metadata: authors: 0: author: Behrens,Kristian
1: author: Brown,W. Mark
2: author: Bougna Lonla,Theophile
count: World
docna: 0: docna: The world is not yet flat : transport costs
            matter !
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,International Trade and Trade Rules,Trade and Services,Business Cycles and Stabilization Policies,Transport Services,Rural Labor Markets
teratopic: Industry,International Economics and Trade
abstracts: cdata!: The paper provides evidence of the
            effects of changes in transport costs on the geographic
            concentration of industries. The analysis uses micro-level
            commodity flow data and micro-geographic plant-level data to
            construct industry-specific ad valorem trucking rates and
            continuous measures of geographic concentration. The
            findings show that, controlling for international trade
            exposure and input-output links, increasing trucking rates
            are significantly associated with declining geographic
            concentration. The effect is large: changes in trucking
            rates explain around 20 percent of the observed decline in
            geographic concentration of Canadian manufacturing
            industries between 1992 and 2008.
repnme: repnme: The world is not yet flat : transport costs
            matter !
display_title: The world is not yet flat :
            transport costs matter !
listing_relative_url: /research/2016/10/26868018/world-not-yet-flat-transport-costs-matter
topicv3: Labor Markets m1326488 855,Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698,Trade in Services m1326928 780
publishtoextweb_dt: 2016-10-17T00:00:00Z
historic_topic: Industry,International Economics and Trade
no_of_pages: 55
author_keys: 0: author_key: 1057789
model_extractions: WPS7862 Policy Research Working Paper 7862 The World Is Not Yet Flat Transport Costs Matter!

_Kristian Behrens W. Mark Brown Théophile Bougna_ Development Research Group Environment and Energy Team October 2016
Policy Research Working Paper 7862

# **Abstract**

The paper provides evidence of the effects of changes in transport costs on the geographic concentration of industries. The analysis uses micro-level commodity flow data and microgeographic plant-level data to construct industry-specific ad valorem trucking rates and continuous measures of geographic concentration. The findings show that, controlling for international trade exposure and input-output links, increasing trucking rates are significantly associated with declining geographic concentration. The effect is large: changes in trucking rates explain around 20 percent of the observed decline in geographic concentration of Canadian manufacturing industries between 1992 and 2008.

This paper is a product of the Environment and Energy Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at tbougna@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
# The world is not yet flat: Transport costs matter!<sup>*</sup>

Kristian Behrens<sup>†</sup> W. Mark Brown<sup>‡</sup> Théophile Bougna<sup>§</sup>

**Keywords:** transport costs; trucking rates; geographic concentration; international trade exposure; input-output links.

**JEL classification:** R12; C23; L60.

*We thank the editor, Gordon Hanson, two anonymous referees, Gabriel Ahlfeldt, John Baldwin, Antoine Bonnet, Pierre-Philippe Combes, Gilles Duranton, Wulong Gu, Julien Martin, Se-il Mun, Yasusada Murata, Frédéric Robert-Nicoud, Mathieu Parenti, Eugenia Shevtsova, Peter Warda, and conference and seminar participants in many places for helpful comments and suggestions. Tim Pendergast provided excellent technical assistance. Financial support from Statistics Canada’s ‘Trade Cost Analytical Projects Initiative’ (api) is gratefully acknowledged. This project was funded by the Russian Academic Excellence Project ’5-100’. Behrens and Bougna gratefully acknowledge financial support from the crc Program of the Social Sciences and Humanities Research Council (sshrc) of Canada for the funding of the _Canada Research Chair in Regional Impacts of Globalization_ . Bougna gratefully acknowledges financial support from cirpée. This work was carried out while Behrens and Bougna were Deemed Employees of Statistics Canada. The views expressed in this paper and all remaining errors are ours. This paper has been screened to ensure that no confidential data are revealed.

> † _Corresponding author:_ Dept. of Economics, Université du Québec à Montréal, Canada; National Research University Higher School of Economics, Russia; cirpée, Canada; and cepr, UK. E-mail: `behrens.kristian@uqam.ca`

> ‡Statistics Canada, Economic Analysis Division (ead); Chief, Regional and Urban Economic Analysis. E-mail: `mark.brown@canada.ca`

> §Development Research Group (decrg), The World Bank, Washington dc. E-mail: `tbougna@worldbank.org`
# **1 Introduction**

Transport and communication costs fell precipitously during the last century, leading many observers to posit that the world has ‘become flat’, that locational differences no longer matter. With the help of cheap transport and communication, business can be done almost anywhere, or so the story goes, leaving policy makers with the impression that we have entered a ‘brave new frictionless’ world. But, if this were true, the costs of trading and transporting goods should no longer influence firms’ location choices and, thereby, the spatial structure of economic is then the for economic to cluster in still activity. Why tendency activity space strong? Why do many industries nowadays still exhibit strong geographic patterns, including new entrants that should face little locational constraints?

We address this seeming contradiction head-on by identifying the causal effect of transport costs on the geographic concentration of industries. Using micro-level commodity flow data and micro-geographic plant-level data, we construct industry-specific ad valorem trucking rates and continuous measures of geographic concentration. We find that, controlling for international trade exposure and input-output links, increasing trucking rates are significantly associated with declining geographic concentration. The effect is large: had trucking rates not fallen between 1992 and 2008, the observed decline in geographic concentration of Canadian manufacturing industries would have been about 20% greater. Our results hold up to a large variety of robustness checks and to instrumental variables estimations that deal with potential endogeneity concerns of our key variables.

Assessing empirically the impact of transport costs on the geographic concentration of industries is important for several reasons. First, it is fair to say that, despite their fundamental theoretical role in spatial modeling, little is still known empirically on how transport costs drive the geographic structure of industries. Second, among the forces that drive the clustering of industries, transport costs have been less studied, much less than the ‘Marshallian’ determinants such as input-output links, labor market pooling, and knowledge spillovers (see Rosenthal and Strange, 2004; Combes and Gobillon, 2015). Consequently, we have little quantitative evidence on the impact of those costs on the geography of industries, and on how their magnitude compares to that of the other forces. Third, transport costs certainly strongly bear on the local composition of economic activity, which is of great policy importance. Changes in transport costs, driven by, e.g., infrastructure investments, inform us on how local economic structure may change (e.g., Duranton, Morrow, and Turner, 2014). These changes play a major role in understanding how regions can weather shocks in international trading environments that have direct repercussions in local labor markets (e.g., Autor, Dorn, and Hanson, 2013; Dauth, Findeisen, and Suedekum, 2014).

Assessing empirically the impact of transport costs on the geographic concentration of industries is also a difficult task. First, we need fine measures of geographic concentration 1
across time to assess changes therein. In this paper, we employ – for the first time to our knowledge – a long panel of continuous measures of geographic concentration, computed from micro-geographic plant-level data using the approach of Duranton and Overman (2005).<sup>1</sup> Using panel data allows us to look at the time-series variation over a nearly 20 year period and to studies that have looked at the cross-sectional variation in the go beyond existing mainly geographic concentration of industries.

Second, we need detailed measures of industry-specific transport costs. We devote substantial effort to the construction of domestic ad valorem trucking rates for 257 industries and 20 years. Most of the literature has looked at infrastructure as a source of variation in transport costs (see Redding and Turner, 2015). By contrast, we build our trucking rates time series from the microdata files on truck shipments within Canada. These measures capture time-changing domestic transport costs and are invariant to the spatial structure of industry, thereby sidestepping the often endogenous nature of standard transportation measures (e.g, transportation margins from input-output accounts).

Third, as suggested by a simple general equilibrium model that we construct, to identify the causal effect of transport costs on the geographic concentration of industries we need to control for both time-varying changes in international trade exposure and general equilibrium effects of access to intermediate input suppliers and customers in vertical production chains. We tackle this rich measures of trade and challenge by developing exposure microgeographic measures of plants’ access to potential suppliers and customers.

Last, we need to deal with the possible endogeneity of our main covariates. For example, it is well documented that productivity rises as an industry concentrates geographically (see, e.g., Rosenthal and Strange, 2004; Combes and Gobillon, 2015). If these productivity gains are passed on to consumers in the form of lower prices that affect also ad valorem trucking rates, the causality may actually run from agglomeration to transport costs and not the other way round. We deal with this issue directly by purging our measure of ad valorem trucking costs of the effect multi-factor productivity. We also use us industry price indices to construct external instruments for our trucking rate series.

The remainder of the is structured as follows. Section 2 the theoretical paper presents background that informs our empirical strategy. Section 3 briefly documents the evolutions of geographic concentration and of transport costs in Canada, including a detailed discussion of the construction of the transport cost variable. Section 4 describes our empirical strategy and discusses the identification issues. Section 5 presents our results and provides a large number of robustness checks and iv estimates. Finally, Section 6 concludes. Technical details are relegated to an extensive set of appendices and a Supplementary Online appendix.

> 1See Holmes and Stevens (2004) for an exhaustive survey of location patterns in North America. They do not report results using continuous measures. Ellison, Glaeser, and Kerr (2010) use a ‘lumpy (county-level) approximation’ of the Duranton and Overman (2005) measure and apply it to us manufacturing data.

2
# **2 Theoretical background**

Our aim is to empirically assess the causal effect of changes in domestic transport costs on the geographic concentration of industries within a country. Changes in transport costs alter firms’ access to customers and suppliers, which affects many key dimensions of their economic environment and behavior: competition, input prices, plant size, output, and optimal location choices, to name but a few. It also affects potential entry in and exit from markets and industries. When aggregated across firms, these changes transform the geographic landscape of plants, employment, and output in different industries.

