metadata: authors: 0: author: Blankespoor,Brian
1: author: Bougna Lonla,Theophile
2: author: Garduno Rivera,Rafael
3: author: Selod,Harris
count: Mexico
docna: 0: docna: Roads and the geography of economic
            activities in Mexico
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Educational Sciences,Ports & Waterways,International Trade and Trade Rules,Business Cycles and Stabilization Policies,Transport Services
teratopic: Communities and Human Settlements,International Economics and Trade,Urban Development
abstracts: cdata!: This paper estimates the impacts of road
            improvements on local employment and specialization in
            Mexico for 1986-2014, through changes in access to domestic
            markets and travel costs to ports and the U.S. border.
            Instrumenting for road placement endogeneity and addressing
            the recursion problem in regressions that involve access to
            markets, the analysis finds significant and positive causal
            effects of improved domestic accessibility on employment and
            specialization. It also finds that employment is stimulated
            by lower transport costs to the U.S. border, but harmed by
            lower transport costs to ports. Heterogeneous effects are
            found across sectors and regions.
geo_reg_key: 517191,119228
repnme: repnme: Roads and the geography of economic
            activities in Mexico
display_title: Roads and the geography of economic
            activities in Mexico
listing_relative_url: /research/2017/10/28110042/roads-geography-economic-activities-mexico
topicv3: Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698,Education m1327048 470,Ports m1327399 167
publishtoextweb_dt: 2017-10-26T00:00:00Z
historic_topic: Communities and Human Settlements,International Economics and Trade,Urban Development
no_of_pages: 42
author_keys: 0: author_key: 957135
1: author_key: 1057789
2: author_key: 1339639
3: author_key: 750638
model_extractions: WPS8226 Policy Research Working Paper 8226 Roads and the Geography of Economic Activities in Mexico _Brian Blankespoor Théophile Bougna Rafael Garduno-Rivera Harris Selod_ Development Research Group Environment and Energy Team & Development Data Group October 2017
Policy Research Working Paper 8226

# **Abstract**

This paper estimates the impacts of road improvements on local employment and specialization in Mexico for 1986–2014, through changes in access to domestic markets and travel costs to ports and the U.S. border. Instrumenting for road placement endogeneity and addressing the recursion problem in regressions that involve access to markets, the analysis finds significant and positive causal effects of improved domestic accessibility on employment and specialization. It also finds that employment is stimulated by lower transport costs to the U.S. border, but harmed by lower transport costs to ports. Heterogeneous effects are found across sectors and regions.

This paper is a product of the Environment and Energy Team, Development Research Group and the Development Data 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 bblankespoor@worldbank.org, tbougna@worldbank.org, rafael. garduno@cide.edu, and hselod@worldbank.org (corresponding author).

_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
# Roads and the Geography of Economic Activities in Mexico<sup>*</sup>

Brian Blankespoor<sup>†</sup> Théophile Bougna<sup>‡</sup> Rafael Garduno-Rivera<sup>§</sup> Harris Selod<sup>¶</sup>

**Keywords:** Mexico, Roads, Market Access, Market Potential, Specialization, Industrial Localization.

## **JEL classification:** R12; R30; C23.

> *We thank Luis Rodrigo Gonzalez Vizuet, Shiyan Zhang, and Lucie Letrouit for excellent research assistance. We are indebted to Mike Bess for the many inputs he provided regarding the history of roads in Mexico, and to Matt Turner, Uwe Deichmann, Maria Marta Ferreyra and Mark Roberts, who provided very useful comments on the preliminary versions of this paper. We also thank participants to the authors’ workshop for the World Bank study on ‘Cities, Productivity and Growth in Latin America and the Caribbean’, and at the Toulouse Development, Labor and Public Policy Seminar. Funding from the World Bank and from dfid under the World Bank Strategic Research Program (srp) ‘ _Transport for Sustainable and Inclusive Growth_ ’ is gratefully acknowledged.

