metadata: authors: 0: author: Burger,Martijn
1: author: Ianchovichina,Elena
2: author: Akbar,Prottoy Aman
count: World
docna: 0: docna: Heterogeneous Agglomeration Economies in the
            Developing Countries : The Roles of Firm Characteristics,
            Sector Tradability, and Urban Mobility
docty: Policy Research Working Paper
subtopic: Labor Markets,Common Carriers Industry,Food & Beverage Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Intelligent Transport Systems,Transport Services,Crime and Society
teratopic: Social Protections and Labor,Macroeconomics and Economic Growth,Industry,Transport,Social Development
abstracts: cdata!: Using geo-coded, firm-level data on more
            than 51,000 establishments in 649 metropolitan areas in 98
            developing economies, from the World Bank’s Enterprise
            Surveys and a new global database on city-level mobility and
            congestion, this paper estimates the “pure” firm
            productivity gains of urban density, net of negative
            externalities associated with limited mobility, crime, and
            pollution. The results suggest that the average size of
            agglomeration economies in the developing world is
            comparable to the one observed in advanced countries, but
            the magnitude of the benefits of density on firm
            productivity substantially varies across firms. Returns to
            urban density are higher for firms operating in the
            tradables sector, exporters, foreign-owned firms, larger
            firms, and more experienced firms. Agglomeration economies
            are lost through both limited uncongested mobility and
            congestion, but the latter has a stronger negative effect on
            agglomeration economies and reduces relatively more the
            agglomeration benefits of firms in the non-tradables sector
            than those producing tradables.
repnme: repnme: Heterogeneous Agglomeration Economies in
            the Developing Countries : The Roles of Firm
            Characteristics, Sector Tradability, and Urban Mobility
display_title: Heterogeneous Agglomeration
            Economies in the Developing Countries : The Roles of Firm
            Characteristics, Sector Tradability, and Urban Mobility
listing_relative_url: /research/2022/03/33751495/heterogeneous-agglomeration-economies-developing-countries-roles-firm-characteristics-sector-tradability-urban-mobility
topicv3: Manufacturing Sectors m1326521 1281,Transport m1326661 135,Crime m1439217 3041,Labor Productivity m1504060 2756,Traffic Management m1583399 4231
publishtoextweb_dt: 2022-03-07T00:00:00Z
dois: 10.1596/1813-9450-9954
historic_topic: Social Protections and Labor,Macroeconomics and Economic Growth,Industry,Transport,Social Development
no_of_pages: 39
author_keys: 0: author_key: 1210533
1: author_key: 453205
2: author_key: 1473784
model_extractions: Policy Research Working Paper 9954 Heterogeneous Agglomeration Economies in the Developing Countries The Roles of Firm Characteristics, Sector Tradability, and Urban Mobility _Martijn Burger Elena Ianchovichina Prottoy Akbar_ Latin America and the Caribbean Region Office of the Chief Economist March 2022
Policy Research Working Paper 9954

# **Abstract**

Using geo-coded, firm-level data on more than 51,000 establishments in 649 metropolitan areas in 98 developing economies, from the World Bank’s Enterprise Surveys and a new global database on city-level mobility and congestion, this paper estimates the “pure” firm productivity gains of urban density, net of negative externalities associated with limited mobility, crime, and pollution. The results suggest that the average size of agglomeration economies in the developing world is comparable to the one observed in advanced countries, but the magnitude of the benefits of density on firm productivity substantially varies across firms. Returns to urban density are higher for firms operating in the tradables sector, exporters, foreign-owned firms, larger firms, and more experienced firms. Agglomeration economies are lost through both limited uncongested mobility and congestion, but the latter has a stronger negative effect on agglomeration economies and reduces relatively more the agglomeration benefits of firms in the non-tradables sector than those producing tradables.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 mburger@ese.eur.nl, eianchovichina@worldbank.org, or prottoy.akbar@aalto.fi.

_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
**Heterogeneous Agglomeration Economies in the Developing Countries: The Roles of Firm Characteristics, Sector Tradability, and Urban Mobility**

Martijn Burger,<sup>∗</sup> Elena Ianchovichina,<sup>א</sup> and Prottoy Akbar<sup>♦</sup>

**Keywords** : agglomeration economies, developing economies, tradables, congestion, urban mobility

**JEL Codes** : D22, E26, O18, R00, R1

> ∗ Martijn J. Burger is a professor in the Department of Organization, Open University of the Netherlands and academic director at the Erasmus Happiness Economics Research Organisation (EHERO), Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, the Netherlands, Tel: +31 (0) 10 4089579, Fax: +31 (0)10 4089141. E-mail: mburger@ese.eur.nl.

> א Elena I. Ianchovichina is a lead economist and the deputy chief economist for the Latin America and the Caribbean Region of the World Bank, 1818 H Street NW, Washington, DC 20433, USA, Tel: +1 202 2803576. E- mail: eianchovichina@worldbank.org.

> ♦ Prottoy A. Akbar is an assistant professor at Aalto University and Helsinki Graduate School of Economics. Email: <u>prottoy.akbar@aalto.fi.</u>

We thank Frank van Oort, Martin Rama, and Bob Rijkers for useful comments on earlier drafts of the paper. We are grateful to Victor Couture, Gilles Duranton, and Adam Storeygard for sharing the mobility data. The findings, interpretations, and conclusions expressed in this paper are entirely ours. 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 or the governments they represent.
# **1. Introduction**

Do all firms in developing countries’ metropolitan areas benefit from agglomeration economies despite the considerable amount of congestion, crime, and pollution in these places? And if not, which firms benefit more than others? From the large urban economics literature, which has traditionally focused on developed economies (Chauvin et al., 2017), we know that firms have good reasons to concentrate in cities. On the supply side, they can profit from co-location through _sharing_ , _matching_ , and _learning_ (Duranton & Puga, 2004; Puga, 2010). Firms benefit from sharing a common pool of resources that need a minimum community size to be supported, such as universities, cultural amenities, and infrastructure. The presence of a large labor pool and a wider variety of input suppliers enable firms to quickly find high quality workers and intermediate inputs. Co-location also makes it easier to generate, diffuse, and accumulate knowledge through face-toface contact, and therefore to spur innovation. These supply-side agglomeration externalities emerge either due to learning by specializing (Marshall, 1890), learning from diversity (Jacobs, 1961), or learning by doing (dynamic learning effects) and are at the core of the agglomeration economics literature (Rosenthal & Strange, 2003; McCann & Van Oort, 2019). Although economists have traditionally focused on the supply-side of agglomeration economies (McCann & Folta, 2009), firms also benefit from greater demand in large urban areas. The demand-side benefits of agglomeration can be substantial as proximity to consumers reduces transportation costs (Alcácer & Chung, 2014); reduced search costs in combination with a greater variety of goods and services in large cities can induce spatial agglomeration benefits (Fujita, 1988).<sup>1</sup> The “pure” agglomeration economies capture both the demand-side market-size externalities and the supply-side learning externalities, net of any quality effects due to sorting and any potential effects of productivity on density (reverse causality). However, these “pure” agglomeration benefits can be substantially reduced and even completely offset by agglomeration diseconomies or congestion costs because the concentration of people and firms increases traffic congestion, crime, exposure to pollution and disease, and competition for consumers and centrally located real estate (Richardson, 1995; Lall et al., 2004; Duranton & Puga, 2020). Grover, Lall, and Maloney (2021) argue that agglomeration costs are particularly high in developing economies where premature urbanization (Gollin et al. 2016) has created “sterile agglomeration” economies. Indeed, there is evidence of “sterile agglomeration” economies in some countries. Combes et al. (2019) and Duranton (2016) show that the benefits of co-location in terms of higher wages are completely

> 1 The higher growth of high-amenity cities compared with low-amenity cities offers evidence on the importance of the demand-side benefits of agglomeration (Glaeser et al., 2001; Markusen & Schrock, 2009).