To formally investigate how changes in domestic transport costs affect geographic concentration, we require a framework that features costly trade between domestic and between domestic and foreign locations, vertical industry links, and geographically mobile industries. To paint a full picture also requires that changes in the competitive environment – brought about by falling transport costs – affect firms’ sizes and outputs. There is, to our knowledge, no model to date that features all these ingredients simultaneously.<sup>2</sup> We hence first develop a ‘simple’ model that allows us to evaluate the impacts of changes in domestic transport costs and international trade exposure on the geographic concentration of industries, taking into account input-output links and allowing for varying toughness of competition (see Appendix A). In our model, there are two domestic regions, labeled _r_ and _s_ , and a rest-of-the-world, labeled _R_ . Region _r_ has _Lr_ workers, and region _s_ has _Ls_ workers. Workers are also consumers, so that _Lr_ and _Ls_ denote regional market sizes. There are two vertically linked industries: a horizonally differentiated final good industry, and a horizonally

> 2The _empirical literature_ has documented that changes in transport and trade costs influence the geographic concentration and composition of economic activity through a variety of channels: market access broadly defined (e.g., Redding and Venables, 2004; Redding and Sturm, 2008; Brülhart, Carrère, and Trionfetti, 2012); infrastructure (e.g., Chandra and Thompson, 2000; Duranton, Morrow, and Turner, 2014); access to intermediates and changes in the value chain (e.g., Hanson, 1996, 1997; Holmes, 1999); tougher competition in product markets and exit dynamics (e.g., Dumais, Ellison, and Glaeser, 2002; Holmes and Stevens, 2014; Behrens, Boualam, and Martin, 2016); or any combination of these. See Redding and Turner (2015) for a recent survey of that empirical literature focusing mainly on infrastructure investments. There are only few contributions that look at transport costs more broadly defined (see, e.g., Storeygard, 2016, who interacts infrastructure with oil prices to provide time-varying measures of transport costs). The _theoretical literature_ that investigates how domestic transport and international trade costs affect the geographic concentration of industries is large and reaches ambiguous conclusions. Decreasing transport and trade costs are dispersive in the models by Krugman and Livas Elizondo (1996), Helpman (1998), and Behrens, Mion, Murata, and Südekum (2013), whereas they are agglomerative in the models by Krugman (1991), Krugman and Venables (1995), and Fujita, Krugman, and Venables (1999). Using a richer spatial structure with two countries and four regions, as well as variable markups for firms, Behrens, Gaigné, Ottaviano, and Thisse (2007) find that falling domestic transport costs are agglomerative, whereas increasing international trade exposure is dispersive within countries. The reasons underlying the diverging results in the literature are related to differences in the modeling of agglomeration and dispersion forces. See Brülhart (2011) for a review of the effects of increased trade openness on the internal geography of countries.

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differentiated intermediate good industry. Workers are geographically immobile but perfectly mobile across industries. The final industry uses labor and a continuum of intermediates. The latter are produced using labor only. Both final and intermediate goods can be shipped at a transport cost _t_ between domestic regions, and at a trade cost _τ_ between domestic regions and the rest-of-the-world. A mass _fR_ of intermediates are imported from abroad. As transport and trade costs change, so do the prices of final and intermediate goods, as well as the trade patterns and firm sizes. Furthermore, changes in _t_ and _τ_ affect the masses of final firms ( _Nr_ and _Ns_ ) and the masses of intermediate firms ( _nr_ and _ns_ ) located in each region. In other words, the spatial structure of economic activity changes as the costs of trading goods domestically and internationally change.

Although the model is highly stylized, it is rich enough to preclude a full analytical investigation. Solving it explicitly and deriving paper-and-pencil comparative static results is, therefore, beyond the scope of this paper. We hence simulate it to guide our empirical analysis. The simulations provide information on: (i) the expected sign of key coefficients; and (ii) what controls are required to isolate the key effect of interest, namely the relationship between changes in domestic transport costs and the spatial concentration of industries. One important question is how we can evaluate the latter. Because we use the count-based Duranton and Overman (2005) measure of geographic concentration in our empirical analysis – which is computed using the distribution of bilateral distances between plants (see the Supplementary Appendix S.1) – a natural counterpart in our simple model is the distribution of plants across Since the market has more – as it has a size – we measure regions. larger plants advantage the geographic concentration of industries by the share of plants located in the larger region in excess of that region’s share of total market size. Formally, we use the following measures: where _L_ = _Lr_ + _Ls_ is the total population; _N_ = _Nr_ + _Ns_ is the total mass of final firms; and _n_ = _nr_ + _ns_ is the total mass of intermediate firms. The first two measures in (1) capture the over-representation (positive values) or under-representation (negative values) of final and intermediate plants in the larger region. The last measure in (1) reports the same information for both industries jointly, i.e., the aggregate spatial distribution of economic activity.

The key question of interest is how the geographic concentration of both industries across regions, as measured by (1), changes with _t_ and _τ_ . Table 1 below summarizes a series of results for different sets of parameter values of the model.

As can be seen from the top panel (a) of Table 1, decreasing domestic transport costs _t_ lead to the geographic concentration of the final goods sector (column 1), slightly more geographic dispersion of the intermediate sector (column 2), and more geographic concentration overall (column 3). As can be seen from the middle panel (b), decreasing international trade costs _τ_ have the opposite effect. They lead to more geographic dispersion of the final good sector 4
Table 1: Changes in geographic concentration with respect to _t_ , _τ_ , and _fR_ .

||||(1)|(2)|(3)||
|---|---|---|---|---|---|---|
|Para<br>_t_|meter v<br>_τ_|alues<br>_fR_|Agglomeration fnal<br>�<br>_Nr_<br>_N_ <sup>_−Lr_</sup><br>_L_<br>�<br>_×_100%|Agglomeration interm.<br>�<br>_nr_<br>_n_ <sup>_−Lr_</sup><br>_L_<br>�<br>_×_100%|Agglomeration both<br>�<br>_Nr_+_nr_<br>_N_+_n_ <sup>_−Lr_</sup><br>_L_<br>�<br>_×_100%|Relative wage<br>_ws_/_wr_|
||||<br><br>|<br><br><br>_(a) Changes in domestic_|<br><br><br>_transport costs, t_||
|1.5|1.5|0.2|0.279|-0.452|-0.037|0.980|
|**1.4**|**1.5**|0.2|**1.010**|**-0.617**|**0.268**|**0.981**|
|1.35|1.5|0.2|1.396|-0.731|0.408|0.981|
|1.3|1.5|0.2|1.823|-0.877|0.554|0.982|
|1.25|1.5|0.2|2.364|-1.082|0.731|0.983|
|||||_(b) Changes in internati_|_onal trade costs, τ_||
|1.4|1.6|0.2|2.266|-0.912|0.814|0.975|
|**1.4**|**1.5**|0.2|**1.010**|**-0.617**|**0.268**|**0.981**|
|1.4|1.4|0.2|0.915|-0.600|0.226|0.981|
|1.4|1.3|0.2|0.851|-0.594|0.199|0.982|
|1.4|1.2|0.2|0.790|-0.590|0.173|0.982|
|||||_(c) Changes in imported_<br>|_intermediates, fR_||
|1.4|1.5|0.1|1.036|-0.616|0.275|0.981|
|**1.4**|**1.5**|**0.2**|**1.010**|**-0.617**|**0.268**|**0.981**|
|1.4|1.5|0.3|0.986|-0.616|0.259|0.981|
|1.4|1.5|0.4|0.961|-0.616|0.250|0.981|
|1.4|1.5|0.5|0.935|-0.615|0.241|0.981|

_Notes:_ Simulations for the baseline parameter values _α_ = 0.6, _σ_ = 5, _β_ = 0.33, _Lr_ = 11, _Ls_ = 10, _F_ = 1, _LR_ = 1, _λR_ = 0.3, _fR_ = 0.2, and _cR_ = 1. The baseline case with _t_ = 1.4 and _τ_ = 1.5 is displayed in bold font. All simuations have been carried out using Mathematica 10. The notebook is available online as supporting material. See Appendix A for details on the model structure.

(column 1), slightly more geographic concentration of the intermediate sector (column 2), and less geographic concentration overall (column 3). As can further be seen from the bottom panel (c) of Table 1, changes in the mass of imported intermediates also have a dispersive effect, this time on both sectors, although that effect is clearly smaller in magnitude. Last, although we do not explicitly summarize it in Table 1, it should be clear that the equilibrium level of geographic concentration depends on the model’s structural parameters – the strength of scale economies, consumers’ preferences for the goods, and the cost structure in each sector.

Our key qualitative findings can be summarized as follows: (i) decreasing domestic transport costs lead to more geographic concentration; (ii) decreasing international trade costs lead to more geographic dispersion; (iii) changes in the trading environment of one industry induce changes in the geographic concentration of vertically linked industries; and (iv) industryspecific fixed parameters related to the degree of scale economies, costs, and consumers’ preferences influence the degree of geographic concentration.<sup>3</sup> What are the empirical implications of the foregoing results? Three conclusions can be drawn for our empirical exercise. First, given the opposite effects of domestic transport and

> 3Results (i), (ii), and (iv) are qualitatively in line with those obtained by Behrens _et al._ (2007) in a two-country four-region model with quadratic quasi-linear preferences. Contrary to our model, there are no vertically linked industries in their model.

