (e.g. direct factor costs such as wages) and agglomeration forces (e.g. ‘separation’ costs such as transmission and transport costs). Clustering is a natural outcome in certain sectors and stages such as fashion clothing that involves local or technological spillovers while in others where such benefits are limited, a geographical separation of stages is observed. Dispersion is generally explained by the relatively higher wages of low-skill labor in the ‘headquarter’ economies (e.g. the United States, Germany, Japan) vis-à-vis nearby ‘factory’ economies, which results in a spatial sorting of skill-intensive stages to highwage nations and labor-intensive stages to low-wage nations (Feenstra and Hanson, 1996). Given the skewed distribution of global trade in value added, there are obvious concerns regarding their impact on regional development. The next section sets the stage for examining this more formally.

# **3. Empirical  Strategy**

To assess the impact of participation in GVCs on the city size distribution, we organize our estimation in two stages. In the first stage, we estimate the Zipf’s coefficient, through the following well-known ranksize equation:<sup>6</sup> l l (1) ii,cc,tt cc,tt cc,tt ii,cc,tt ii,cc,tt Where RR = AA is the rank of city −γγ PP + εε _i_ in country _c_ at time _t_ and is the population of the given city _i_ in country _c_ at time _t_ and is a country-year specific intercept term. In sum, an estimate of the Zipf’s coefficient, ii,cc,tt ii,cc,tt , is generated by running a regression of log rank on log city size. The assumption of Pareto distribution RR PP cc,tt is useful in assuring that the Zipf’s coefficient is consistent even when estimated by running a regression AA cc,tt only on the sample of the larger cities, that is, the upper tail of the distribution. γγ In the second stage, we follow Rosen and Resnick (1980), Soo (2005) and Ioannides et al. (2008) in delineating the determinants of city size distribution as measured by the Zipf’s coefficient, : cc,tt γγ (2) where cc,tt cc,tt is a vector of time and/or country specific fixed effect, as appropriate. Time fixed effects capture cc,tt cc,tt γγ = φφ + ββ l XX + ee the linear time trend in city size distribution that affects all countries and may have nothing to do with any cc,tt φφ

> 5 “Smile curve” was introduced by Acer founder and CEO Stan Shih in the early 1990s. There are only a few studies on the smile curve in the economics literature (e.g. Mudambi 2008; Baldwin et al., 2014), although the concept is widely used in the International Business literature (e.g. Yan and Islam, 2011).

> 6 We follow Soo (2005) and Ioannides et al. (2008) in assuming that the city size distribution is Pareto.

5
specific country trait. Country fixed effects capture the changes in city size distribution due to factors that are invariant over time (e.g. geographical location) and affect a given country in all years. is a vector of country specific characteristics at time _t_ when the Zipf’s coefficient is measured while the vector of cc,tt parameters measures the effect of changes in these variables on city size distribution. XX **_Defining a city_** ββ

# **_Defining a city_**

Gabaix and Ioannides (2004) highlight that there is no universally accepted definition of a city for statistical purposes. Differences in definition of cities and the available data on such arbitrarily and non-uniformly demarcated administrative boundaries worldwide may make international comparisons tricky. The definition of cities would be an issue even if official statistics are reliable because it is based on the statistical authorities’ definition of city boundaries (Soo, 2005). First, statistical authorities’ definition of a city may not necessarily be an economically meaningful definition of ‘city’ (e.g. Rosen and Resnick, 1980). Second, the definition of a city adopted by these authorities across a set of countries is also not uniform, so we cannot really expect a city in, say, Africa, to be similar to the definition of a city in Europe. We use WorldPop’s definition of urban agglomeration in our work to map out spatially concentrated regions.

# **_Estimating size distribution_**

Gabaix and Ioannides (2004) explain some of the pitfalls of measuring city size distribution using the Zipf’s coefficient. The most important challenge is that this rank-size regression is only an approximation and it is reasonably accurate in estimating the Zipf’s coefficient in large samples only, but not recommended for cities, as it is inefficient and biased in small samples. To counter this, we support our results with the following estimates of size distribution of urban agglomerations in robustness checks.