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offset by the higher costs of operating in French and Colombian cities. Grover and Maloney (2022) find that the elasticity of physical total factor productivity with respect to density is negative in Colombia, Ethiopia, and Indonesia, but positive in Chile. The bulk of the literature on agglomeration economies, however, finds evidence of strong productivity gains of urban density in both developed and developing countries (see e.g., Melo et al., 2009; Beaudry & Schiffauerova, 2009; Combes & Gobillon, 2015; Duranton, 2015; Chauvin et al., 2017; Badr et al., 2019; Jones et al., 2020; Grover et al., 2021).<sup>2</sup> At the same time, there seem to be notable differences across countries and industries (De Groot et al., 2016; Ahlfeldt & Pietrostefani, 2019).

This mixed empirical evidence on the size of net agglomeration benefits in developing economies has attracted the attention of researchers in the field of regional science and urban economics (Duranton, 2015; Glaeser and Henderson, 2017; Grover et al., 2021) and motivates the empirical investigation in this paper. By exploring how these gains vary with firm characteristics and capabilities, the tradability of the sector in which the firm operates, and the mobility within the urban center in which the firm is located, we hope to inform the debate on “sterile” agglomeration economies in the developing economies. Specifically, the paper contributes to this literature in the following ways. It uses geo-coded data on more than 51,000 establishments in 649 metropolitan areas in 98 developing economies,<sup>3</sup> from the World Bank’s formal Enterprise Surveys and a multilevel estimation technique to assess the “pure” labor productivity gains of urban density, net of negative externalities such as crime, pollution, and limited mobility. We then test to what extent these estimated agglomeration economies differ across firms operating in the tradable versus nontradable sectors and across firms of different sizes, market orientation, ownership, and management capabilities. The results suggest that the average size of agglomeration economies in the developing world is comparable to the one observed in advanced economies, but the magnitude of the benefits of density on labor productivity substantially vary across firms. In line with the Venables’ (2017a) hypothesis, returns to urban density are higher for firms operating in the tradables sector. Manufacturing, exporting, and foreign-owned firms as well as larger and more experienced firms benefit more from agglomeration externalities than firms providing services in the domestic market, domestic-owned firms, and smaller and less experienced firms.

Another contribution of this paper is its focus on urban mobility as a key agglomeration cost, which has not received sufficient attention in the agglomeration literature. Recent research by

> 2 In this regard, several papers show that the urban density effects are similar in developed and developing countries (Chauvin et al., 2017; Henderson et al. 2019).

> 3 These economies represent 70% of all developing economies and cover over 94% of the population in all developing economies.

3
Akbar, Couture, Duranton, and Storeygard (2021) on urban mobility has shown that traffic in developing economies is generally much slower than in developed countries,<sup>4</sup> particularly because of infrastructure deficiencies in the metropolitan areas of the developing economies. However, apart from being a direct agglomeration cost, the lack of urban mobility can also indirectly affect pure agglomeration externalities because it makes sharing, matching, and learning in cities more difficult. Mobility is key to the generation of agglomeration economies, which tend to decay strongly with travel time (Graham, 2007; Rosenthal and Strange, 2008; Graham & Melo, 2011). At the same time, the lack of mobility is a consequence of the concentration of people and economic activities and a by-product of agglomeration benefits (Duranton and Puga, 2020). Ultimately, it is unclear whether mobility issues harm some firms more than others and whether these issues are more pronounced in some parts of the developing world. Building on the agglomeration economies literature that has examined the role of transport networks and travel times (e.g., Graham, 2007; Melo et al., 2017) and using a new database of mobility indexes for 1,228 cities constructed by Akbar et al. (2021), based on more than 530 million simulated trips on Google Maps for the period between June and October 2019, we examine how firm-specific agglomeration benefits are dependent on urban “uncongested” travel speeds and congestion in the developing countries. Low uncongested travel speed or the speed in the absence of traffic originates from the lack of good infrastructure in a metropolitan area and is characterized by slow traffic during the whole day, while congestion delay is typically caused by traffic during peak travel times. Thus, both low travel speeds and congestion limit mobility within metropolitan areas and, therefore, obstruct the generation of external economies of scale (see also Collier & Venables, 2016; Venables, 2017b; Gerritse & Arribas-Bel, 2018), but congestion does so during specific parts of the business day, with a potentially bigger effect on the non-tradable sector, which unlike the tradable sector depends more heavily on the local consumer market. In our research, we find that agglomeration economies are lost both through limited uncongested mobility and through congestion, but the latter tends to have a much stronger negative effect on agglomeration economies and to harm relatively more firms in the non-tradable sectors than those producing tradables.

The findings in this paper suggest that “sterile” agglomeration economies are most likely in developing economies with congested cities and an urban enterprise sector dominated by small, inefficient, domestic-owned establishments serving the domestic market and operating in the nontradables sector. These cities – also known as consumption cities with high shares of employment in urban non-tradables (Jedwab et al., 2022) – tend to be more prevalent in developing economies

> 4 Likewise, Grover et al. (2021) find that for the average city density in their data, 19-30 percent fewer hours are spent in traffic congestion in developed countries compared to developing countries.

4
dependent to a large degree on commodity exports and developing economies undergoing premature de-industrialization (Jedwab et al., 2022).<sup>5</sup> The remainder of this paper is organized as follows. Section 2 examines the literature on agglomeration economies, focusing specifically on issues of mobility, and lists the main hypotheses tested in the paper. Section 3 presents the data and methodology including the econometric framework, definitions of variables, and estimation issues. Section 4 discusses the results obtained using different estimators, the baseline results, and the evidence on the hypotheses formulated in Section 2. The final section concludes and provides suggestions for future research.

# **2. Literature and hypotheses**

If the agglomeration benefits and costs vary by sector and type of firm, the net agglomeration economies at the country and regional levels are likely to depend on the sectoral composition and firm characteristics of the countries’ enterprise sectors. We use theoretical insights to identify the firm and sectoral characteristics that might matter most for the ability of firms to internalize agglomeration externalities.

# _2.1. Agglomeration economies: The role of sector tradability and firm characteristics_

Theory tells us that the supply-side benefits of co-location are relatively small for firms in the nontradable sectors such as retail, personal services, construction, government, and property management (Venables 2017a). These sectors employ relatively low-skilled labor, account for a large share of activity in the economy and are unlikely to exhibit increasing returns to scale if they were to agglomerate (Venables 2017a). By contrast, the supply-side benefits of co-location are relatively large for firms in the tradable sector, especially those using more skill-intensive labor (Bacolod et al., 2009; Andersson et al., 2014; Venables, 2017a; Matano et al., 2020; Stavropoulos et al., 2020). In the developing world, manufacturing is typically a relatively skilled sector and thus likely to exhibit increasing returns to scale and benefit from agglomeration externalities in large urban areas. Cognitive skills that help people to better absorb and process information and knowledge are more valuable in more concentrated markets; such skills enhance the benefits from matching (Behrens and Robert-Nicoud, 2015) and knowledge spillovers between firms (Caragliu and Nijkamp, 2012).

Hence, our first three hypotheses test whether the effect of urban density on productivity differs for firms in the non-tradable and tradable sectors. The latter sector relies for business on non-local

> 5 Consumption cities differ from consumer cities (Glaeser et al., 2001), which are cities with better amenities within a country.

5
markets and includes all manufacturing firms, some of which are exporters and some foreignowned firms.

_Hypothesis 1A_ : Urban density has a greater effect on the productivity of firms in the tradable sectors than those operating in the non-tradable sectors of developing economies.

_Hypothesis 1B:_ Urban density has a greater effect on the productivity of exporting firms than those serving domestic markets in developing economies.

_Hypothesis 1C:_ Urban density has a greater effect on the productivity of foreign-owned firms than non-foreign-owned firms, which tend to operate in domestic markets.