5
international trade costs, changes in the geographic concentration of industries can go either way, depending on the relative magnitude of changes in these costs. Since domestic transport and international trade costs are partly determined by the same underlying forces – international trade still requires domestic transportation at some point – any analysis of the changes in transport costs on the geographic concentration of industries needs to control for changes in international trade costs broadly defined. We will control for these costs using measures of trade exposure – both for imports and exports and for different groups of countries – though these are only proxies for international trade costs since they are an amalgam of transport costs, foreign productivity, and other forces.

Second, changes in the trading environment of one sector, intermediates in this case, have a direct impact on the geographic structure of the other sector, final in this case. As the mass of imported intermediates increases, holding trade and transport costs fixed, the final sector disperses more although it is not directly but only indirectly affected (through the input-output links). This suggests that we need to control for general equilibrium effects that are channeled through the impacts of changes in the geographic structure of some sectors on the other sectors. We will control for these effects novel measures equilibrium by constructing microgeographic that proxy access to potential local inputs and outputs.

Last, a large set of parameters that are fairly stable over time influence the degree of geographic concentration across industries in any cross section. To control for these factors, we will use panel data and focus on changes and not on levels. Doing so obviously does not solve the problem of industry-specific time-varying unobservables, but it is a step forward compared to purely cross-sectional analyses of the determinants of geographic concentration that most of the literature (e.g., Rosenthal and Strange, 2001; Ellison _et al._ , 2010) has used until now (yet, see Storeygard, 2016, for an approach using time-varying proxies for transport costs).

# **3 Measurement and descriptive evidence**

Our analysis requires two key pieces of information: (i) a measure of geographic concentration of industries; and (ii) a measure of transport costs of industries. To control for unobserved factors that drive cross-sectional differences in geographic concentration and transport costs, we also require a panel dimension. We now discuss the data and the procedures that allow us to construct these key empirical measures. We also take a first descriptive look at the data.

## **.1 3 Geographic concentration**

We draw on Statistics Canada’s Annual Survey of Manufacturers (asm) Longitudinal Microdata file from 1990 to 2009. This file contains between 32,000 and 53,000 manufacturing plants per year, covering 257 naics 6-digit industries. For every plant, we have information about: its 6
primary naics industry; its employment; its sales; and its 6-digit postal code. The latter allows us to effectively geo-locate the plants using latitude and longitude coordinates of postal code centroids, which are spatially very fine-grained in Canada. A detailed description of the data 1. and the data sources is provided in Appendix B.

We measure the geographic concentration of industries using the Duranton and Overman (2005; henceforth, do) _K_ -densities. Since this approach has become fairly standard by now (see, e.g., Murata, Nakajima, Okamoto, and Tamura, 2014; Behrens and Bougna, 2015; Kerr and Kominers, 2015), we relegate the technicalities to the Supplementary Appendix S.1. The do _K_ -densities are kernel-smoothed distributions of the bilateral distances between plants in an industry. We compute them year by year for all industries at the naics 6-digit level.<sup>4</sup> Since _K_ -densities are distribution functions, we can also compute their cumulatives (cdf) up to some distance _d_ . The cdf of the _K_ -density at distance _d_ provides a measure of the share of plants in an industry that are located at most at distance _d_ from each other. Kerr and Kominers (2015) develop a model where the clustering of firms is driven by spatial interactions between pairs of plants and is subject to distance decay and (fixed) interaction costs. They show that the _K_ -densities can be within that framework and that information on interpreted they provide the ‘overall degree’ of geographic concentration of industries and on ‘cluster shapes’.

Table 2: Mean of the Duranton-Overman cdfs across industries, 1990 and 2009.

|||(1) Un|weighted||(2)|Employm|ent weigh|ted||(3) Sales|weighted||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||cdfat a|distance of||||||
|Year|10km|50km|100km|500km|10km|50km|100km|500km|10km|50km|100km|500km|
|1990|0.020|0.076|0.139|0.420|0.021|0.083|0.151|0.449|0.022|0.086|0.156|0.453|
|2009|0.013|0.056|0.107|0.373|0.015|0.063|0.121|0.397|0.017|0.068|0.126|0.403|
|Mean (1990–2009)|0.015|0.064|0.121|0.394|0.017|0.073|0.136|0.422|0.019|0.077|0.141|0.428|
|% change|-36.0%|-27.1%|-22.6%|-11.3%|-28.7%|-23.3%|-20.3%|-11.4%|-21.5%|-21.2%|-19.3%|-11.0%|

_Notes:_ Authors’ computations based on the Annual Survey of Manufacturers Longitudinal Microdata file, 1990–2009. We report the values for the starting and the end years only since the series in between decrease rather smoothly (see our cepr discussion paper version for the full table). The means of the cdf are based on 257 industries and are not weighted (but the cdfs for each industry are weighted by either employment in the middle columns (2), or by sales in the right columns (3); see Supplementary Appendix S.1 for details). ‘Mean (1990–2009)’ refers to the mean of the _K_ -densities over the whole 1990–2009 period, on a yearly basis. ‘% change’ is the percentage change between 1990 and 2009.

The standard _K_ -densities are computed based on plant counts, i.e., distances between pairs of plants without any weighting scheme. This relates to the theoretical measures we have used in our model as given by (1). Yet, we can also compute weighted versions. In particular, we can weight pairs of plants by either plant-level employment or plant-level sales. For these weighted versions of the _K_ -density the unit of observation is the employee or a dollar of sales. A value of 0.086 at 50 kilometers using sales weights (see panel (3) of Table 2 for 1990) means, for example, that 8.6% of the industry sales are generated in plants that are at most 50 kilometers

> 4Table 9 in the Supplementary Appendix S.2 summarizes the _K_ -densities for the geographically most concentrated industries in 1990, 1999, and 2009, respectively.

7
from each other.

Table 3: Summary statistics for _K_ -densities and transport costs.

||Industry|Mean|Sta|ndard devia|tion|
|---|---|---|---|---|---|
|Variable|detail||Overall|Between|Within|
|Duranton-Overman_K_-density cumulative (cdf) at 10km|6-digit|0.015|0.031|0.023|0.021|
|Duranton-Overman_K_-density cumulative (cdf) at 50km|6-digit|0.065|0.060|0.050|0.034|
|Duranton-Overman_K_-density cumulative (cdf) at100km|6-digit|0.120|0.085|0.074|0.042|
|_Ad valorem_truckingcosts as a share ofgoods shipped(avg. load, shipped 500km)|_L_-level|0.034|0.035|0.030|0.005|

_Notes:_ Based on the sample that we use in our regression analysis, which includes 4,369 observations covering 257 industries and 17 years. The standard deviation is decomposed into between and within components, which measure the cross sectional and the time series variation, respectively. Additional information regarding our data sources and the construction of our key variables is provided in Appendix B.

Tables 2 and 3 provide descriptive statistics for our measures of geographic concentration. As can be seen from panel (1) of Table 2, manufacturing has geographically dispersed in Canada between 1990 and 2009.<sup>5</sup> The average value of the cdf at 50 kilometers distance has decreased by 27.1% over a twenty year period. In other words, while 7.6% of the bilateral distances between the plants in the ‘average industry’ were less than 50 kilometers in 1990, only 5.6% of those distances still remained in that distance range in 2009. Table 2 further shows that concentration has decreased more at shorter distances: are but plants dispersing, less so at longer distances.<sup>6</sup> This finding suggests that the incentives for plants to locate in very close spatial proximity to each other are weakening over time. It also likely reflects the fact that manufacturing industries have been bid out of cities because of higher land and labor costs there, and that they are moving to smaller nearby urban, sub-urban, or rural areas as a consequence (see, e.g., Henderson, 1997). As can further be seen from Table 2, that trend also affects the employment-weighted and the sales-weighted measures, but to a lesser extent. This is consistent with the fact that geographic concentration is stronger in terms of employment than in terms of plants, and even stronger in terms of sales than in terms of employment, especially at short distances (see Figure 6 in the Supplementary Appendix S.2). Last, Table 3 shows that although the bulk of the variation in the _K_ -density cdfs is cross sectional, there is also 5This finding is consistent with the evidence reported in Behrens and Bougna (2015) and Behrens, Boualam, and Martin (2016), who have shown – using a different dataset – that the geographic concentration of manufacturing industries has decreased over the first decade of the years 2000 in Canada.

> 6Whereas the cdf of the _K_ -density is easily interpretable and provides a natural measure to track the changing geographic concentration of industries, it cannot tell us anything about whether or not industries are statistically significantly concentrated or not. Table 10 in the Supplementary Appendix S.2 summarizes location patterns by year, based on their statistical significance (see Duranton and Overman, 2005, and the Supplementary Appendix S.1 for more details). We find that the share of statistically significantly concentrated industries has decreased over our study period, thus confirming the downward trend in the _K_ -density cdfs. In a nutshell, there is a clear trend towards less geographic concentration, and that trend is captured by both the cdfs and the statistical tests.

8
substantial time-series variation. This variation will allow us to identify the effect of changes in transport costs on changes in geographic concentration.

To summarize, the descriptive evidence points to a significant decrease in the geographic concentration of manufacturing industries in Canada over the last 20 years, no matter whether that geographic concentration is measured in terms of plant counts, employment, or sales. The pace of decline, however, likely differs across industries in systematic ways. Understanding which factors drive that decrease to what extent and for which industries – with a special focus on transport costs – is the key objective of the remainder of this paper.

# **.2 3 Transport costs**

The second key ingredient of our analysis is an industry-specific measure of transport costs. Contrary to most existing studies, we use _direct measures_ constructed from detailed micro-data files on shipments within Canada.