- (i) Some studies prefer using a Gini coefficient for measuring spatial inequality (e.g. Henderson and Wang, 2007) instead of the Zipf’s coefficient. Inspired by Duranton (2007), who suggests significant deviations of actual distribution of city size from the Zipf’s Law, this preference for Gini coefficients is clearly due to the flexibility it allows with respect to the specific size distribution. The Gini coefficient is the mean absolute deviation among all pairs of cities, relative to the mean city size. To check for the robustness of our results on the impact of participation in GVCs on spatial concentration, we use the Jasso-Deaton formulation for calculating the Gini coefficient: 2

- G =<sup>llcc,tt+ 1</sup> − nncc,tt −1 −1�

- cc,tt ii,cc,tt ii,cc,tt

- llGG cc,tt cc,tt cc,tt �RR PP

- where is the sample of cities in country ll ll _c_ and time  �ll _t_ . ii=1 cc,tt

- (ii) Gabaix and Ioannides (2004) recommend using a Hill estimator (Hill, 1975). For a sample of ll

- (ii) cities in country _c_ and time _t_ , with sizes 1 2 the Hill (maximum

- likelihood) estimator under the null of perfect power law is given as: cc,tt nn

- ll PP ≥PP ≥⋯⋯⋯≥PP −1

- = <u>,</u> ∑ l −l cc tt

- ll

- cc,tt nn

- Dobkins and Ioannides (2000) and Soo (2005) use the Hill estimator for estimating Zipf’s Law. γγ� ii=1 PPii,cc,tt PPnn,cc,tt

Both these studies find that the Hill estimate of the Pareto exponent is always smaller than the OLS estimate. Black and Henderson (2003) reconcile the apparent appropriateness of the Hill estimator by suggesting that the reliability of this estimate is dependent on the curvature of the log rank–log population relationship. Soo (2005) deals with a comparison of the Hill and the 6
OLS estimate of the Zipf’s coefficient in linear and quadratic formulation in much greater detail, we simply use the Hill estimator to check for the robustness of our results.

# **_Measuring participation and the knowledge content of GVCs_**

Besides domestic content, the value added in gross exports of a country can be decomposed into the following three categories (Koopman et al., 2010):

- (i) Value added exported in intermediates directly to the importers

- (ii) Value added exported in intermediates re-exported to third countries, and,

- (iii) Valued added exported in intermediates that are re-imported home.

Each of these categories represents a measure for capturing a country’s participation in GVCs. The OECD TiVA database offers several indices for measuring participation in GVCs. Some examples include the following measures (as a percent of gross exports): (i) DVA in exports of intermediate products, (ii) DVA embodied in foreign exports, and (iii) re-imported DVA. These indices are defined in Annex table A.1.

Participation in GVCs is, however, not an indicator of the knowledge content of participation. We rely on sectoral measures to measure knowledge content. Within manufacturing, we follow Banga (2013) in defining the high-tech manufacturing to include the following sectors: Electrical and optical equipment; Machinery and equipment, nec; Transport equipment; and Chemicals and non-metallic mineral products. Low-tech manufacturing includes the following sectors: Textiles, textile products, leather and footwear; Food products, beverages and tobacco; Agriculture, hunting, forestry and fishing; Mining and quarrying; and Manufacturing nec; recycling. For services, we use the extent of forward participation in GVCs in the services sector, in general, and in highly knowledge intensive R&D and business services, in particular, to draw comparison within and across the sectors.