# _2.2. Agglomeration economies: The role of urban mobility and congestion_

Regional heterogeneity also plays a role in explaining differential returns to urban density. The literature has already examined regional spatial structure (e.g. Meijers and Burger, 2010), national and international urban network embeddedness of metropolitan regions (e.g., McCann and Acs, 2011), and transport infrastructure within metropolitan regions (e.g. Graham & Melo, 2011). Key in these debates are functional integration of regions and travel time. Connectivity within metropolitan regions is important and agglomeration externalities are contingent on the flows of goods, people, and knowledge between areas within a city. Apart from reducing the effective market size, and thus demand-driven agglomeration benefits, long travel times reduce opportunities for agglomeration benefits by increasing the effective distance between people and economic possibilities (Avner & Lall, 2016). Labor market pooling and finding the right employees becomes more difficult in a fragmented city. Low mobility increases the wage at which a person is willing to supply labor (reservation wage hypothesis), reduces the number of available jobs for a given commuting time (Patacchini and Zenou, 2005), and decreases the affordability of job searches and commutes (Manning & Petrongolo, 2017). Thus, limited mobility increases labor costs, decreases opportunities for matching in labor markets, and potentially results in the misallocation of human resources. Indeed, as suggested by Collier and Venables (2016, p.2), the value of urban mobility goes _‘well beyond the ‘user-benefits’ of standard cost-benefit appraisal as it supports an economic environment in which the economic potential of cities – scale, specialisation, and agglomeration economies – can be achieved’_ . In other words, improved mobility increases access of firms to other firms and population, which induces external economies to scale (Venables, 2007) that are not part of standard transportation benefits. By contrast, the lack of mobility decreases the effect of urban density (Graham & Melo, 2011).

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Urban mobility depends on the availability and quality of urban infrastructure and infrastructure services, urban topography, shape and density, urban activity, and any policies that may affect the ability of people to travel along some routes during different times of the day. Many cities in the developing world are growing fast, but improvements in intraurban infrastructure and connectivity services have not kept up pace. The resulting long travel times and congestion during peak travel times, in turn, obstruct the creation of external economies of scale (Lall et al., 2017; Collier & Venables, 2016; Venables, 2017b).<sup>6</sup> Although there are several studies that use transport-based measures of agglomeration externalities (e.g. Lall et al., 2004; Graham, 2007; Holl, 2012), these studies do not show how mobility affects the generation of agglomeration economies (Melo & Graham, 2018). To the best of our knowledge, only three studies focusing on US metropolitan areas have examined how intra-city mobility affects the generation of agglomeration economies. Chatman and Noland (2014) find an indirect effect of public transit on productivity through unanticipated agglomeration benefits from transit-induced urban densification. Likewise, Melo and Graham (2018) find that public transit strengthens urban agglomeration effects, but additional road infrastructure weaken it through dispersion of economic activities. Conversely, Gerritse and Arribas-Bel (2018) find that highway density improves agglomeration benefits.

Provided the mixed empirical evidence, it is also still unclear which firms’ agglomeration benefits are most harmed by low mobility. Poor urban mobility due to insufficient or poor-quality infrastructure is expected to be harmful to all types of urban activity as it slows down travel speed throughout the day and night and thus increases intraurban travel costs for all types of economic activity. By contrast, congestion during the peak travel hours is expected to be more harmful to firms in the non-tradable sector since these firms depend more heavily on the local consumer market than those involved in tradable activities such as manufacturing or digital services. Employment in the tradables sector is relatively high skilled, implying that workers in the sector have better mobility than most consumers of non-tradable services as they can afford to live in locations closer to work and with better access to transport infrastructure and faster modes of transport (see e.g., Oviedo et al., 2019; Hernandez et al., 2020). In the case of manufacturing, even in very congested cities firms can optimize the delivery system by adjusting shipments times or by using storage and keeping inventory at hand. Therefore, we expect that limited urban mobility due

> 6 For example, Duranton (2021) argues that traffic infrastructure in Latin American cities is highly underdeveloped and congested, in line with the limited investments in new roads (Ferreyra and Roberts, 2018) and the fact that LAC metropolitan areas are among the most congested cities in the world (Akbar et al., 2021).

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to traffic congestion is likely to harm less the agglomeration benefits of firms in the tradable sector relative to the non-tradable sector and formulate the following two related hypotheses.

_Hypothesis 2A_ : The effect of urban density on firm productivity in developing economies depends on mobility within metropolitan areas, so that firms in lower mobility cities benefit less from agglomeration economies than firms in higher mobility cities.

_Hypothesis 2B_ : The moderating effect of congestion on the relationship between urban density and labor productivity is stronger for firms in the non-tradable sector than for firms in the tradable sector.

# **3. Data and methodology**

In our empirical investigation, we would like to examine the “pure” agglomeration effects, which capture both the supply-side learning externalities and the demand-side market-size externalities, _net_ of any geographical, agglomeration costs, quality effects due to sorting, and any potential effects of productivity on density (reverse causality). We start with a baseline model of the relationship between urban density and labor productivity, in which none of the controls for agglomeration costs are included and in which we do not address reverse causality and sorting. In each subsequent model, we address each of these issues by controlling for geographical, agglomeration costs, quality effects due to sorting, and any potential effects of productivity on density.

# _3.1. Data sources_

We use a harmonized and geo-coded data set based on the World Bank Enterprise Surveys for 98 low- and middle-income developing economies for the period from 2009 to 2020. See Appendix A1 for information on the economies included in the analysis. These firm-level surveys are representative of a country’s private sector, but exclude the resource-based sector, the financial sector, and firms with less than five employees. The enterprise surveys are produced using a stratified random sampling strategy and cover in a comparable way a range of businessenvironment topics including sales, number of employees, age of firm, manager experience, ownership characteristics, employees’ education, the degree to which firms see crime as an obstacle, and the location of the establishments, among others.<sup>7</sup> Appendix B displays the list of all firm-level variables used in the analysis.

To test our hypotheses, we link the enterprise survey data to metropolitan area–level data by obtaining GPS coordinates or detailed location names for all establishments in the Enterprise

> 7 We looked at the country classification at the time of the survey.

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Survey data. Specifically, we utilize the GHS-FUA spatial package developed by Moreno-Monroy et al. (2020), which offers a global delineation of metropolitan or functional urban areas (FUAs) combining a morphological and functional approach. Metropolitan areas consist of urban centers with at least 50,000 inhabitants and their surrounding commuting zones. Since detailed GPS or place name data are not readily available for every Enterprise Survey, particularly for the earlier years, and not all relevant establishment characteristics – most notably sales and employee education – are recorded for every establishment, our initial _full sample_ consists of 51,702 establishments in 649 metropolitan areas of 98 economies.

Most surveys have been conducted in the main urban centers although in recent years it has become more common to conduct surveys in smaller urban centers and more rural areas. In our data set, we link the location of the establishments to FUAs (also called metropolitan areas) with at least 50,000 inhabitants. Hence, our data set excludes rural areas and towns with fewer than 50,000 inhabitants. Of the establishments located in metropolitan areas, 68% are located in metropolitan areas with more than 1 million inhabitants, 29% are located in medium sized FUAs (between 100,000 and 1 million) and the remaining 3% are found in small FUAs between (50,000 and 100,000). Thus, our data set covers predominantly the largest metropolitan areas in developing economies. Second, our _common_ sample, which we use to test out hypotheses on urban mobility, consists of 38,526 establishments in 356 metropolitan areas in 80 developing economies because only for these metropolitan areas we have information on urban mobility and congestion from the work of Akbar et al. (2021). Appendix A1 includes the list of economies included in the _common_ sample.

# _3.2. Description of variables_

# _Dependent variable: Labor productivity_

Labor productivity is measured as the natural logarithm of sales per worker, computed as the revenue deflated and represented in 2009 USD during the last fiscal year divided by the number of employees (see also Amin & Okou, 2020). We use labor productivity instead of the TFP measure (Francis et al., 2020) because information for the latter is only available for less than 40% of the observations and is not available for firms in the non-tradable sectors.