> †Development Data Group, The World Bank, E-mail: `bblankespoor@worldbank.org`

> ‡Development Research Group, The World Bank, E-mail: `tbougna@worldbank.org`

> §Centro de Investigación y Docencia Económicas, Mexico. E-mail: `rafael.garduno@cide.edu`

> ¶ _Corresponding author_ . Development Research Group, The World Bank. E-mail: `hselod@worldbank.org`
# **1 Introduction**

There are many benefits associated with investments in transport infrastructure which can stimulate growth through trade, structural transformation, agglomeration and productivity (see Redding and Turner, 2015, and Berg el al., 2017 for surveys). The impacts can be large: Calderón, Moral-Benito and Servén (2009) estimate that a 10 percent improvement in the stock of infrastructure (including transport infrastructure) increases per capita gdp by 0.7 to 1 percent. In Latin America, in particular, the potential to stimulate growth through transport investments could be especially significant given the large infrastructure gap in most Latin American countries (see Fay and Morrison, 2007; Gonzalez, Guasch and Serebrisky, 2007). Perrotti and Sánchez (2011) estimate that ’closing the infrastructure gap’ in Latin American countries would require investments in the amount of up to 6.2 percent of annual GDP for the period 2012- 2020. In theory, benefits could then materialize in the form of a reduction in transportation costs (or travel time), more intense competition, and deeper economic integration. Locations where access to markets is improved would become more attractive for firms, potentially triggering a process of concentration and specialization of local economic activity whereby innovation, the sharing of input markets, and improved matching on the labor market would increase productivity, and in turn lead to local economic development and an increase in living standards.

In this paper, we focus on the first steps in this potential chain of events, in the case of Mexico, a country which experienced relatively large investments in roads over the past decades and from which lessons could be derived for the rest of the continent. In Mexico, road transport accounts for 57% of freight transport and remains a vital component of the economy. The Mexican road network links the country from north to south and between its two oceanic coastlines; and some of the most important road connections link the capital city with border crossings to the US. We investigate the extent to which these road improvements increased access to markets and, in turn, affected the location of economic activities.

Our paper contributes to a growing literature that looks at the effect of transport infrastructure on local economic development outcomes (Donaldson and Hornbeck, 2015; Duranton and Turner, 2012; Michaels, 2008; Baum-Snow, 2007; Baum-Snow et al., 2017; Jedwab and Storeygard, 2016; Alder, 2016; Alder, Robert and Tewari, 2017; Straub and Bird, 2015, Ghani et al., 2015; Faber, 2014; Rothenberg, 2015, Fyre, 2015; Galasso and Oettl, 2017). In view of this literature, our paper looks at the effects of roads on the geographical concentration of economic activities and the specialization of localities.

There are two challenges to estimate the causal impact of roads on local economic outcomes: the non-random placement of roads, and the recursion problem inherent in regression of economic outcomes on market access measures. To address these two we problems, adopt three separate identification strategies. First, we use the so-called ’doughnut’ strategy, which 1
allow to instrument access to markets excluding zones that are more likely to be prone to an endogeneity bias due to non-random road placement. Second, in order to avoid the recursion problem, we substitute market access indicators with measures of access to infrastructure that do not involve population or income. These measures include the number of roads intersecting a circle of a given radius around the centroid of each locality, or the number of kilometers of roads within a given distance to that centroid. Third, we use these access to infrastructure variables as instruments for market access indicators.

Our paper makes three principal contributions. First, we construct a new panel of geo- referenced roads data in Mexico over three decades (for the period 1986 2014). Second, we also take advantage of the panel nature of our data to estimate time-varying measures of market access for more than 2,000 localities (whereas previous studies, either just used a few dates or had a sample with a limited number of observations). Third, to the best of our knowledge, our paper is the first to study the effect of market access on specialization.

Our key findings are that transportation improvements over the last three decades increased access to markets in Mexico—which we measure in concurrent ways—and that these improvements in accessibility had a positive impact on local employment and economic specialization. The effects are large and heterogeneous across sectors. A 10 percent increase in market access results in a 1.6 to 2.1 percent increase in employment and a large increase in locality specialization. The effect of our market potential measure is slightly larger. A 10 percent increase results in a 2.9 to 6.5 percent increase in employment, and a 13.0 percent increase in output specialization. We also effects across sectors with in commerce and find heterogeneous employment services benefiting more than manufacturing from road improvements.

The rest of the paper is structured as follows. Section 2 presents the Mexican context. Section 3 describes the data used, explains the construction of our market access and market potential measures, and provides information on the trends in industrial concentration and specialization in Mexico. Section 4 presents our empirical methodology and discusses identification strategies. Section 5 discusses our empirical results regarding the effects of market access and market potential on employment and specialization, and Section 6 concludes.