# **_Endogeneity of GVCs with city size distribution_**

We are interested in capturing the _causal_ effect of GVCs on city size distribution -- however, it is possible that changes to the urban system come from some other unobserved sources and countries respond to this by integrating their economies in global supply chains. For instance, analysis of firms in the Italian automotive industry (Bacchiocchi and Florio 2012) shows that being located in a high-density industrial district and producing more research-intensive automotive-specific goods increases the probability of internationalization of supplier firms. Thus, we expect that some exogenous factors can drive spatial dynamics of cities within a country which in turn affect the participation in GVCs, making the latter an endogenous determinant.

Similarly, location of firms in urban clusters improves their probability of participating in GVCs and perhaps also impacts the knowledge content of their participation. If this is so, our regression of city size distribution on variables measuring the participation and knowledge content of GVCs will capture the association between GVCs and urban system that includes not only the direct causal effect (from GVCs to urban system) but also any indirect feedback effect (from urban system to GVCs). In the worst case where participation in GVCs does not impact the urban system at all, our estimations could pick the reverse feedback effect from the urban system.

To address endogeneity of GVCs with urban structure, we adopt an instrumental variables approach. Certain factors affect the general environment for trade but are less likely to be spatially consequential. For instance, the real effective exchange rate (REER), defined as the nominal exchange rate deflated by an index of price or index of costs, takes account of price level differences between trading partners and 7
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countries for which we have city data, do we find the value of Zipf’s coefficient to be less than 1. This implies that cities in our sample are more evenly distributed relative to predictions by the rank-size rule. For urban agglomerations however, we find that for 20 of the 39 countries which report their populations in urban agglomerations, we observe a Zipf’s coefficient lower than 1, implying that urban agglomerations present greater concentration in economic activity of a country relative to that predicted by the Zipf’s law.

# _Do urban agglomerations represent higher spatial concentration?_

In the last few years, administratively defined cities have become less meaningful as most of the growth is occurring through expanding suburbs, especially in developed countries. Table A.3 presents a comparison of the estimated Zipf’s coefficient from regression in equation (3) for a subset of countries across administratively defined cities with that in their urban agglomerations. Gabaix and Ioannides (2004) note that the Zipf’s coefficient for administratively defined city is higher than urban agglomerations. This point was made first by Rosen and Resnick (1980) and has been revisited by Soo (2005) who finds that the mean value of the Pareto exponent is lower for agglomerations than for cities. Our results suggest that this pattern continues to hold for most countries in our sample. Since urban agglomerations tend to be larger relative to the core city, they tend to show greater statistical dispersion in size distribution, that is, in economic terms have a greater concentration in economic activity.

# _Participation in GVCs and Size Distribution: Some Motivating Correlations_

Although we use four indicators to measure participation in GVCs, here we show an association between participation in GVCs and spatial dispersion using the “Re-imported domestic value added (DVA)”. Figures 2a and 2b show an added variable (AV) plot of this index with the city and UA level Zipf’s coefficient, respectively, after controlling for population density, GDP per capita (linear and square terms) and an indicator for membership in the OECD group.

Figure 2a suggests that higher participation in GVCs is correlated with higher spatial dispersion across cities. However, in the corresponding plot for urban agglomerations shown in figure 2b, we see a reverse pattern. A negative slope of this plot implies that an enhanced engagement in GVCs, is associated with higher spatial concentration. The two figures together imply that participation in GVCs is associated with a de-concentration of population from the city core to urban periphery, but not greater decentralization to places far from agglomerations.

Does a higher knowledge content GVCs participation intensify spatial concentration? Figure 2c presents the AV plot between Re-imported DVA in knowledge intensive R&D services and spatial dispersion of urban agglomerations. Figure 2c shows that the magnitude of this negative relationship is larger when participation in GVCs is based on knowledge intensive sectors.