# _Independent variable: Urban density_

We use urban density instead of urban population size to measure agglomeration since recent research argues that in the developing world agglomeration externalities predominantly arise from density rather than absolute market size (Henderson et al., 2019). Urban density is obtained from 9
the GHS-FUA spatial package (Moreno-Monroy et al., 2020) and it is measured as the population living in urban centers in a metropolitan area divided by the land area of the urban centers in that metropolitan area.

# _Moderator variables for firm and sector characteristics_

We assess the moderating effect of tradability by looking at sector, exporting behavior, and foreign ownership. The sector classification provided in the enterprise surveys is based on the International Standard Industrial Classification of All Economic Activities (ISIC). In line with the taxonomy of De Gregorio et al. (1994), all firms active in manufacturing (ISIC 15-37) are part of the tradable sector, while firms active in services, including construction (ISIC 45), wholesale trade (ISIC 50-51), retail trade (ISIC 52), hotels and restaurants (ISIC 55), transportation services (ISIC 60-64), business services (ISIC 70-74) and personal services (ISIC 80-98) are part of the nontradable sector. Utilities, mining, and financial services are not included in the Enterprise Surveys. Our _manufacturing_ dummy is therefore 1 if a firm operates in the manufacturing sector and 0 otherwise.

We then explore the moderating effect of exporting and foreign ownership. The _exporter_ dummy is set at 1, if a firm exports at least 10% of its sales, and 0 otherwise. The _foreign-ownership_ dummy also takes the value 1 if at least 10% of the establishment is foreign owned, and 0 otherwise. While all exporting firms belong to the tradable sector, in our sample about one fifth of all manufacturing establishments, which represent the tradables sector in our analysis, are exporters and only around 10% are foreign owned. By contrast, only 6.6% of services firms, which in our classification belong to the non-tradables sector, are exporters. Therefore, our non-tradable sector includes mostly firms serving the domestic market, while our analysis of the moderating effect of exporting and foreign ownership considers to the extent possible the influence of services firms operating in the tradable sector. Details are provided in Appendix Table B2.

# _Mobility and congestion variables_

We obtain measures of mobility and congestion indexes from Akbar et al. (2018; 2021), who use travel time predictions from millions of simulated trips to compute city-level speed indices. Specifically, the database of Akbar et al. (2021) makes use of roughly 538 million trip instances in 1,228 cities<sup>8</sup> that have been queried on Google Maps between June and October 2019. In each

> 8 Countries in which Google Maps is restricted (e.g., China and the Republic of Korea) and a few other cities that are missing credible variation in real-time travel data were excluded.

10
case, Google's estimate of the duration and distance on the most recommended and fastest routes was recorded and each trip was queried at multiple times of the day and days of the year.<sup>9</sup> To construct comparable<sup>10</sup> city-specific speed indices from trips _i_ in city c( _i_ ), Akbar et al. (2021) regress their trip-level measure of speed outcomes on observable trip characteristics _X,_ including trip length, distance to the city center, trip type, time of day, day of week, and weather, to estimate city fixed effects that serve as city-level mobility indices, _Mobilityc(i)_ : ′ log S S o o 0 + α M . (1) ii ii cc(ii) ii SS_o SS = ββ + MMooM ooMM + SS Real-time speed outcomes ( _Speed_outcome_ i) on recommended route are used to estimate a baseline index of city speed, or an overall mobility index. Then, because Akbar et al. (2018; 2021) observe the same trips at different times of the day, they conduct analogous estimations of (1) using speed outcomes in the absence of traffic. From these estimations they obtain city-level uncongested mobility indexes. The difference between the city-level overall and uncongested mobility indexes is the city-level index of congestion delay caused by traffic. Accordingly, the overall mobility index decomposes exactly into the congestion index and the uncongested mobility index, and each index can be related to city characteristics for cross-city comparisons. We use the three indexes to distinguish the role of congestion from the role of uncongested mobility in the relationship between mobility, density, and agglomeration economies. Descriptive statistics of the obtained speed indices are provided in Appendix A2.

The mobility data of Akbar et al. (2021) offers several advantages over alternative sources, such as INRIX and TomTom data. First, the mobility indices are based on millions of trips on Google Maps, simulated over the same period of time using the same trip sampling strategy for cities all around the world. This was done precisely to facilitate cross-country analysis of the kind undertaken here. Second, Akbar et al. (2018) show that their mobility indices are robust to various alternative trip sampling strategies (such as with different weights on trip destinations, directions of travel or time of day). The indices also condition out the effect of trip characteristics that may vary systematically across cities and would bias any speed estimates based on actual trips. Third, Akbar et al. (2018) show for India that the predicted speeds on the simulated trips are still

> 9 Akbar et al. (2021) design the aggregate distribution of departure times (i.e., the time of query) across trip instances to mimic the distribution of departure times in the US National Household Travel Survey, except that they oversample sparse late night departure times in order to estimate travel times when there is no traffic. 10 The naive approach would be to divide the sum of trip lengths by the sum of trip durations to get an average speed for each city. However, this would ignore the fact that characteristics of trips can differ systematically across cities. Another attractive feature of these indices over raw means of travel times (such as from travel surveys) is that they are not confounded by trip characteristics that may be important determinants of speed but may also vary systematically across cities, such as trip length, departure times and weather conditions.

11
comparable to those from actual trips in cities, including relatively small cities where one might worry more about poor data quality.

The speed indices in Akbar et al. (2021) are based only on trips within the built-up urban cores, whereas we sample establishments from the broader metropolitan area surrounding it. However, given the monocentric nature of most metropolitan areas in the developing economies, it is safe to assume that the effect of mobility within the urban core on the agglomeration economies of establishments within and outside of it are correlated. Finally, for ease of interpretation, in the regression analysis we use the additive inverses of the mobility indexes.

# _Other agglomeration diseconomies_

We control for several agglomeration diseconomies, including whether a firm experiences crime, theft, and disorder as an obstacle to doing business (measured on a 0-4 scale), whether a firm perceives competition from informal firms as an obstacle to doing business (measured on a 0-4 scale), and whether a firm experienced electricity outages during the previous year (measured with a dummy variable). We also control in our regressions for the average level of air pollution (PM 2.5), obtained from the Urban Centre Database, which is a part of the Global Human Settlement Layer Database.

# _Controlling for physical geography_

Several other metropolitan area-level variables may confound the relationship between urban density and labor productivity, particularly non-human endowments, such as average temperature and precipitation. We obtain information on these variables from the Urban Centre Database, available as part of the Global Human Settlement Layer Database, and control for them in our regression analysis.

# _Controlling for firm sorting and initial sorting of workers_

To control for firm sorting, we include the size of the firm by accounting for (1) the number of full-time employees working in the establishment and (2) a dummy variable indicating whether the establishment is part of a large conglomerate. As noted by Van Oort et al. (2012) the effects of agglomeration that are often found in area-based studies can be compositional in nature because a location may be productive due to the density of large firms and the presence of internal rather than external economies of scale. Along similar lines, the productivity premium may be driven by a higher capital intensity of firms in cities rather than agglomeration economies. Hence, in the absence of a uniform measure of capital for a large set of firms, we control for investments in fixed assets made by firms over the past year. Second, we must account for the fact that cities 12
attract better employees because high-skilled employees sort into cities with better opportunities (Combes et al., 2008). Hence, we control in our econometric analysis for employees’ skills by including the percentage of employees who finished high school and the presence of on-the-job training. Finally, we control for other firm characteristics capturing capabilities and experience including firm age, manager experience, sole proprietorship, and state ownership. Appendix Table B1 provides definitions for all variables included in the analysis.