To estimate ad valorem rates, we first use a model to predict trucking firm (carrier) revenues for a 500 kilometers trip by commodity for the average tonnage using shipment (waybill) data from Statistics Canada’s Trucking Commodity Origin-Destination Survey (see Brown, 2015, for details). We estimate the ‘prices’ charged by trucking firms as a function of distance shipped, tonnage, and a set of commodity and firm fixed effects. To begin, we assume firms set prices such that both fixed and variable (linehaul) costs are just covered. Firms are assumed to set prices based on a fixed component and kilometers shipped: _Rm_ , _kc_ = _α_ + _βdk_ , where _Rm_ , _kc_ is the revenue earned by carrier _m_ for shipment _k_ composed of commodity _c_ , _α_ is the fixed price component, _β_ is rate per kilometer, and _dk_ is the distance shipped. Of course, firms may also price on a per tonne-km basis and this needs to be taken into account. Assuming firms set prices based on an unknown average tonnage _t_<sup>_∗_</sup> shipped implies that the rate per tonne-km is _Rm_ , _kc_ = _α_ + _β_ ( _dk_ / _t_<sup>_∗_</sup> ) _t_<sup>_∗_</sup> . For loads less (greater) than _t_<sup>_∗_</sup> the implicit price per tonne-km will be scaled upward (downward) to ensure that the price on a per-km basis is maintained, which is captured by the following function: where _tk_ is the actual tonnage shipped and _φ_ ( _t_<sup>_∗_</sup> _− tk_ ) is the scaling factor. Factoring out the known tonnage results in a flexible function that allows firms to price using either rule (or some hybrid of the two). Equation (2) can be estimated using a simple quadratic form The model is further augmented with commodity and carrier fixed effects and a series of controls to account for the quarter the shipment was made, the effect of empty backhauls on 9
prices, and fuel prices. The model is estimated across three types of carriers – truck-load, less-than-truck load, and specialized – with the estimated rate being the weighted average by value of the three by commodity (see Brown, 2015, for a detailed discussion of the data and model). Because prices are measured on a quarterly basis, fixed and variable costs are to time.<sup>7</sup> Prices to are permitted vary through charged shippers predicted by commodity using their average tonnage for a 500 kilometers trip.<sup>8</sup> The final predicted price, _R_<sup>�</sup> _c_ , _t_ , is the weighted average by value of the three carrier types by commodity and year. While, as will become apparent, we only use predictions from 2008, the entire period is used in order to bring as much information to bear on the cross-sectional estimates by commodity. Predictions from the model closely match observed annual prices on aggregate.

Finally, the predicted prices _R_<sup>�</sup> _c_ , _t_ are converted to ad valorem trucking rates _T_<sup>�</sup> _c_ , _t_ by measuring the value of shipments. Since the value of shipments are not reported, they have to be estimated by multiplying the average tonnage shipped for each commodity by their respective value tonne derived from an trade in 2008. The per ‘experiment export file’ produced only ad valorem estimate at the commodity level in 2008 is _Tc_ ,2008 = _R_<sup>�</sup> _c_ ,2008/ _Vc_ ,2008, where _Vc_ ,2008 is the estimated value of the commodity for the average tonnage shipped in 2008. Using an industry-commodity concordance, the ad valorem transportation rates in 2008 for commodities _Tc_ ,2008 are aggregated to an industry basis _Ti_ ,2008.

Finally, to generate a time series, yearly trucking industry price indices **P** trans, _t_ and manufacturing industry price indices **P** _i_ , _t_ from Statistics Canada’s klems database are used to project the ad valorem rates backwards and forwards in time, thereby creating an industry-specific ad valorem transportation rate time series: Observe that our measure (4) of transport costs is more direct and detailed than those usually used in the agglomeration literature.<sup>9</sup> Yet, it is by construction unlikely to be fully exogenous to industrial location patterns since it depends on price indices. We return to this important point later. Note, however, that we estimate transport costs for a ‘representative shipment’ by truck, holding distance fixed at 500 kilometers. Hence, variable shipping distances that result from optimal location choices of plants in an industry have a priori no direct Figure 1 depicts the year-on-year changes in the (unweighted) cross-industry average transport costs for a representative 500 kilometers shipment. As can be seen, transport costs are

> 7Time enters as trends through a spline with knots set to reflect changing trends in a trucking price index generated from the same file.

> 8While we do not directly control for the time costs of transportation they will be, at least partially, embedded in the transport prices (which would capture quality of service for time-dependent trips).

> 9One exception is the work by Combes and Lafourcade (2005), who provide detailed estimates of transport costs for France. Their costs are, however, not industry specific.

10
Figure 1: Changes in average ad valorem trucking costs in Canada.

<!-- Start of picture text --> 1990 1995 2000 2005 2010<br>Year<br>.038<br>.036<br>.034<br>Mean transportation cost (500 km, truck)<br>.032<br><!-- End of picture text --> first decreasing – due, essentially, to reductions in labor costs at constant fuel prices – and then increasing – due, essentially, to increasing fuel prices at constant labor costs. They range from about 3.8% of the value of the shipment in the early nineties, to about 3.2% in the mid-nineties, with an average value of 3.4% (see Table 3). These figures are fairly close to the average ad valorem rates of 5% reported by Glaeser and Kohlhase (2004, p.206) using 2002 us data. As in their case, there is significant cross-industry variation in ad valorem trucking rates in our data. Table 11 in the Supplementary Appendix S.3 provides additional information on the highest and lowest ad valorem transport costs from 1992 to 2008. The highest ad valorem transport costs are for industries with low value to weight ratios (e.g., Cement Manufacturing and Breweries), with an average ad valorem transport cost across the top 10 industries in 2008 of 14%. The lowest are in industries with high value to weight ratios (e.g., Computer and Peripheral Equipment Manufacturing, and Medical Equipment and Supplies Manufacturing), with an average ad valorem transport cost in 2008 across the bottom 10 industries of 0.4%.

# **4 Empirical approach**

We now discuss the empirical model that we estimate and the different identification concerns that we need to address.

## **.1 Model 4**

The patterns documented in Section 3.1 reveal that there is a clear trend toward the geographic dispersion of industries. As further documented in Section 3.2, transport costs have 11
also changed. To identify the causal effect of changes in the latter on changes in the former, we now turn to multivariate analysis. We work at the industry-year level and take advantage of the panel nature of our data. More precisely, we estimate the following model: where _γi_ , _t_ ( _d_ ) is the _K_ -density cdf for industry _i_ in year _t_ at distance _d_ ; _Ti_ , _t_ is our measure of ad valorem transport costs (4) of industry _i_ in year _t_ ; **X** _i_ , _t_ is a vector of time-varying industry controls measures of international trade and links between (including exposure input-output industries; see Appendices B.2 and B.3 for details on how we construct those measures); _αt_ and _μi_ are year and industry fixed effects, respectively; and _εi_ , _t_ is the error term.<sup>10</sup> The latter is assumed to be independently and identically distributed with the usual properties for consistency of ols. Our main coefficient of interest is _βT_ , which captures the impact of changes in transport costs on changes in the geographic concentration of industries.

# **.2 4 Identification issues**

In order for _βT_ to capture the causal effect of changes in transport costs on geographic concentration, we need to address a number of identification issues. In general, the two main that the of the determinants of concentration are problems plague identification geographic omitted variables and reverse causality (see Combes and Gobillon, 2015, for a recent survey). All studies based on cross-sectional industry data (e.g., Rosenthal and Strange, 2001; Ellison _et al._ , 2010) are potentially prone to these identification problems and use different strategies to overcome them. We first discuss the omitted variables problem and explain the large number of controls that we include in our panel estimations of (5) to deal with it. We then turn to the reverse causality problem and detail our instrumentation strategies. Table 8 in Appendix B.1 summarizes our main variables and provides descriptive statistics.

> 10One may be worried that identification in (5) comes from the _within_ variation in the data. The latter could be small given yearly data, especially for the spatial variables. This point has been made in other studies (e.g., Ellison _et al._ , 2010, p.1200), but those studies usually use more aggregated measures of agglomeration. Those measures change much more slowly over time than the _K_ -densities, especially at short distances. The reason is that our microgeographic measures are constructed from geocoded data, and that there is a lot of churning (entry and exit) at short distances. This is not picked up by spatially more aggregated measures, but it is by ours. Substantial plant-level churning creates a tension. One the one hand, there is _a lot of year-on-year variation_ , which allows for identification using within variation (see Table 3). On the other hand, there is also _a lot of noise_ at a small geographical scale, which makes the estimates less precise. The _K_ -density cdfs are kernel-smoothed across distances, which reduces the noise due to plant-level churning (see our cepr discussion paper version for additional details). Smoothing is also a standard procedure when working with the asm longitudinal files (e.g., Statistics Canada usually uses three-year moving averages to filter noise in these series).