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<!-- Start of picture text --> + * BEL2010 x FEKBO0RTUN2011 FEKBO0RTUN2011TUN2011 o.. *MLT2005** CRI2011<br>SNiNsn©NiNsn©Nsn©sn©© AU!* AUS19954AB%2000* JPN1995x* AUS19954AB%2000* JPN1995x AUS19954AB%2000* JPN1995x4AB%2000* JPN1995xB%2000* JPN1995x* JPN1995x JPN1995xx p2011 xrxfpsypryz005xfpsypryz005fpsypryz005ypryz005 x CHN2010 SN+2N+22 - x MLT2019 PRT2000vee%vee%% MEX2010<br>3 x AUS2000SMO AUS2000SMOSMO AOD x *rusoanoRUS2000rusoanoRUS2000RUS2000 _xuSAAT== 3EjEj *PO12000, cweapso44<br>xt * CHE2010 CHE2010 xiRNRG haga, ~ SWE1995 SWE1995 <- * CRI2000 x SWEZ005 SWEZ005<br>P= x PRC pao PRC pao pao uae * CAN1995 x DEU211 DEU211 s x LUKte0x2000 LUKte0x2000 x WIESE WIESE<br>Bo * HUN HUN Bese XCAN2008 x wrygono9 wrygono9 7) SL2010% ay 2099 2099 RUSE sanytggyz000 XUSA2010<br>oOa2a22 x SeersceapaantyCYP AVEOMD.e200Brena9000nuaeategoossceapaantyCYP AVEOMD.e200Brena9000nuaeategoostyCYP AVEOMD.e200Brena9000nuaeategoosCYP AVEOMD.e200Brena9000nuaeategoos AVEOMD.e200Brena9000nuaeategoose200Brena9000nuaeategoos200Brena9000nuaeategoosnuaeategoos —* CAN2O11* CAN2000 CAN2O11* CAN2000* CAN2000 CAN2000 fa@29299 EER** CHLsesarqengons20sesarqengons2020 orTeaaa emrteteoenmeSTeaaa emrteteoenmeSaaa emrteteoenmeS emrteteoenmeSeS CoRROREsnoOREsnosno faFOOnooFOOnoonoo *USA2000<br>i TN%% KOR2000 x MERZ MERZ aNSS $< TRL1995 * KOR2010 * DEUOTT DEUOTT<br>SN=&S=&S&SS “Sikx HKG201%ix HKG201%i HKG201%i KHMSUOb KrzodssTN% foooOaNSoOaNS *CHEDORpOGO«SBI$<«SBI$<9TRL1995<br>a ISL20868T2000 ISL20868T2000 7 = 7 « 1DNAGPRZI KOREA?R2005R2005 |<br>- x EST2011 EST2011 KOR2010 r Pert teas<br>. * 1SL2010 x HKG2000 x SGP2000<br>a2 0 -.2 0 2 4 6 8<br>coef== .38516546, (robust) seRei=‘e-importe.10309366,PRei=‘e-importe.10309366,P=‘e-importe.10309366,P‘e-importe.10309366,P.10309366,PP wh DVAt= DVAt=t= 3.74inin inAggregAggregateAggregAggregateAAAggregateAti & 8 coef= -.21933236,= -.21933236, -.21933236, (robust) seRe-imported = 09617893, DVAt = -2.28in Aggregate seRe-imported = 09617893, DVAt = -2.28in AggregateRe-imported = 09617893, DVAt = -2.28in Aggregate = 09617893, DVAt = -2.28in Aggregate 09617893, DVAt = -2.28in Aggregate DVAt = -2.28in Aggregatet = -2.28in Aggregate = -2.28in Aggregate -2.28in Aggregatein Aggregate Aggregate<br>*cRI2011<br>© x MLT2005<br>e x MLT1995 * BEL2000<br>oeNwNw MLT2011 % PRT2000 x BEL2011<br>mo) x MEX2010 MEX2010<br>2ro}ro} * POL2000 SWEPAB011 SWEPAB011<br>By x BEAIBRRIEOT<br>2% = CREO CREO EMER SWE2000<br>Ss x LU%4g442000 * MEX2000 MEX2000 * SWE1995 SWE1995<br>a7)[2[2 > 1SL2010 1SL2010 TTS 41.2000,EoEo as 20 x MEX1995*CANZO11*CANZO11 meses * USA2010*CAN2000*CAN2000<br>go xtelait Sage Sage aa ees<br>2 SAREsagzampssagzamps GRGD E Y ET AERAOOOOOO PRMDhaoneALDITehaoneALDITeALDITeTe aadipereRatHoraoesRatHoraoes _ * USA2000<br>=EaEa x cHBQeEP2010% NZL199510% NZL1995 NZL1995 i<br>aly nesta 1995 *IND2014 * DEU2011 DEU2011<br>a * CHE2000 CHE2000 vpuagift20TM *KOR2010<br>fs *<1DN2010 x CHN CHN<br>Y * wee% KOR2000% KOR2000 KOR2000 x 1KGR2099KGR2099<br>* SGP2000<br>-1 -.05 | 0 05 A 15<br>Re-imported DVA in R&D Services<br>coef = = -1.0724309, (robust) se = .44720585, t = -2.4<br><!-- End of picture text -->
Table 1: Descriptive Statistics