# _3.3 Econometric strategy_

Following earlier studies on agglomeration and firm performance (e.g. Fontes et al., 2010; Van Oort et al., 2012; Schmutzler & Lorenz, 2018; Stavropoulos et al., 2020), we utilize a mixed hierarchical model to examine the relationships between urban density and firm productivity and how it changes when we include controls for firm and metropolitan characteristics. Although the OLS estimator is often used in research on firms and agglomeration economies (Van Oort et al., 2012), ignoring grouping of data (firms nested in metropolitan area) would typically result in an underestimation of standard errors and unreliable regressions coefficients (Rabe-Hesketh & Skrondal, 2008; Raudenbusch & Bryk, 2002) for the metropolitan-area level variables and an overstatement of statistical significance because of the violation of the assumption of independent observations (Snijders and Bosker, 2011). See Hox et al. (2017), Rabe-Hesketh & Skrondal (2008), and Snijders and Bosker (2011) for a discussion on mixed hierarchical or multilevel modeling.

Specifically, we start from the following equation: i 0 + ∑ + ∑ 0i (2) qq rr ii=1 qq0 qqi ii=1 rr0 rrii 0ii in which MMM = ββ is labor productivity of the ββ αα -th firm nested within the φφ ZZ + oo + SS -th metropolitan area and and are sets of vectors containing the explanatory variables at the firm and metropolitan area ii qqi MM ii αα levels, respectively. The rrii _β_ s and _φ_ s refer to the associated regression slope coefficients, while ZZ subscripts _q_ and _r_ indicate the number of explanatory variables included at the firm and metropolitan area levels, respectively. Unlike a standard OLS equation, the error in Equation (2) includes the usual error term, 0i , plus an additional component, , which captures the correlation of errors within metropolitan areas and herewith accounts for the clustering of errors. 0ii SS oo Building on this baseline model, in subsequent models we also include cross-level interactions between metropolitan-level variables (e.g., urban mobility, urban density, congestion) and firmlevel variables (e.g., sector, exporter, foreign ownership) to test the hypotheses outlined in Section 2.

13
An often-cited problem with multilevel models is the sample-size requirements at the higher levels of the hierarchy. Data sparseness or a limited number of observations per group can lead to inaccuracy of parameter estimates. Although there is no official rule for the number of observations per group (metropolitan areas in our case), rules of thumb in the literature recommend a minimum of 20 to 50 observations per group within each component (Raudenbush & Bryk, 2002; Kreft & De Leeuw, 1998; Maas & Hox, 2004). Several simulation studies have shown that multilevel modeling may be used even with as little as 10 observations per group (Bell et al., 2014; McNeish & Stapleton, 2016). In this study, we therefore include all metropolitan areas with at least 10 establishments surveyed.

In addition, the standard multilevel model assumes that the firm-level explanatory variables are uncorrelated with the metropolitan area-level error terms. However, both theoretically and empirically, such an assumption is difficult to meet due to the sorting of firms and workers across space and not correcting for this would lead to inconsistent parameter estimates. As indicated by Snijders and Bosker (2011), the correlation between the lower-level predictor variables and higherlevel error terms can be removed by including higher-level means of the firm-level predictor variables in the regression model, a procedure known as the Mundlak (1978) correction using the _mundlak_ command in Stata (Perales, 2013).<sup>11</sup> Hence, our regressions models are augmented with this correction, yielding the following equation: i 0 + ∑ + ∑ + ∑ 0i (3) qq qq rr in which MM = ββ are the metropolitan area averages of the firm-level variables ii=1 ββqq0ααqqi ii=1 γγqq0αα<sup>�</sup> qqii ii=1 φφrr0ZZrrii + oo0ii + SS , which absorb all possible correlations between the firm-level variables and group-level random effects qqii qqi . Several αα<sup>�</sup> αα studies have shown that the Mundlak correction provides the estimates and standard errors 0ii oo equivalent to the fixed effects model (Huang, 2018; Oshchepkov and Shirokanova, 2020), but still offers the possibility (unlike the FE model) to include metropolitan area variables.

An alternative would be to estimate the models using OLS with clustered standard errors. Although these estimations are included in the sensitivity analysis of this paper in Table 1, we refrain from using this estimation strategy in our baseline models because the key difference between the methods concerns the estimation of the standard errors, where multilevel modeling typically allows for a lower number of clusters than OLS with clustered standard errors for robust variances of coefficients. In addition, OLS with clustered standard errors is more vulnerable to

> 11 As noted by Kim and Frees (2007) and Huang (2018), this is also a main reason why economists have shied away from using multilevel modeling. See, for instance, Burger et al. (2011) for an application of the Mundlak correction to the study of agglomeration economies.

14
bias due to unequal group sizes – as the model also assumes equal group sizes (Cameron et al., 2008; Oshchepkov and Shirokanova, 2020). Unequal group sizes are part and parcel of our data set since we have a few metropolitan areas with many observations and many metropolitan areas with a limited number of observations. Because our goal is to estimate the impact of the group level variable – urban density – the Mundlak model seems to be the best reasonable choice. In addition, the Mundlak estimation accounts for omitted variable bias by incorporating average firmlevel characteristics in the model and thus factoring in regional firm demography and socioeconomic conditions.

Although the Mundlak model addresses part of the endogeneity problems in agglomeration economics models, this estimation technique does not control for endogeneity arising from reverse causality between firms’ productivity, agglomeration economies, and mobility. Specifically, the location choices of firms resulting in lower or higher levels of urban density and mobility in some places could be induced by the migration of firms between places of low productivity to places of high productivity (Melo et al. 2009). The most common technique to deal with this reverse causality is to isolate the effect that runs from urban density to labor productivity by means of a two-stage least squares (2SLS) estimation. Based on earlier work of (amongst others) Ciccone and Hall (1996), Combes et al. (2010), and Chauvin et al. (2017), we used historical and geography variables which can be thought to only affect labor productivity through their effect on current urban density: (1) population residing in the metropolitan area in 1975 and (2) average altitude of the main center. The 1975 historical urban density variable is constructed in exactly the same way as our 2015 variable, using the delineation of that period, obtained from the Global Human Settlement Layer. The altitude variable is also obtained from the Global Human Settlement Layer and is measured as the number of meters above sea level. We perform Wu-Durban-Hausman tests after our estimations to gauge whether urban density can be treated as exogenous variable. As shown in the tables in Section 4, the used instruments are relevant (not weak) and exogenous.

Although there may be a problem of reverse causality from a theoretical point of view, this does not necessarily mean that there is a problem from an econometric point of view (Combes et al, 2008). If indeed the independent variable of interest is exogenous, then the Mundlak estimation, which is a more efficient estimator, should be preferred over its 2SLS counterpart. In addition, estimating 2SLS models including interaction effects is a challenge in and of itself without having good instruments for the interaction effects.

15
# **4. Empirical results**

This section presents the results obtained using different estimators, the baseline results, and the evidence on the hypotheses in Section 2.

# _4.1. Results from different estimators and baseline findings_

Table 1 shows our baseline estimations, which examine the association between urban density and the dependent variable – sales per worker – using both conventional random effects and Mundlak estimation. The results suggest that urban density is positively related to labor productivity (Table 1, Column 1). Doubling urban density is associated with an increase in sales per worker by 10% on average. The urban density effect is robust to the inclusion of firm-level control variables in our model (Table 1, Columns 2 and 3) and additional metropolitan area-related control variables (Table 1, Columns 4 and 5). Depending on the model specification, the effect of urban density is weakly significant (at the 10% level) or insignificant. Differences between the conventional random effects model and the Mundlak estimation are limited.