12
**Omitted variables.** We control for a wide range of time-varying industry-specific agglomeration factors. We know since Marshall (1890) that industries may concentrate for various reasons that are unrelated to the transport costs of goods, but linked to the costs of transporting people and ideas (see Duranton and Puga, 2004, for a review). Knowledge spillovers and labor market pooling are among the most important ‘Marshallian’ factors. Industries may also concentrate geographically because of localized natural advantages like natural resources or raw materials. The importance of controling for these has been pointed out, among others, by Kim (1995), Ellison and Glaeser (1999), and Ellison _et al_ . (2010). Furthermore, various other characteristics linked to the industrial organization of industries – industry size, the mean plant size, the size distribution of in the the of multi-unit or owner- plants industry, presence firms, foreign ship – are also likely to affect their spatial structure (see, e.g., Rosenthal and Strange, 2003). We account for these time-varying factors as follows. First, we control for knowledge spillovers using as a proxy an industry’s research and development (R&D) intensity, i.e., the ratio of R&D expenditure to total output of that industry (see Rosenthal and Strange, 2001). Second, we employ a proxy related to workers’ educational attainment to control for aspects of labor market pooling. More specifically, we use the share of hours worked by all workers with postsecondary education in the total number of hours worked in the industry.<sup>11</sup> Third, we control for the importance of natural advantages using the share of inputs from natural resource-based industries, and the sectoral energy inputs as a share of total sector output. Last, we control for basic industry structure and scale effects by including the following controls: total industry employment; mean plant size; the Herfindahl index of firm-level concentration; the share of plants controlled by multi-unit firms; and the share of plants controlled by foreign firms. These variables proxy for sectoral differences in the size distribution of firms, for potential differences in the location patterns of multi-unit multinationals, and differences in ‘business culture’.

As suggested by our theoretical model in Section 2 and Appendix A, controlling for inputoutput links and changes in international trade costs is important. We construct two controls that proxy for the costs of international trade and the proximity to customers and suppliers. Concerning the former, we construct measures of industry-level trade exposure (exports and imports), broken down by broad country groups – nafta, oecd excluding nafta, and low-cost countries. in international trade in international Changes exposure broadly capture changes trade costs, as well as changes in foreign productivity and the competitive environment in 2. general. We provide details and descriptive statistics in Appendix B. We also discuss possible endogeneity concerns there. Turning to the proximity to customers and suppliers, we construct 11We also constructed proxies for labor market conditions using the non-production to production workers ratio and other educational characteristics of the workforce. The latter are available at a more aggregated industry level ( _L_ -level) from Statistics Canada’s klems database (e.g., the share of hours worked by all workers with a university degree, and the labor productivity index). These measures, however, are not statistically significant in the time series because they change quite slowly over time and are thus soaked up by the industry fixed effects.

13
novel microgeographic measures that are input-output share weighted distance measures to the closest potential customers and suppliers. These measures capture how close an industry is to other vertically linked industries from which it buys or to which it sells. We provide details on how we construct those measures, their descriptive statistics, and a discussion of their possible limitations and remaining endogeneity concerns, in Appendix B.3.<sup>12</sup> Finally, the panel structure of our data allows us to control for industry-specific timeinvariant factors and general macroeconomic trends. The former is important since, as suggested by the model in Section 2 and Appendix A, unobserved industry characteristics map into sizable cross-sectional differences in the geographic concentration of industries (see also Table 3, which shows that the major share of the variance in the _K_ -densities is cross sectional). The latter captures, at least partly, general trends that affect the geographic concentration of industries (e.g., technological change due to improvements in itc that could make economic activity more footloose). Our broad range of time-varying controls, when combined with fixed effects, will capture many factors that may drive changes in geographic concentration of industries that are unrelated to changes in transport costs.

**Reverse causality.** Neither the panel structure of the data nor the controls help with potential problems of reverse causality. The latter may affect our key variable of interest, transport costs, which – as a price – reflect both demand and supply conditions in the market. A first problem is that productivity rises as an industry concentrates geographically (see, e.g., Rosenthal and Strange, 2004; Combes and Gobillon, 2015). Because our measure of transport costs is computed on an ad valorem basis and includes the industry price index, the causality may run from agglomeration to lower prices and, therefore, lower ad valorem transport costs. At the same time, agglomeration may lead to imbalances in shipping patterns, and the latter may increase the cost of transportation due to standard logistics problems like ‘backhaul’ of empty trucks (e.g., Jonkeren, Demirel, van Ommeren, and Rietveld, 2009; Behrens and Picard, 2011; Tanaka and Tsubota, 2016). Agglomeration would thus increase the transportation price index and bias our estimates. In a nutshell, **P** trans, _t_ / **P** _i_ , _t_ in expression (4) is likely to be endogenous to the degree of geographic concentration of an industry, with stronger concentration increasing that ratio due to a combination of rising freight prices and lower output prices. Thus, the ols estimate of _βT_ is likely to be upward biased in our model.<sup>13</sup>

12As explained in Appendix B.3, our proxies for input-output links are reduced form and not structural (unlike, e.g., the ‘structural supplier and market access’ in Redding and Venables, 2004). As pointed out by Combes and Gobillon (2015, p.274), there is generally no satisfying solution to control for supplier and market access in empirical estimation. Either we use a structural model – which requires a lot of assumptions and has its own limitations – or we use reduced-form proxies that aim at capturing those interactions.

> 13Industries that concentrate geographically are also likely to ship their output over different distances than industries that are less agglomerated. This problem does, however, not affect our estimates since our measure of transport costs is constructed for a representative shipment over a fixed distance of 500 kilometers.

14
To deal with those problems, we adopt three different strategies. First, we clear out the effect of – one source of – on our productivity growth presumed endogeneity prices by regressing transport cost series on industry multi-factor productivity indices (from the klems database), as well as industry and year fixed effects. We then use the residual from that regression as a proxy for the transportation cost series. By definition, that residual is orthogonal to any productivity-driven price changes that could stem from the changing geographic concentration of industries. This strategy does not deal directly with the transportation price index.

Second, we use us manufacturing industry price indices as external instruments for the transport cost series. This instrumentation strategy is similar to that of Ellison _et al._ (2010), who instrument the us input-output tables and the us industry labor requirements with those of the uk. The underlying idea is the following. Assume that the geographic concentration of an industry increases over time because of unobserved factors that we cannot control for in our analysis. This increasing geographic concentration then raises ad valorem transport costs via price decreases of the industry’s output. Provided that the changes for the us are not driven by the same unobserved factors that affect the spatial concentration of the industry in Canada, but that the us price series **P**<sup>US</sup> trans, _t_<sup>/</sup><sup>**P**US</sup> _i_ , _t_<sup>arecorrelatedwiththechangesin</sup><sup>**P**trans,</sup><sup>_t_/</sup><sup>**P**</sup><sup>_i_,</sup><sup>_t_,theywill</sup> provide valid instruments for the Canadian transport cost series. Two potential limitations of these instruments are the following: (i) there may be common underlying unobserved factors that drive changes in the concentration of the same industries in Canada and in the us; or (ii) the geographic concentration of an industry in Canada affects directly the productivity – and, therefore, the price indices – in the us. While we cannot completely rule out those possibilities, neither strikes us as very plausible. First, the panel nature of our data and the extensive set of time-varying controls should pick up most of the unobserved factors that may drive the increasing concentration of the industry; and second, the Canadian economy is small to the us so that in the concentration in Canada are compared economy, changes geographic very unlikely to have substantial productivity impacts in the us.<sup>14</sup> Last, as we have a large number of industries and a fairly long time series, our setting lends itself reasonably well to the construction of internal instruments. We implement the method suggested by Lewbel (2012), which exploits heteroscedasticity and variance-covariance restrictions to obtain identification with 2sls when some variables are endogenous and when external instruments are either weak or not available. C more details on Appendix provides this method. We use this approach to deal with remaining potential endogeneity issues in our trade exposure and input-output distance measures (see Appendices B.2 and B.3 for a

> 14The elasticity of productivity to the density or size of economic activity is usually in the 3–8 percent range. Hence, huge changes in the geographic structure are needed to obtain large productivity changes. Furthermore, given that the Canadian economy is ten times smaller than the us economy, shocks to Canadian productivity have very limited effects on the us, safe for a couple of states relatively close to the border or a couple of borderspanning industry networks (like the automotive industry). Excluding the automotive industries (naics 3361, 3362, and 3363) has no qualitative effect on the ‘ad valorem trucking cost (residual)’ coefficient.

15
discussion). We continue to use the external us instrument for transport costs. We view this approach as an additional robustness check – that takes care of possible endogeneity issues with our trade and input-output controls – on top of the previous two strategies that rely on either ‘filtering’ or the use of external instruments and standard 2sls iv.

It should be clear that is very difficult to fully solve all endogeneity issues, given the sectoral level of aggregation at which we work. Yet, the panel nature of our data – which allows for the inclusion of industry fixed effects – our extensive set of time-varying controls, as well as the construction and instrumentation strategies for our main variable of interest, transport costs, all help us to be reasonably confident that we identify causal effects of changes in transport costs _Ti_ , _t_ on our measure _γi_ , _t_ ( _d_ ) of geographic concentration.

# **Results 5**

We first estimate several variants of equation (5), which differ by the sets of industry characteristics and controls they include.<sup>15</sup> We only report results using the unweighted do _K_ -density cdf as our measure of concentration. the or geographic Using employment sales-weighted measures yields similar results (see Table 13 in the Supplementary Appendix S.4).

## **.1 Baseline results 5**

Table 4 summarizes our baseline results. **Model 1** reports the raw unconditional correlation without controls, whereas **Model 2** reports within estimates without controls. As can be seen, the coefficient on the trucking costs is negative and highly significant. In words, falling trucking costs are associated with the geographic concentration of industries. We then progressively add in **Models 3** to **6** the variables suggested by the model developed in Section 2 to control for international trade exposure, input-output links, and other industry characteristics.

Starting with **Model 3** , we add our trade variables (import and export exposure by country groups) to the baseline case. As can be seen, rising import shares are across the board associated with falling geographic concentration. The (non-oecd) Asian share of imports, which we use as a proxy for low-wage countries, has the largest estimated coefficient in absolute value and is the most statistically significant. One explanation for the dispersive effect of import competition is that firms become more footlose as they source a larger share of their intermediates from abroad and no longer rely on (possibly localized) domestic suppliers. Another explanation, for which Holmes and Stevens (2014) provide empirical evidence, is that import

> 15We performed a Hausman test for (5) to confirm that the appropriate estimator is a fixed-effects estimator and not a random-effects estimator. The result of the test strongly confirms (at the 1% level) that the fixed-effects estimator is the preferred specification. Note also that we work with the universe of manufacturing industries, so that there is no sampling variability in industries that would warrant the use of the random-effects estimator.