|Variable<br>|Mean Sta|ndard Deviation|
|---|---|---|
|SpatialDispersion|||
|Zipf's Coefficient (absolutevalue} city|1.10|0.215|
|Zipf's Coefficient (absolute value) -urban agglomerations|1.03|0.325|
|GICorrected Zipf's Coefficient (absolute value)- city|1.17|0.211|
|GICorrected Zipf's Coefficient (absolute value) -urban|||
|agglomerations|1.11|0.361|
|Gini Coefficient (Inverse)|1.83|0.753|
|Coefficient ofVariation (Inverse)|0.62|0.520|
|Hill's Estimator|0.86|0.349|
|Participation in GVCsandknowledge co|ntent||
|Re-importedDVA_ % ofgross exports|0.16|0.186|
|Re-imported DVA, % ofgross exports - Aggregate Services|0.07|0.074|
|Re-imported DVA, % ofgross exports -Other Business Services|0.05|0.053|
|Re-imported DV A, % ofgross exports-R&D Services|0.06|0.054|
|Re-importedDV<br>A, % ofgross exports-high-tech Manufacturing|0.23|0.287|
|Re-importedDVA, % ofgross exports -low-tech Manufacturing|0.12|0.134|
|DVA in exports ofintermediate products, %gross exports|444|12.6|
|DVA in exports ofintermediate products, % gross exports -|||
|AggregateServices|44.5|8.5|
|<br>DVA in exports ofintermediate products, % gross exports -|||
|OtherBusinessServices|64.5|10.1|
|<br>DVA in exports ofintermediate products, % gross exports -|||
|R&D Services|79.2|78|
|DVA in exports ofintermediate products, % gross exports - High-<br>tech Manufacturing|37.3|92|
|DVA in exports ofintermediate products, % gross exports - Low-|||
|tech Manufacturing|38.7|19.2|
|DVA embodied in foreign exports, % gross exports|20.4|6.407|
|Ratioofforwardtobackwardinternationallinkages|1.19|1.564|

Notes: The table presents descriptive statistics for indices measuring spatial dispersion and participation in GVCs for all the sample of countries and years included in our work.
Table 2: Participation in GVCs (°%o gross exports) and Size Distribution of Urban Agglomerations

|DV: SpatialDispersion (Absolute<br>valueofZipf’scoefficient) in|Re-tmportedDVA|DVA ininterm.<br>=Exports|DVA embodied in<br>foreign exports|Ratio offorwardto<br>backward linkages|
|---|---|---|---|---|
|Aggregate|QQ)|(2)|G)|(4)|
|Participation inGVCs|-0.033++|0.09344|-0.087+-4+|-0.165+4+|
||(0.016)|(0.033)|(0.027)|(0.051)|
|R-squared|0.606|0.629|0.641|0.626|
|AdjustedR-squared|0.545|0.572|0.586|0.569|