**Table 1: Baseline regressions on the effects of urban density**

||(1)<br>Random<br>Effects<br>Full<br>Sample|(2)<br>Random<br>Effects<br>Full<br>Sample|(3)<br>Mundlak<br>Full<br>Sample|(4)<br>Random<br>Effects<br>Common<br>Sample|(5)<br>Mundlak<br>Common<br>Sample|(6)<br>Mundlak<br>IV<br>Full<br>Sample|(6)<br>Mundlak<br>IV<br>Common<br>Sample|(7)<br>OLS-<br>2SLS<br>Common<br>Sample|
|---|---|---|---|---|---|---|---|---|
|Urban density (ln)|0.10<br>(0.06)*|0.10<br>(0.06)*|0.08<br>(0.06)|0.13<br>(0.09)|0.15<br>(0.09)*|0.14<br>(0.20)|0.45<br>(0.37)|0.17<br>(0.40)|
|Firm-level|||||||||
|Number of<br>employees(ln)||0.02<br>(0.01)***|0.02<br>(0.01)***|0.03<br>(0.01)***|0.02<br>(0.01)***|0.01<br>(0.00)***|0.02<br>(0.01)***|0.02<br>(0.01)*|
|Fixed assets (0/1)||0.16<br>(0.01)***|0.16<br>(0.01)***|0.14<br>(0.02)***|0.14<br>(0.02)***|0.16<br>(0.01)***|0.14<br>(0.01)***|0.12<br>(0.03)***|
|Firm age (ln)||0.09<br>(0.01)***|0.09<br>(0.01)***|0.09<br>(0.01)***|0.09<br>(0.01)***|0.09<br>(0.01)***|0.09<br>(0.01)***|0.09<br>(0.02)***|
|Manager<br>experience(ln)||0.06<br>(0.01)***|0.06<br>(0.01)***|0.05<br>(0.01)***|0.05<br>(0.01)***|0.06<br>(0.01)***|0.05<br>(0.01)***|0.08<br>(0.02)***|
|Sole<br>proprietorship<br>(0/1)||-0.32<br>(0.02)***|-0.31<br>(0.02)***|-0.35<br>(0.02)***|-0.34<br>(0.02)***|-0.31<br>(0.01)***|-0.34<br>(0.02)***|-0.38<br>(0.03)***|
|Foreign ownership<br>(0/1)||0.34<br>(0.02)***|0.34<br>(0.02)***|0.37<br>(0.02)**|0.37<br>(0.02)***|0.34<br>(0.02)***|0.37<br>(0.02)***|0.35<br>(0.04)***|
|State ownership<br>(0/1)||-0.29<br>(0.05)***|-0.27<br>(0.05)***|-0.40<br>(0.07)***|-0.38<br>(0.07)***|-0.27<br>(0.05)***|-0.38<br>(0.07)***|-0.49<br>(0.12)|
|Exporter (0/1)||0.23<br>(0.02)***|0.24<br>(0.02)***|0.22<br>(0.02)***|0.21<br>(0.02)***|0.24<br>(0.02)***|0.22<br>(0.02)***|0.20<br>(0.03)***|
|Part of larger firm<br>(0/1)||0.18<br>(0.02)***|0.18<br>(0.02)***|0.14<br>(0.02)***|0.15<br>(0.02)***|0.18<br>(0.02)***|0.15<br>(0.02)***|0.13<br>(0.03)***|
|Share employees<br>with higher ed.||0.40<br>(0.02)***|0.40<br>(0.02)***|0.41<br>(0.02)***|0.41<br>(0.02)***|0.40<br>(0.02)***|0.42<br>(0.02)***|0.41<br>(0.06)***|
|Training program<br>(0/1)||0.16<br>(0.01)***|0.16<br>(0.01)***|0.15<br>(0.02)***|0.15<br>(0.02)***|0.16<br>(0.01)***|0.15<br>(0.02)***|0.16<br>(0.03)***|
|Experienced crime<br>(0-4)||||-0.02<br>(0.01)**|-0.02<br>(0.01)**||-0.01<br>(0.01)**|-0.03<br>(0.01)**|

16
|Experienced<br>outages(0-1)||||-0.02<br>(0.01)**|0.02<br>(0.01)**||0.02<br>(0.01)**|-0.01<br>(0.03)|
|---|---|---|---|---|---|---|---|---|
|Experienced inf.<br>competition(0-1)||||-0.10<br>(0.01)***|-0.10<br>(0.01)***||-0.10<br>(0.01)***|-0.09<br>(0.02)***|
|Metropolitan<br>area-level|||||||||
|Lack of mobility||||0.14<br>(0.24)|0.10<br>(0.25)||-0.45<br>(0.52)|-0.24<br>(0.54)|
|Pollution (PM 2.5,<br>μgm-3/10)||||0.05**<br>(0.02)|0.03<br>(0.02)||-0.02<br>(0.03)|0.02<br>(0.04)|
|Abs. difference<br>from ideal<br>temperature||||0.00<br>(0.01)|0.00<br>(0.01)||0.00<br>(0.02)|0.01<br>(0.01)|
|Average<br>precipitation(m)||||0.01<br>(0.01)|0.01<br>(0.01)||-0.00<br>(0.01)|0.00<br>(0.01)|
|Number of firms|51828|51828|51828|38526|38526|51828|38526|38526|
|Number of<br>metropolitan areas|649|649|649|356|356|649|356|356|
|Metropolitan-level<br>average of firm-<br>level variables|NO|YES|YES|YES|YES|YES|YES|YES|
|Country fixed-<br>effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Industry fixed-<br>effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Year fixed-effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Anderson<br>canonical corr. LM<br>(p-value)||||||0.00|0.00|0.00|
|Cragg-Donald<br>Wald F Statistic||||||2284.67|922.25|1015.43|
|Stock-Yogo<br>(critical value; 10%<br>maximal IV size)||||||19.93|19.93|19.93|
|Hansen/Sargan (p-<br>value)||||||0.09|0.66|0.25|
|Durbin-Wu-|||||||1.00|0.28|
|Hausman(p-value)|||||||||
|Standard errors in par|entheses. *|p<0.10; **p|<0.05; ***|p<0.01 (Clu|ster-robust s|tandard for|2SLS estim|ation).|

All controls have expected signs suggesting a positive association between labor productivity and the following set of firm characteristics: size, age, human capital, capital investment, managerial experience, exporting status, and foreign ownership, while state ownership and sole proprietorship are negatively associated with labor productivity. We find evidence of agglomeration diseconomies in the form of crime and informal competition.

When we re-estimate our full specifications using a Mundlak IV-estimator (Table 1, Columns 5 and 6) and 2SLS (Table 1, Column 7), the coefficient on the density variable increases, but the effect becomes (highly) insignificant. In both the Mundlak IV and 2SLS estimations, we tested whether the urban density variable is econometrically endogenous using the Durbin-Wu-Hausman 17
test, which indicated that we cannot reject the null-hypothesis that urban density is exogenous. Following earlier work by Lee and Gordon (2011), this is not surprising because urban densification can be considered a long-term process. Given the insignificance of the Durbin-WuHausman test, we use the Mundlak estimation in the remainder of this paper and consider as baseline results the ones shown in columns 3 (full sample) and 5 (common sample) of Table 1.

Although the obtained coefficients are in line with the coefficients found in the wider agglomeration economies literature focusing on the developed economies, the urban density effect is at most only weakly significant at the 10% level. Hence, although our results to some degree point to the presence of “pure” agglomeration externalities in the developing world, they also indicate that there is a lot of uncertainty around the true value of the urban density parameter in our models. One explanation for the high standard errors is the relatively small sample size at the metropolitan level and a limited number of metropolitan areas within each economy. We have information on only 649 metropolitan areas in the full sample and 356 metropolitan areas in the common sample. Hence, we are mostly looking at the top of the urban hierarchy within economies and with the inclusion of country-fixed effects, we are exploiting variation in urban density between a limited number of metropolitan areas within countries. Most likely, these small sample sizes at the metropolitan level partly explain why we find only a weakly significant effect of urban density.