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Table 4: Baseline estimation results for specification (5).

|Variables|**(Model 1)**|**(Model 2)**|Dependent<br>**(Model 3)**|variable is the<br>**(Model 4)**|cdfat50km<br>**(Model 5)**|<br>**(Model 6)**|**(Model 7)**|
|---|---|---|---|---|---|---|---|
|Ad valorem trucking costs|-0.265<sup>_a_</sup>|-0.337<sup>_b_</sup>|-0.263<sup>_b_</sup>|-0.250<sup>_b_</sup>|-0.183<sup>_b_</sup>|-0.208<sup>_b_</sup>||
||(0.037)|(0.155)|(0.122)|(0.098)|(0.078)|(0.088)||
|Ad valorem trucking costs (residual)|||||||-0.261<sup>_a_</sup><br>(0.078)|
|Asian share of imports|||-1.639<sup>_a_</sup>||-1.309<sup>_a_</sup>|-1.132<sup>_a_</sup>|-1.118<sup>_a_</sup>|
||||(0.413)||(0.379)|(0.380)|(0.383)|
|oecdshare of imports|||-1.103<sup>_a_</sup>||-0.638<sup>_c_</sup>|-0.491|-0.475|
||||(0.400)||(0.341)<br>|(0.344)|(0.345)|
|naftashare of imports|||-1.188<sup>_a_</sup>||-0.715<sup>_b_</sup>|-0.562<sup>_c_</sup>|-0.549<sup>_c_</sup>|
||||(0.363)||(0.327)|(0.327)|(0.327)|
|Asian share of exports|||0.370||0.371|0.482|0.482|
||||(0.542)||(0.422)|(0.405)|(0.412)|
|oecdshare of exports|||0.366||0.423<sup>_b_</sup>|0.440<sup>_b_</sup>|0.443<sup>_b_</sup>|
||||(0.274)||(0.210)|(0.189)|(0.193)|
|naftashare of exports|||0.298||0.279|0.319|0.318|
||||(0.294)||(0.206)|(0.196)|(0.201)|
|Input distance||||-0.359<sup>_a_</sup>|-0.343<sup>_a_</sup>|-0.361<sup>_a_</sup>|-0.358<sup>_a_</sup>|
|||||(0.064)|(0.059)|(0.055)|(0.055)|
|Output distance||||-0.262<sup>_a_</sup>|-0.290<sup>_a_</sup>|-0.313<sup>_a_</sup>|-0.318<sup>_a_</sup>|
|||||(0.046)|(0.044)|(0.042)|(0.043)|
|Average minimum distance||||-0.323<sup>_a_</sup>|-0.300<sup>_a_</sup>|-0.296<sup>_a_</sup>|-0.294<sup>_a_</sup>|
|||||(0.046)|(0.047)|(0.039)|(0.039)|
|Industry controls included|No|No|No|No|No|Yes|Yes|
|Number ofnaicsindustries|257|257|257|257|257|257|257|
|Number of years|17|17|17|17|17|17|17|
|Year dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations (naics_×_ years)|4,369|4,369|4,369|4,369|4,369|4,369|4,369|
|_R_<sup>2</sup>|0.165|0.054|0.096|0.442|0.473|0.516|0.518|

_Notes:_ The dependent variable is the unweighted (count based) Duranton-Overman _K_ -density cdf at 50 kilometers distance.<sup>_a_</sup> ,<sup>_b_</sup> and<sup>_c_</sup> denote coefficients significant at the 1%, 5% and 10% levels, respectively. We use simple ols. Standard errors are clustered at the industry level and given in parentheses. Our measures of input and output distances, as well as average minimum distance, are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. A constant term is included in all regressions but not reported. **Model 6** includes the following industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with post-secondary education; In-house R&D share of sales). Last, our preferred specification, **Model 7** , uses the residual trucking rate as the main explanatory variable.

competition from low-wage countries leads to significant exit of large plants that produce standardized ‘main segment’ goods that are very exposed to competition.<sup>16</sup> Since those plants are the ones that are predominantly clustered at short distances, their exit will significantly reduce the extent of measured geographic concentration.<sup>17</sup> As can also be seen from **Model 3**

> 16We cannot disentangle the impact of exit vs relocation on the spatial structure. However, we control for the size of the industry, which at least partly picks up entry and exit dynamics. Note that relocations are quite rare and should have little impact on our results. The bulk of the variation is driven by entry and exit.

> 17This is a somewhat surprising result, because we would expect the productivity enhancing effects of geographic concentration to shelter firms from low-wage competition. Yet, one should keep in mind that clustering

17
in Table 4, rising export shares are across the board associated with increasing geographic concentration, although the effect is only significant for the share of exports to oecd countries. This pattern may be driven by the fact that more isolated non-exporting plants have a higher probability of exit, or that localization increases the export participation and performance of plants (e.g., Koenig, Mayneris, and Poncet, 2010).

Next, **Model 4** adds our input-output distances – the industry mean of the average minimum distance to a dollar of inputs or outputs computed using the five nearest plants in each industry – as well as our minimum distance (density) control.<sup>18</sup> As can be seen, the estimated coefficients on the input and output distance measures are negative and highly significant, and they tend to be of similar magnitude. Industries tend to follow their suppliers and customers, i.e., industries where potential suppliers or clients disperse tend to also disperse. This shows that controlling for vertical industry links – as suggested by our model – is important in order to capture the general equilibrium effects of changing geographic industry structure. Note that this effect is not driven by changes in overall density which we control for (and the associated variable is highly significant). Note also that the coefficient on trucking costs barely changes when including our measures for access to suppliers and customers. **Model 5** shows that the joint inclusion of both trade and input-output controls, as suggested by theory, does not significantly change our baseline estimates. Although the coefficient on trucking cost drops, it remains negative and highly significant.

Next, **Model 6** adds a large number of time-varying industry controls that may influence the geographic concentration of industries. We include a measure of industry size, several controls for industry structure (the Herfindahl index of the firm-size distribution, mean plant size, the share of plants controlled by multiplant firms, and the share of plants controlled by foreign-owned firms), and several proxies for natural advantages (the share of inputs from natural resource-based industries, and the share of energy inputs in total output). We also include proxies for the skill composition of the workforce and for knowledge spillovers to control for ‘Marshallian’ forces. Our results are robust to the inclusion of these controls.

As discussed in Section 4.2, our measure of transport costs is potentially endogenous. We return to this point in more detail below. As a first step to address this problem, we use in **Model 7** – our preferred specification – the residual transport cost obtained from a first-stage regression of that cost on industry multi-factor productivities and a set of industry and year provides firms with benefits as long as clusters grow (positive shocks), but that the unravelling of clusters (negative shocks) may lead to a domino effect as the agglomeration benefits dissipate with the exit of firms. Also, as shown by Holmes and Stevens (2014), plants in clusters operate on different market segments than non-clustered plants, and they are more vulnerable to import competition. See also Behrens, Boualam, and Martin (2016) for recent evidence on the dispersive effect of import competition on Canadian textile clusters between 2001 and 2013.

> 18See Appendix B.3 for a discussion of the need to include the minimum distance control. Using _N_ = 3, 7, 10 plants to construct these distances yields qualitatively very similar results.

18
fixed effects.<sup>19</sup> Consistent with our prior, the coefficient on transport costs becomes larger in absolute value when using the productivity-purged residual (compare **Models 6** and **7** ). This is in line with our expectations discussed in Section 4.2, where we have argued that endogeneity concerns due to reverse causality are likely to bias the coefficient upwards (towards zero in this case). In what follows, we hence systematically use the residual measure of ad valorem trucking costs in all our regressions. We more fully deal with remaining endogeneity issues later. Last, note that in our prefered specification about half of the time-series variation in geographic concentration is explained by the model (5).

How large are the effects of transport costs on the geographic concentration of industries? As shown in Section 3.1, the degree of geographic concentration of manufacturing industries has significantly fallen in Canada between 1990 and 2009. How much of that change is explained by changes in transport costs? To gauge the strength of the effect, we compute the predicted change in the cdfs by holding the ad valorem trucking costs constant at their 1992 values, while still allowing the other variables to change through time. The observed change in the cross-industry average cdf between 1992 and 2008 at a distance of 50 kilometers is - 23.37%. Holding the ad valorem trucking rate fixed at its 1992 level, the change would have been -28.36%. Thus, had ad valorem trucking costs not decreased by about 4% over the 1992– 2008 period, the average geographic concentration of industries would have fallen by about 5 percentage points more (about 20% of the overall change).<sup>20</sup>

# **.2 5 Spatial scale**

One of the of the do measure of concentration is that we can it strengths geographic vary continuously across distances. We hence next investigate the spatial scale at which changes in transport costs operate by changing the distance _d_ at which the _K_ -density cdf is evaluated. Doing so allows us to highlight how our key variable of interest influences the geographic concentration of industries at different spatial scales. Furthermore, we can provide plots of the marginal effects over the whole distance range, and thus allowing for a fine analysis of the spatial dimensions of the changes in agglomeration due to changes in transport costs. The left half of Table 5 summarizes our results for different distances. To save space, we only report results for **Model 7** at three selected distances: 10, 100, and 500 kilometers. As can be seen, the qualitative results do not depend on the distance threshold _d_ . Yet, there is a general tendency

> 19When using the ‘ad valorem trucking cost residual’ from the first-stage regression, we need to bootstrap the standard errors to control for the presence of an estimated regressor. We did this for the baseline specification (see **Model 7** in Table 7), and it makes virtually no difference. We hence report non-bootstrapped standard errors (yet clustered by industry) in all specifications.