Notes: The table shows the impact of participation in GVCs on size distribution of urban agglomerations in a panel of countries from 1995, 2000, 2005, 2008-2011. The base model corresponds to 91 country-year patrs for 37 countries. All explanatory variables are in unit standard deviation format. The measure of spatial dispersion, that is, the absolute value of Zipf coefficient, is winsorized at the bottom 2% and top 98% percentile level. All estimations include an year fixed effect and control for the following country characteristics GDP per capita, squared GDP per capita, population density, an indicator of whether the country is in the OECD group. and Government expenditure (% GDP). Estimations report standard errors in parantheses. +, ++, and +++ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Table 3: Knowledge content of GVCs and Size Distribution of Urban Agglomerations

|DV: Spatial Dispersion (Absolute<br>valueofZipf's coefficient)|(1)|Q)|(3)|(4)|
|---|---|---|---|---|
||Panel a:DV<br>Aggregate|A in exports o<br>Aggregate<br>Services|fintermediates (<br>High-tech<br>Manufacturing|% gross exports)<br>Low-tech<br>Manufacturing|
|Participation inGVCs|-0.093--+<br>(0.033)|0.077+<br>(0.024)|-0.101--+<br>(0.029)|-0.046<br>(0.029)|
|R-squared|0.629|0.633|0.655|0.605|
|AdjustedR-squared|0.572|0.577|0.602|0.545|

Panel b: Re-imported DVA (% gross exports)

||Aggregate|Ageregate<br>Services|OtherBusiness<br>services|R&D services|
|---|---|---|---|---|
|Participation inGVCs|-0.033++|-0.042++|-0.052++|-0.053++|
||(0.016)|(0.020)|(0.024)|(0.022)|
|R-squared|0.606|0.608|0.633|0.621|
|AdjustedR-squared|0.545|0.548|0.4577|0.563|

Notes: See Table 2. The table measures knowledge content of participation n GVCs across aggregate products and services, aggregate services, high-tech manufacturing and low-tech manufacturing services. Estimations report standard errors in parantheses. +, ++, and +++ denote statistical significance at the 10%, 3%, and 1% levels, respectively.
Table 4: Participation in GVCs and Size Distribution of Urban Agglomerations (IV

|E|stimations)||||
|---|---|---|---|---|
|DV: SpatialDispersion (Absolute<br>valueofZipf's coefficient) in<br>|Re-impor<br>|tedDVA|DVA inE<br>interm<br>|xports of<br>ediates<br>|
|Aggregate|rr<br>(1)|(2)|r<br>(3)|r,<br>(4)|
|Participation inGVCs|-0.054-+<br>(0.025)|-0.045++<br>(0.022)|-0.105 <br>(0.094)|9 -0.164++<br>(0.082)|
|Hansen Overid testP value (Ho:|||||
|mstruments are valid)|0.303|0.592|0.110|0.495|
|Exogeneity test|0.407|0.406|0.504|0.324|
|Full-setofcontrols|No|Yes|No|Yes|

Notes: See Table 2. The instrument used include Real effective exchange rates. FDI inflows (% of GDP) and total volume of FDI inflows for both the measures of participation in GVCs. Estimations report standard errors in parantheses. +, ++, and +++ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
for measuring participation in GVCs and spatial dispersion as well as accounting for the endogeneity of GVC participation.

# **References**

Ahmed, S., M. Appendino and M. Ruta. (2015). Depreciations without Exports? Global Value Chains and the Exchange Rate Elasticity of Exports. Policy Research Working Paper 7390, World Bank, Washington, DC.

Alperovich, G.A. (1993). An explanatory model of city-size distribution: evidence from cross-country data. Urban Studies 30: 1591– 1601.

Baldwin, R. (2011a). Trade And Industrialization After Globalization's 2nd Unbundling: How Building And Joining A Supply Chain Are Different And Why It Matters. NBER Working Paper No. W17716.

Baldwin, Richard (2011b). 21st Century Regionalism: Filling the gap between 21st century trade and 20th century trade rules. CEPR Policy Insight No. 56.