At the same time, the high standard errors can indicate that the effect of urban density is heterogeneous. This idea would be supported by the highly inflated coefficients in both the Mundlak and 2SLS estimates, which capture local average treatment effects instead of average treatment effects, and an increase in coefficients indicate considerable heterogeneity in the studied population: some firms reap the benefits of agglomeration economies, while (most) other firms experience “sterile” agglomerations. They enjoy some urban benefits, but these benefits hardly outweigh the urban costs.

We find no evidence that the effects differ due to regional heterogeneity (see Appendix C). The relationship between urban density and productivity does not differ across world regions or income groups.<sup>12</sup> However, as outlined in the previous section, firm characteristics, sector tradability and urban mobility may play a role. The effects of these moderator variables will be explored in the next subsections.

> 12 These results are available upon request from the authors.

18
# _4.2. Urban density and the moderating effect of tradability_

Table 2 shows the estimations by firm type, obtained by re-estimating specification (3) and specification (5) of Table 1 and including an interaction between urban density and (a) the manufacturing sector dummy in Panel A of Table 2, (b) the exporter dummy (Panel B), and (c) the foreign ownership dummy (Panel C). In line with our hypotheses, we see that the association between urban density and labor productivity is stronger for manufacturing than services firms, and especially strong for exporting firms and foreign-owned firms. Testing by means of interaction effects shows that these differences are all statistically significant (Table 2) and they remain statistically significant even when we run a horse race between the three different moderators by including all interactions simultaneously in the model (Table 2, Panel G).

These results provide support for our hypotheses 1A-C and are in line with the discussion in Venables (2017a). He argues that firms in the tradable sector benefit more from co-location than firms in non-tradables because they are likely to exhibit increasing returns to scale in large agglomerations as they capitalize on learning externalities and better matching opportunities in denser and larger markets. However, exporters and foreign firms make up a minority share of firms in the economy. Many activities are service-oriented and do not seem to benefit considerably from the presence of large markets (given that they by nature benefit less from supply-side agglomeration economies).

The observation that the relationship between urban density and labor productivity is moderated by the exporter and foreign ownership variables may suggest that predominantly large and experienced firms enjoy agglomeration economies. After re-estimating the model using the common sample and interactions with firm size, age, and manager’s experience, the results indeed show that the urban density effect is significantly stronger for larger and older firms, and firms with more experienced management (Table 2, Panels D-F). These results are in line with previous studies showing that the association between agglomeration externalities and firm performance is contingent on the absorptive capacities of firms in local economic systems (Fritsch and Kublina, 2018; Stavropoulos et al., 2020). Small and inexperienced (relatively young) firms cannot adapt easily to new techniques and find it difficult to quickly absorb diverse forms of knowledge. By contrast, large and experienced firms have the absorptive capacity to internalize the external economies of scale and gain from agglomeration economies. We then examine whether the ability of firms to capitalize on agglomeration externalities is driven by tradability or by size and experience by including all interactions simultaneously in the regression. Our results suggest that 19
_both_ tradability and firm characteristics, such as size and experience, matter for firms’ ability to benefit from agglomeration economies (Table 2, Panels G and H).

**Table 2: The Moderating Effect of Sector and Firm Characteristics on the Relationship between Urban Density and Labor Productivity – Mundlak Estimation**

||(1)<br>Full Sample|(2)<br>Common Sample|
|---|---|---|
|**Panel A: Manufacturing**|||
|Urban density (ln)|-0.04(0.06)|0.06(0.09)|
|Urban density (ln) * Manufacturing|0.16 (0.03)***|0.18 (0.03)***|
|**Panel B: Exporter**|||
|Urban density (ln)|0.06(0.23)|0.16(0.09)*|
|Urban density (ln)* Exporter|0.23(0.04)***|0.23(0.05)***|
|**Panel C: Foreign Ownership**|||
|Urban density (ln)|0.07(0.06)|0.17(0.09)**|
|Urban density (ln)* Foreign Ownership|0.23(0.04)***|0.28(0.06)***|
|**Panel D: Firm Size**|||
|Urban density (ln)|0.09(0.06)*|0.19(0.09)**|
|Urban density (ln)* Number of employees(ln)|0.09(0.01)***|0.05(0.01)***|
|**Panel E: Firm Age **|||
|Urban density (ln)|0.09(0.06)|0.18(0.09)**|
|Urban density (ln)* Firm age(ln)|0.07(0.02)***|0.10(0.02)***|
|**Panel F: Manager Experience**|||
|Urban density (ln)|0.09(0.06)|0.20(0.08)**|
|Urban density (ln)* Manager experience(ln)|0.05(0.02)**|0.10(0.03)***|
|**Panel G: Tradability Horserace**|||
|Urban density (ln)|-0.05(0.06)|0.04(0.09)|
|Urban density (ln)* Manufacturing|0.14(0.03)***|0.16(0.05)***|
|Urban density (ln)* Exporter|0.12(0.04)***|0.16(0.04)***|
|Urban density (ln)* Foreign Ownership|0.25(0.05)***|0.24(0.06)***|
|**Panel H: Horserace**|||
|Urban density (ln)|-0.02(0.06)|0.07(0.09)|
|Urban density (ln)* Manufacturing|0.12(0.03)***|0.14(0.04)***|
|Urban density (ln)* Exporter|0.07(0.04)*|0.14(0.05)**|
|Urban density (ln)* Foreign Ownership|0.22(0.06)***|0.24(0.07)***|
|Urban density (ln)* Number of employees(ln)|0.05(0.01)***|0.01(0.01)|
|Urban density (ln)* Firm age(ln)|0.04(0.02)*|0.05(0.03)*|
|Urban density (ln)* Manager experience(ln)|0.02(0.02)|0.07(0.03)**|
|Standard errors in parentheses. *p<0.10; **p<0.05;<br>interactions have all been mean-centered. The mo<br>sector, and time-fixed effects and a Mundlak corre<br>common sample includes all firm and metro contro<br>correction,followingModels 5 in Table 1.|***p<0.01. Continuous varia<br>del using the full sample inclu<br>ction, following Model 3 in T<br>ls, country, sector, and time-|bles included in the<br>des all firm controls, country,<br>able 1. The model using the<br>fixed effects and a Mundlak|

# _4.3. Urban density and the moderating effect of urban mobility and congestion_

How does urban mobility and congestion moderate the relationship between urban density and firm productivity? The baseline regressions in Table 1 show no significant relationship between urban mobility and labor productivity. However, urban mobility issues linked to either poor 20
uncongested mobility and/or to congestion during peak business hours can still potentially reduce the agglomeration benefits. In addition, the moderating effect of urban mobility may vary between firms engaged in tradable and non-tradable activities. As indicated in our conceptual framework, the non-tradable sector depends more heavily on local consumer markets and, hence, the lack of urban mobility is expected to be more harmful to these firms than those involved in the tradable sector. At the same time, because the tradable sector in most developing countries employs relatively skilled workers, who are less dependent on local markets, the lack of urban mobility is expected to affect less the tradable activities.

We test these hypotheses by estimating two-way and three-way interactions using the _common sample_ . The results of these estimations are presented in Tables 3 and 4. From these analyses we draw the following conclusions. First, in column 1 of Table 3 we find support for Hypothesis 2A in Section 2 that, on average, the effect of urban density on firm productivity depends on mobility within metropolitan areas. This effect is driven by both uncongested mobility and congestion in that the urban density effect is considerably lower in cities with limited uncongested mobility (lowspeed cities) (Table 3, Column 2) and in highly congested cities (Table 3, Column 3). However, compared to uncongested travel speed, congestion has a stronger moderating effect on agglomeration economies as indicated by the much larger absolute value of the coefficient on the interaction term between urban density and congestion.