> 20We can repeat the exercise for import shares. Holding all import shares fixed at their 1992 level, the change in the cdf would have been -14.63%. In words, had imports remained at their 1992 levels, the geographic concentration would have fallen by about 9 percentage points (i.e., 60%) less than what we observed.

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Table 5: Estimation of (5) by distance and by incremental change in the cdf.

||**Mo**|**del (7), by dist**|**ance**||**Model (7), by**|**incremental**c|df|
|---|---|---|---|---|---|---|---|
|Variables|cdf 10km|cdf 100km|cdf 500km|_∆γi_(10, 25)|_∆γi_(25, 50)|_∆γi_(50, 100)|_∆γi_(100, 500)|
|Ad valorem trucking costs (residual)|-0.269<sup>_a_</sup><br>(0.080)|-0.250<sup>_a_</sup><br>(0.073)|-0.212<sup>_a_</sup><br>(0.048)|-0.253<sup>_a_</sup><br>(0.079)|-0.238<sup>_a_</sup><br>(0.080)|-0.229<sup>_a_</sup><br>(0.069)|-0.105<br>(0.090)|
|Asian share of imports|-1.359<sup>_a_</sup>|-0.923<sup>_a_</sup>|-0.307<sup>_b_</sup>|-1.029<sup>_b_</sup>|-0.724<sup>_b_</sup>|-0.352|0.583|
||(0.467)|(0.299)|(0.139)|(0.433)|(0.337)|(0.235)|(0.429)|
|Input distance|-0.382<sup>_a_</sup>|-0.340<sup>_a_</sup>|-0.242<sup>_a_</sup>|-0.332<sup>_a_</sup>|-0.322<sup>_a_</sup>|-0.315<sup>_a_</sup>|-0.193<sup>_a_</sup>|
||(0.063)|(0.049)|(0.033)|(0.061)|(0.055)|(0.054)|(0.041)|
|Output distance|-0.307<sup>_a_</sup>|-0.307<sup>_a_</sup>|-0.197<sup>_a_</sup>|-0.341<sup>_a_</sup>|-0.340<sup>_a_</sup>|-0.302<sup>_a_</sup>|-0.122<sup>_a_</sup>|
||(0.046)|(0.040)|(0.027)|(0.045)|(0.045)|(0.045)|(0.039)|
|Average minimum distance|-0.322<sup>_a_</sup>|-0.268<sup>_a_</sup>|-0.137<sup>_a_</sup>|-0.298<sup>_a_</sup>|-0.243<sup>_a_</sup>|-0.204<sup>_a_</sup>|-0.038|
||(0.046)|(0.035)|(0.024)|(0.041)|(0.043)|(0.038)|(0.036)|
|Other trade shares included|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry controls included|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_R_<sup>2</sup>|0.473|0.540|0.545|0.481|0.417|0.436|0.168|

_Notes:_ All estimations for 257 industries and 17 years (4,369 observations). The dependent variable is the unweighted (count based) DurantonOverman _K_ -density cdf at the reported distance.<sup>_a_</sup> ,<sup>_b_</sup> and<sup>_c_</sup> denote coefficients significant at the 1%, 5% and 10% levels, respectively. We use simple ols. All specifications include industry and year fixed effects. Standard errors, given in parentheses, are clustered at the industry level. Our measures of input and output distances are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. A constant term is included in all regressions but not reported. All industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with post-secondary education; In-house R&D share of sales) are included but not reported. All trade share variables are included in the regressions, but we do not report detailed results to save space.

kilometers. Note also that the standard errors start to increase more quickly after about 110 to 120 kilometers, i.e., the estimated coefficients on transport costs become less precise.

# **. Robustness checks 5 3**

We now provide additional evidence on the robustness of our key findings. First, we investigate the robustness of our results to the choice of the dependent variable. Table 13 in the Supplementary Appendix S.4 shows that the effect of transport costs on geographic concentration is weaker – and the of the model lower – when the latter explanatory power slightly is measured using either employment- or sales-weighted cdfs. Although the key qualitative flavor of the results and the sign and significance of our key coefficient remain unchanged, the estimates using employment- or sales-weighted _K_ -densities are slightly less sharp. Furthermore, the effect of import competition tends to be more limited to imports from Asia, and the coefficient tends to be smaller too. This suggests that much of the adaptation to import competition, particularly from low-wage countries which are responsible for the bulk of exit in Canadian manufacturing, occurs for smaller plants and firms (Behrens _et al._ , 2016). The residual transport cost variable remains significantly negative in all specifications that we estimate, irrespective of how we construct the dependent variable. In a nutshell, changes in transport 21
costs have a significant effect on the geographic concentration of economic activity, no matter whether we consider plants, employment, or sales to measure that concentration.

Table 6: Estimation results for specification (5), robustness checks.

|Variables|Dependent<br>**(Model 8)**|variable is the<br>**(Model 9)**|cdfat50km<br>**(Model 10)**|
|---|---|---|---|
|Ad valorem trucking costs (residual)|-0.245<sup>_a_</sup><br>(0.082)|-0.198<sup>_b_</sup><br>(0.083)|-0.192<sup>_b_</sup><br>(0.086)|
|Asian share of imports|-1.119<sup>_a_</sup>|-1.035<sup>_a_</sup>|-1.041<sup>_a_</sup>|
||(0.387)|(0.398)|(0.401)|
|Input distance|-0.337<sup>_a_</sup>|-0.238<sup>_a_</sup>|-0.232<sup>_a_</sup>|
||(0.056)|(0.055)|(0.056)|
|Output distance|-0.324<sup>_a_</sup>|-0.292<sup>_a_</sup>|-0.298<sup>_a_</sup>|
||(0.041)|(0.039)|(0.038)|
|Average minimum distance|-0.264<sup>_a_</sup>|-0.290<sup>_a_</sup>|-0.272<sup>_a_</sup>|
||(0.040)|(0.037)|(0.037)|
|Other trade shares included|Yes|Yes|Yes|
|Industrycontrols included|Yes|Yes|Yes|
|_Additional controls:_||||
|Distance to major container ports|-0.235<sup>_b_</sup>||-0.140|
||(0.098)||(0.104)|
|Eastern share of plants||0.758<sup>_a_</sup>|0.715<sup>_a_</sup>|
|||(0.159)|(0.159)|
|Number ofnaicsindustries|257|257|257|
|Number of years|17|17|17|
|Year dummies|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|
|Observations (naics_×_ years)|4,369|4,369|4,369|
|_R_<sup>2</sup>|0.525|0.551|0.553|

_Notes:_ The dependent variable is the unweighted (count based) DurantonOverman _K_ -density cdf at 50 kilometers distance. _a_ , _b_ and _c_ denote coefficients significant at the 1%, 5% and 10% levels, respectively. We use simple ols. Standard errors are clustered at the industry level and given in parentheses. Our measures of input and output distances, as well as average minimum distance, are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. A constant term is included in all regressions but not reported. All industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with post-secondary education; In-house R&D share of sales) are included but not reported. All trade share variables are included in the regressions, but we do not report detailed results to save space.

Second, one may worry that import entry points are geographically localized, so that increasing imports could lead to the geographic concentration of industries around those entry points. This may, in turn, bias the transport cost coefficient. To address this potential concern, we include as a control the average minimum distance of plants in each industry from one of the five major Canadian container ports.<sup>23</sup> As can be seen from **Model 8** in Table 6, although 23Those ports are Halifax (NS), Saint John (NB), Montreal (QC), Vancouver (BC), and Prince Rupert (BC).

22
industries that do move closer to import entry points tend to concentrate more geographically, the coefficients on the other variables are basically unchanged. Another concern we need to address is that the above-average economic growth of the western Canadian provinces might be driving the observed dispersion of manufacturing. To address that concern, we include in our as an additional control the share of eastern in each As can regressions plants industry.<sup>24</sup> be seen from **Model 9** in Table 6, this reduces somewhat the coefficients on trucking costs and Asian share of imports, yet does not change our qualitative results. Although the westward shift of the Canadian economy over the 1992–2008 period partly accounts for increasing geographic dispersion of industries, that effect is limited and does not substantially reduce the effect of trucking costs and import competition. Last, **Model 10** shows that the joint inclusion of both controls does not change our results either.