Baldwin, R. (2012). Global Supply Chains: Why They Emerged, Why They Matter, and Where They are Going. CEPR Discussion Paper No. 9103 Baldwin, R., T. Ito and H. Sato. (2014). The smile curve: Evolving sources of value added in - manufacturing. Available at: <u>http://www.uniba.it/ricerca/dipartimenti/dse/e.g.i/egi2014 papers/ito</u> Banga R. (2013). Measuring Value in Global Value Chains. UNCTAD Working Background Paper.

Black, D., and J.V. Henderson. (2003). Urban Evolution in the USA. Journal of Economic Geography, 3(4):343-372.

Córdoba, J.-C. (2008). On the Distribution of City Sizes. Journal of Urban Economics, 63:177-197.

Cosar. A K and P. Fajgelbaum(2016). Internal Geography, International Trade, and Regional Specialization. American Economic Journal: Microeconomics 8(1): 24–56.

Deardorff, A. V. (1998). Fragmentation in Simple Trade Models, RSIE Discussion Paper 422, University of Michigan, January 8, 1998.

Desmet, K. and E. Rossi-Hansberg. (2009). Spatial Growth and Industry Age. Journal of Economic Theory, 144: 2477-2502 Duranton, G. (2007). Urban evolutions: the fast, the slow, and the still. American Economic Review, 97(1): 197-221.

15
Duranton, G. and Puga, D. (2001). Nursery cities: Urban diversity, process innovation, and the life cycle of products. American Economic Review, 91(5): 1454–1477.

Duranton, Gilles, and Diego Puga. (2020) The Economics of Urban Density. Journal of Economic Perspectives, 34 (3): 3-26.

Eeckhout, J. (2004). Gibrat's Law for (All) Cities. American Economic Review, 94(5):1429-1451.

Feenstra, Robert, and Gordon H. Hanson. (1996). Globalization, Outsourcing, and Wage Inequality," American Economic Review, 86(2): 240 - 245.

Fillat Castejon, C., J. Francois, and J. Woerz. (2008). Cross-border Trade and FDI in Services. CEPR Discussion Paper No. 7074, Centre for Economic Policy Research, London.

Fujita, M., Krugman, P., Venables, A.J. (1999). The Spatial Economy: Cities, Regions and International Trade. MIT Press, Cambridge, MA.

Gabaix, X. (1999). Zipf Law for Cities: An Explanation. Quarterly Journal of Economics,114(3):739-767.

Giesen, K., A. Zimmermann, and J. Suedekum (2010). The Size Distribution across All Cities: Double Pareto Lognormal Strikes. Journal of Urban Economics, 68:129-137.

Grossman, G. and E. Rossi-Hansberg (2008). Trading Tasks: A Simple Theory of Offshoring. American Economic Review, 98(5):78-97 Gover, A, S.V. Lall, and W.F. Maloney (2021) Place, Productivity, and Prosperity: Revisiting Spatially Targeted Policies for Regional Development. World Bank.

Grunfeld, L. and A. Moxnes. (2003). The Intangible Globalization: Explaining the Patterns of International Trade in Services. Norwegian Institute for International Affairs Working Paper No. 657, Norwegian Institute for International Affairs, Oslo, Norway.

Hanson G. (1996). Localization economies, vertical organization, and trade. American Economic Review, 86(5): 1266-1278.

Henderson, J. V. (1988). Urban Development. Oxford: Oxford University Press.

Henderson, J. V. (1974). The Sizes and Types of Cities. American Economic Review 64: 640-56.

Henderson, J. V. (1977). Economic Theory and the Cities. Oxford: Oxford University Press.

Henderson, J. V. and H.G. Wang. (2007) Urbanization and city growth: The role of institutions. Regional Science and Urban Economics 37: 283–313 16
Hill, B.M. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics, 3: 1163– 1174.