**Table 3: The Moderating Effect of Urban mobility and Congestion on the Relationship between Urban Density and Labor Productivity –Mundlak Estimation, Common Sample**

|**Mobility moderators:**|**Lack of mobility**|**Lack of**<br>**uncongested**<br>**travel speed**|**Congestion**|
|---|---|---|---|
|Urban density (ln)|0.08(0.10)|0.12(0.09)|0.04(0.10)|
|Lack of mobility|0.01(0.27)|-0.00(0.36)|0.16(0.60)|
|Urban density (ln) * Mobility<br>moderator|-1.17 (0.46)**|-1.05 (0.54)*|-2.51 (1.14)**|

Standard errors in parentheses. *p<0.10; **p<0.05. Variables included in the interactions have all been meancentered. All models include the firm-level controls from Table 1, Mundlak correction, and country correction, and industry and time fixed effects. N=38,526.

At the same time, urban mobility’s moderating effect on agglomeration economies is stronger for firms in the non-tradables sector compared to those in tradables (Table 4). This effect is driven by congestion (Table 4, Panel C) irrespective of the tradability dimension examined in columns 1-3 of Table 4. Hence, we cannot reject Hypothesis 2B; the agglomeration economies of manufacturing firms in the tradable sector are reduced to a lower degree by congestion than the agglomeration economies of services firms in the non-tradable sector (see also Figures 1A-1C). This finding suggests that agglomeration economies accruing to tradable firms are to a lower 21
degree contingent on congestion. These results are in line with the conjecture that firms in the non-tradable sector are more dependent on local consumer markets, while firms in the tradable sectors rely less on local markets and employ workers who are better able to locate close to their workplaces and/or afford faster transport services.

**Table 4: The Moderating Effect of Urban mobility and Congestion on the Relationship between Urban Density and Labor Productivity in Tradables – Mundlak Estimation**

|**Tradability indicator**|**Manufacturing**|**Exporter**|**Foreign-owned firm**|
|---|---|---|---|
|**Panel A: Lack of Mobility**||**Three-way interaction**||
|Urban density (ln)|0.04 (0.10)|0.06 (0.10)|0.07 (0.10)|
|Lack of mobility|0.01 (0.27)|-0.04 (0.27)|-0.02 (0.27)|
|Urban density (ln) * Tradability indicator<br>(0/1)|0.17 (0.04)***|0.13 (0.06)**|0.16 (0.08)**|
|Urban density (ln) * Lack of mobility|-1.63 (0.48)***|-1.25 (0.45)***|-1.19 (0.45)***|
|Tradability indicator (0/1) * Lack of<br>mobility|0.04 (0.10)|0.48 (0.13)***|0.56 (0.18)***|
|Urban density (ln) * Tradability indicator *<br>Lack of mobility|0.65 (0.22)***|0.71 (0.33)**|0.65 (0.42)|
|**Panel B: Lack of Uncongested Travel**<br>**Speed**||**Three-way interaction**||
|Urban density (ln)|0.00 (0.10)|0.12 (0.09)|0.13 (0.09)|
|Lack of uncongested travel speed|-0.06 (0.37)|-0.06 (0.35)|-0.01 (0.36)|
|Urban density (ln) * Tradability indicator<br>(0/1)|0.17 (0.04)***|0.15 (0.06)**|0.22 (0.07)***|
|Urban density (ln) * Lack of uncongested<br>travel speed|-1.34 (0.57)**|-1.00 (0.53)*|-0.98 (0.53)*|
|Tradability indicator (0/1) * Lack of<br>uncongested travel speed|0.11 (0.12)|0.60 (0.15)***|0.42 (0.20)**|
|Urban density (ln) * Tradability indicator) *<br>Lack of uncongested travel speed|0.38 (0.28)|0.35 (0.42)|0.22 (0.48)|
|**Panel C: Congestion**||**Three-way interaction**||
|Urban density (ln)|-0.07 (0.11)|0.03 (0.10)|0.04 (0.10)|
|Congestion|0.54 (0.62)|0.20 (0.60)|0.13 (0.60)|
|Urban density (ln) * Tradability indicator<br>(0/1)|0.17 (0.04)***|0.23 (0.07)***|0.23 (0.07)***|
|Urban density (ln) * Congestion|-4.10 (1.19)***|-2.85 (1.13)**|-2.74 (1.14)**|
|Tradability indicator (0/1) * Congestion|-0.42 (0.22)*|0.13 (0.32)|0.57 (0.36)|
|Urban density (ln) *|2.14 (0.49)***|1.79 (0.73)**|1.66 (0.85)*|
|<br>Congestion * Tradabilityindicator||||
|Standard errors in parentheses. *p<0.10; **<br>centered. All models include the firm-level c<br>and time fixed effects. Common samples,N|p<0.05; ***p<0.01. Varia<br>ontrols from Table 1, Mu<br>=38,526.|bles included in the interact<br>ndlak correction, and countr|ions have all been mean-<br>y correction, and industry|

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# **Figure 1A: Moderating effect of congestion by sector**

<!-- Start of picture text -->
-.2 -.15 -.1 -.05 0 .05 .1 .15<br>Congestion (Mean=0)<br>Services Manufacturing<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>-.4<br>Effect of Urban Density (ln)<br>-.6<br>-.8<br><!-- End of picture text -->

# **Figure 1B: Moderating effect of congestion by export status**

<!-- Start of picture text -->
-.2 -.15 -.1 -.05 0 .05 .1 .15<br>Congestion (Mean=0)<br>Non-exporter Exporter<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>-.4<br>Effect of Urban Density (ln)<br>-.6<br>-.8<br><!-- End of picture text -->

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# **Figure 1C: Moderating effect of congestion by foreign ownership status**

<!-- Start of picture text -->
-.2 -.15 -.1 -.05 0 .05 .1 .15<br>Congestion (Mean=0)<br>Domestically owned Foreign-owned<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>-.4<br>Effect of Urban Density (ln)<br>-.6<br>-.8<br><!-- End of picture text -->

Note: The data for these graphs come from the results in Table 4, Panel C (Congestion) and the x-axis is based on the full-range of the variable. Please note that we look here at the differences between slopes and not the difference in point estimates. The interaction term (presented in Table 4) tells us whether or not the marginal effect of urban density depends on the value of lack of mobility. Since this coefficient is significant, we would conclude that the interaction effect of urban density and lack of mobility depends on the sector/export/ownership status. The p-values of the point estimates answer a completely different question, namely whether the groups at a specific point in the distribution differ from each other.

# **5. Concluding remarks**

We use firm-level, geocoded data on 51,000 establishments in 649 cities in 98 developing countries, from the World Bank Enterprise Surveys and a new global database of city-level mobility and congestion indexes from Akbar et al. (2021) to unpack the relationship between urban density and firm productivity. We estimate the “pure” firm productivity gains of urban density in the developing world, net of negative externalities associated with congestion, crime, and pollution. Our results show that while on average the agglomeration economies in the developing countries are comparable in size to those in advanced economies, not all firms profit from their location in dense urban areas. The firms that reap the highest returns to urban density tend to be in the tradables sector; these are manufacturing firms, exporters, and foreign-owned firms, and larger and more experienced firms. In addition, we show that part of the ‘pure’ agglomeration benefits in developing economies are lost through poor mobility in large metropolitan areas due both to limited uncongested mobility and traffic congestion. The latter has a stronger negative effect on 24
agglomeration economies and reduces relatively more the agglomeration benefits of firms operating in the non-tradables sector than those producing tradables.

Although this paper offers new evidence on agglomeration economies in developing economies, our research has some limitations. First, our data cover mostly the largest cities within economies and do not include formal firms with fewer than 5 employees as well as informal firms. The paper also excludes firms in two important tradable sectors – resources and finance. When these data gaps are filled, our analysis could be extended to test more thoroughly the robustness of the results presented in this paper and to look at other issues that potentially affect agglomeration economies in developing economies. These include exposure to natural disasters, ecological quality, institutional environment, and spatial planning, to name a few. Finally, more detailed data at the establishment level, particularly with regard to the skills of employees and the absorptive capacity of firms, would provide us with a better understanding of the heterogeneous effects we find and whether non-tradables are predominantly losing out ‘pure’ agglomeration economies on the supply or demand side.

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