We also run a large number of robustness checks that we do not report in detail. First, we re-estimate the model by averaging all variables over five year periods. Doing so reduces the year-on-year volatility of some variables (e.g., the trade variables), and allows for slowly moving variables like R&D expenditures to be potentially better identified in the regressions. It also deals with business cycle aspects that may drive the changes in the geographic concentration of industries. The last three columns of Table 13 in the Supplementary Appendix S.4 show that our basic findings are unchanged when replacing year-on-year variations with five-year averages. Second, our results may be partly driven by a small number of sectors that were subject to major changes over our study period. For example, as documented by Behrens _et al._ (2016), the Canadian textile and clothing industry experienced a remarkable downward trend in the number of plants and in its geographic concentration in the wake of the end of the Multi-Fibre Arrangement in 2005. Given that the textile and clothing industry contains some of the initially most strongly agglomerated sectors in Canada (see Table 9 in the Supplementary Appendix S.2), the large changes in those sectors may drive some of our key results. That this is not the case, and that all of our main findings are robust to the exclusion of those sectors, is shown in Table 12 in the S.2. We also run our ex- Supplementary Appendix regressions by cluding the ‘high-tech’ sectors, and the results are qualitatively unchanged. Third, we compute measures of ‘upstreamness’ of industries following Antràs, Chor, Fally, and Hillberry (2012). Using those measures, we split industries into the top quintile Q5 (most upstream industries) and the bottom quintile Q1 (most downstream industries). We then reestimate the model by interacting the transport and trade variables with those upstream-downstream dummies to capture potentially different impacts on different industries in the vertical production chain. The results are summarized in Table 14 in the Supplementary Appendix S.4. When including our input-output measures, splitting by upstreamness has virtually no effects on our main coefficients, which suggests that our input-output measures capture quite well vertical industry links. When excluding those measures, we find that more downstream industries are more 24We compute each industry’s share of plants in Ontario, Quebec, and the Atlantic provinces.

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sensitive to both transport costs and import competition, although the differential effects are quite imprecisely estimated.

We additional details on other robustness checks that we run provide many (controlling for sectoral changes in ict; heterogeneous transport coefficients across industries; asymmetric coefficients for rising and falling costs; non-linear transport cost specifications etc.) in the Supplementary Appendix S.4. To summarize, our key findings are robust to a large number of robustness checks and continue to hold true in a variety of alternative specifications. Sectors that see their transport costs increase or that experience greater import competition tend to disperse more than other sectors.

# **. 5 4 Controlling for endogeneity**

We finally address additional potential endogeneity concerns that we discussed in Section 4.2. The results of the different estimations are summarized in Table 7. **Model 11** replicates **Model 7** of Table 4. As explained previously, we use the residual of a regression of ad valorem trucking costs on sectoral multifactor productivity – including a set of industry and year fixed effects – in that specification. The residual from that regression is, by construction, orthogonal to multifactor productivity. **Model 11** in Table 7 differs from **Model 7** in Table 4 only by the standard errors, which are bootstrapped using 200 replications. The results do not change.

Although the residual transport cost is purged from productivity effects, endogeneity concerns linked to, e.g., backhaul, remain. Hence, we run some instrumental variable (iv) regressions to check the of our results. **Model 12** summarizes our iv-2sls results where we validity instrument the ad valorem trucking rate residual by replacing the Canadian price indices with their us counterparts. The rationale underlying this instrumentation strategy was explained in Section 4.2. The first-stage results are summarized in column 3 of Table 7. As can be seen, the instrument is strong (with a first-stage _F_ -test value of 19.07 and a first-stage _R_<sup>2</sup> of 0.62). Column 4 of Table 7 shows that, in line with our expectations, the instrumented coefficient is substantially more negative than the coefficient for the residual ad valorem trucking rate, which is itself already more negative than the coefficient using the unpurged trucking rate. The direction of the bias in the estimated coefficients is the same as in **Model 7** , which suggests that ols estimates underestimate the impact of changes in ad valorem transport costs on the geographic concentration of industries.

Last, **Models 13** and **14** in Table 7 address remaining endogeneity concerns that may affect the trade variables and the input-output distance measures (see our discussions in Appendices B.2 and B.3, respectively).<sup>25</sup> Since we do not have any good external instruments for those

> 25For example, plants are identified in our data by their primary industry only. Hence, although we exclude the industry itself when constructing the input-output distance measures, multi-product plants whose secondary activities belong to the same industry might be included in those measures.

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Table 7: Controlling for the potential endogeneity of _Ti_ , _t_ in specification (5).

||De|pendent varia|ble is thecd|fat50kilome|ters|
|---|---|---|---|---|---|
|Variables|**(Model 11)**<br>Base|**(Mode**<br>First stage|**l 12)**<br>iv-2sls|**(Model 13)**<br>Lewbel1|**(Model 14)**<br>Lewbel2|
|Ad valorem trucking costs (residual)|-0.261<sup>_a_</sup>||-0.393<sup>_a_</sup>|-0.197<sup>_b_</sup>|-0.218<sup>_b_</sup>|
||(0.0781)||(0.0956)|(0.0929)|(0.0915)|
|Ad valorem trucking costsus(instrument)||0.485<sup>_a_</sup>||||
|||(0.111)||||
|Asian share of imports|-1.118<sup>_a_</sup>|-0.056|-1.095<sup>_a_</sup>|-1.592<sup>_a_</sup>|-1.618<sup>_a_</sup>|
||(0.383)|(0.107)|(0.381)|(0.533)|(0.501)|
|Input distance|-0.358<sup>_a_</sup>|0.035<sup>_c_</sup>|-0.356<sup>_a_</sup>|-0.143<sup>_c_</sup>|-0.224<sup>_a_</sup>|
||(0.0545)|(0.020)|(0.055)|(0.075)|(0.075)|
|Output distance|-0.318<sup>_a_</sup>|-0.011|-0.322<sup>_a_</sup>|-0.386<sup>_a_</sup>|-0.360<sup>_a_</sup>|
||(0.043)|(0.015)|(0.042)|(0.083)|(0.087)|
|Average minimum distance|-0.294<sup>_a_</sup>|0.005|-0.290<sup>_a_</sup>|||
||(0.039)|(0.014)|(0.039)|||
|Other trade shares included|Yes|Yes|Yes|Yes|Yes|
|Industry controls included|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|
|_R_<sup>2</sup>|0.518||0.516|0.319|0.330|
|First-stage_R_<sup>2</sup>||0.628||||
|First-stage_F_ test of excluded instruments||19.07||||

_Notes:_ The dependent variable is the unweighted (count based) Duranton-Overman _K_ -density cdf at 50 kilometers distance.<sup>_a_</sup> ,<sup>_b_</sup> and<sup>_c_</sup> denote coefficients significant at the 1%, 5% and 10% levels, respectively. Our measures of input and output distances are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. Model 11 replicates our preferred Model 7 but the standard errors are bootstrapped because of the generated regressor. Model 12 instruments the ‘Ad valorem trucking costs’ using costs constructed from us price indices. Models 13 and 14 use the Lewbel (2012) methodology to instrument input-output distances and trade shares. In Model 13, only a subset of the import shares is instrumented, while all trade shares are instrumented in Model 14. See Appendix C for additional details. A constant term is included in all regressions but not reported. All industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with postsecondary education; In-house R&D share of sales) are included but not reported. All trade share variables are included in the regressions, but we do not report detailed results to save space.

variables, we use the Lewbel (2012) estimator with internal instruments for the input-output distances and the trade shares (see Lewbel, 2012, and Appendix C for more details on the implementation).<sup>26</sup> The excluded external instrument is the us price-based ad valorem trucking costs as before. As can be seen from the results in Table 7, the instrumented coefficient on the Asian share of imports increases, as do most of the other trade share coefficients. At the same time, both the magnitude of transport costs and of the input and output distances decreases slightly. However, these variables remain significant and their magnitude is in the same ballpark than in the case of ols. Thus, our results appear to be robust. Decreases in ad val26 Since there is an insignificant correlation between the oecd export share and the squared residuals, we did not include it. We substituted instead the nafta import share because it is consistently significant in the baseline set of models and it meets the criteria for being internally instrumented (see Appendix C).

25
orem trucking costs lead to more geographic concentration of manufacturing industries, even when potential endogeneity concerns in transport costs, trade exposure, and buyer-supplier relationships are taken into account.

# **6 Conclusions**

We develop a long industry panel of continuous measures of geographic concentration and combine them with measures of ad valorem trucking rates, international trade exposure, upstream and downstream links, and a large number of controls, to assess whether the ‘world is flat.’ Our answer is an emphatic, not yet! That is, the key message of our findings is that changes in the geographic concentration of industries due to changes in transport costs are sizable. This is a robust finding that survives a battery of checks, including extensive efforts to address inherent endogeneity issues that plague such estimations. We should also add that, to the best of our knowledge, this is the first instance where direct measures of transport costs have been used to assess their effects on the geographic concentration of industries.

The lessons for researchers from this work are twofold. The first is that it is difficult to contemplate investigating industry location (or co-location) without taking transport costs explicitly into account (see, e.g., Combes and Gobillon, 2015). In a nutshell, investing in better measures of transport costs is important and likely to pay substantial dividends. The second is that it is equally difficult to consider the effects of transport costs in isolation. Their general equilibrium effects on input-output links and competition, and more generally their endogenous nature as market prices, have to be grappled with. This involves challenges – both theoretical and – with investments for both. While we believe we empirical large required have made some strides developing the necessary empirics, theoretical work in moving from simulations to full-blown analytical results is still called for and needed.

The lesson for policy makers is simple: small changes in transport costs – e.g., due to infrastructure projects or any other policy that changes the costs of trading goods across nations and regions – still impact the economic geography of industries. Contrary to what seems a received wisdom in many policy circles, the world is not yet a flat featureless plain. Even small changes in transport costs – combined with historically low levels of these costs – can strongly affect geography because firms compete globally and their slim profit margins are driven to a large extent by locational advantage. In the end, the debate surrounding the ‘flat world’ is a classical instance of the fallacy consisting in equating ‘low’ with ‘unimportant’.<sup>27</sup>

> 27See, e.g., the ‘kaleidoscopic comparative advantage’ debate in international trade (Jagdish Bhagwati, “Why the world is not flat”, 2010; available at `http://www.worldaffairsjournal.org/blog/jagdish-bhagwati/whyworld-not-flat` ). Last accessed on July 11, 2016.

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