Hummels, D., Ishii, J. and Yi, K.-M. (2001). The nature and growth of vertical specialization in world trade’, Journal of International Economics 54(1): 75–96.

Ioannides, Y and S. Skouras. (2013). US city size distribution: Robustly Pareto, but only in the tail. Journal of Urban Economics, 73(1): 18–29 Ioannides, Y., H. Overman, E. Rossi-Hansberg and K. Schmidheiny. (2008). The effect of information and communication technologies on urban structure. Economic Policy, 54(23):201-242 Ioannides, Y.M., and H.G. Overman (2003). Zipf Law for Cities: An Empirical Examination. Regional Science and Urban Economics, 33:127-137.

Koopman, R., W. Powers, Z. Wang, S. Wei (2010). Give Credit Where Credit Is Due: Tracing Value Added in Global Production Chains”, NBER Working Paper No. 16426.

Krugman, P. (1991a). Geography and Trade. Cambridge, Mass.: MIT Press.

Krugman, P. (1991b). Increasing Returns and Economic Geography. Journal of Political Economy 99: 483-99.

Lanz, R. and A. Maurer. (2015). Services and global value chains – some evidence on Servicification of manufacturing And services networks. WTO Working Paper ERSD-2015-03 Mia, I. and E. Austin. (2008). Assessing the Foundations of Mexico's Competitiveness: Findings from the Global Competitiveness Index 2007-2008. White Paper, World Economic Forum.

Nitsch, V. (2005). Zipf Zipped. Journal of Urban Economics, 57:86-100.

Pham, M. D. Mishra, K. Cheong, J. Arnold, A.M. Trinh, H. Ngo, H. Nguyen. (2013). Trade Facilitation, Value Creation, and Competitiveness: Policy Implications For Vietnam's Economic Growth: Volume 1. The World Bank.

Redding S. and P. Fagjelbaum (2018) Trade, Structural Transformation and Development: Evidence from Argentina 1869-1914.

October 2018. NBER Working Paper 20217 Rodríguez-Pose, A (2018), "The revenge of the places that don’t matter (and what to do about it)", Cambridge Journal of Regions, Economy and Society 11(1):189–209 Rosen, K.T., and M. Resnick. (1980). The Size Distribution of Cities: An Examination of the Pareto Law and Primacy. Journal of Urban Economics, 8:165-186.

17
Rozenfeld, H. D., D. Rybski, X. Gabaix, H. A. Makse. (2011). The Area and Population of Cities: New Insights from a Different Perspective on Cities. American Economic Review 101(5):2205-2225.

Sanyal, K., and Jones, R., (1982). The theory of trade in middle products. American Economic Review 72(1): 16–31 Soo, K. (2005). Zipf ’s law for cities: A cross-country investigation. Regional Science and Urban Economics, 35(3): 239–63.

Soo, K.T. (2007). Zipf Law and Urban Growth in Malaysia. Urban Studies, 44(1):1-14.

UNCTAD. (2013). World Investment Report 2013. Global Value Chains: Investment and Trade for Development, New York & Geneva: United Nations.

Venables, A. J. (1999). Fragmentation and multinational production, European Economic Review 43: 935-945.

World Bank. 2008. World Development Report 2009: Reshaping Economic Geography. Washington, DC: World Bank.

World Trade Organization (WTO). (2011). Trade patterns and global value chains in East Asia: From trade in goods to trade. Available at: <u>https://www.wto.org/english/res_e/publications_e/stat_tradepat_globvalchains_e.htm</u> WTO and IDE-JETRO. (2011). Trade Patterns and Global Value Chains in East Asia: From Trade in Goods to Trade in Tasks,” World Trade Organization and Institute of Developing Economies, Geneva and Tokyo, 2011 (http://www.ide.go.jp/English/Press/pdf/20110606_news.pdf).

Zipf, G.K. (1949). Human Behaviour and the Principle of Least Effort. Addison-Wesley: Reading, MA.

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