# **2 The Mexican context: Lessons from the literature**

## **2.1 The concentration of industries and specialization of localities**

Economic activity in Mexico is highly concentrated in the Mexico City Metropolitan Area, which in 2010, contributed to a quarter of the national Gross Value Added even though it covers less than 0.3 percent of the national territory. In the wake of Michael Porter’s work (Porter, 1998), there has been increasing policy interest in Mexico to facilitate the industrial concentration and specialization of localities and the development of industries, so as to ultimately 2
achieve higher economic growth. However, economic studies that shed light on these issues are scarce and only a few case studies focus on industrial concentration and specialization.

A first set of such studies measures the level of concentration of various industries and its increase over time (Unger, 2003) and tries to identify the determinants of industrial clustering, concluding that, in Mexico, labor force skill play a very important role to attract clusters, much more than wage differentials (Unger and Chico, 2004). Complementing these observations, several authors identify the locations that have become more specialized over time, focusing on different spatial scales. Pérez and Palacio (2009) find that specialization increased during the 1994-2004 period. Focusing on Mexican cities, Kim and Zangerling (2016) find that, between 1990 and 2010, specialization only increased in the Mexico City Metropolitan Area, but did not in other cities which remained very much diversified.

A second set of case studies points at the potential benefits of clustering in Mexico. Monge (2012) suggests that clustering of the tequila industry in the state of Jalisco (in Western Mexico) reduced transaction costs and that entrepreneurs in that sector benefited from labor specialization. Dávila (2008), who studied the economic performance and the commercial integration with Texas of industrial clusters in northeastern Mexico<sup>1</sup> during the 1993-2003 period, suggests that economic clusters played a role to foster productive innovations and stimulate bilateral trade.

# **2.2 Road investments: A historical perspective**

In Mexico, large investments in roads started during the Spanish Colony (1521-1810), but these roads were mainly focused on connecting natural resources (especially silver and gold) with the port of Veracruz, to ship them to Spain.

During the presidency of Porfirio Días (between 1884 and 1911), while railways flourished throughout Mexico, the road network, which essentially dated back to the colonial period, received little financial support from the government and consequently deteriorated (Bess, 2016b). The Mexican Revolution, between 1910 and 1920, did not improve the situation. In 1918, municipal surveyors in Mexico City evaluated that the conflict had damaged around 4,000 km of roads serving the federal capital (Bess, 2016b). In the 1920s and 1930s, during the presidency of Alvaro Obregon and Plutarco Elias Calles, national and state political leaders engaged in the reconstruction and enlargement of the road network, mobilizing, to this aim, the private sector, public school teachers and rural communities all over the country (Bess, 2016a). Thousands of kilometers of highways were constructed.

The 1940s and 1950s were characterized by a great hope in the promises of industrialization and a generalized drive toward economic modernization. Road construction was perceived as necessary to allow market growth and improve the accessibility of regions (Bess, 2014). This

> 1i.e. Chihuahua, Coahuila, Nuevo Leon and Tamaulipas

3
conception led to the building of hundreds of kilometers of new roads in Mexico by state road-building agencies, mobilizing large public spending and private domestic investments as well as foreign ones. The U.S. invested millions of dollars of direct investments in Mexican transportation industry and infrastructure.

During President Miguel Aleman’s term (1946-1952), the first freeway (from Mexico City to Acapulco) was opened and became a model for the construction of future freeways. During this period, road building played a key role in the modernization of the Mexican economy and the development of major commercial industries (Bess, 2014).

In the 1960s, roads were built to respond to the needs of private firms and also to serve the national and state to build with rural commu- governments’ objective strategic relationships nities. Under President Adolfo Lopez Mateos (in office between 1958 and 1964), an unprecedented amount of 300 million pesos was raised in bonds for the building of new highways (Bess, 2017). In addition, a government owned company was founded to build more than a thousand kilometers of toll roads in the center of Mexico.

The presidency of Carlos Salinas de Gortari (1988-1994) constituted a significant milestone in the history of road construction in Mexico with the launch of a very ambitious program, the _Programa Nacional de Solidaridad_ , which led to the construction of 5,800 kilometers of privately financed highways at a cost of $15 billion (Foote, 1997). At the same time, cutting its own spending on road infrastructure, the government privatized toll road operations, a lucrative business for road-building firms. This allowed Mexico to build, in just six years, ’what had taken two decades to achieve in western European countries’ (Foote, 1997). However, the tolls ended trucks from these new roads. In the extremely high up deterring using response, federal government announced, in January 1997, the mobilization of $3.3 billion over 30 years to restructure the highway network. This outlay was added to a $ 1.7 billion toll-road rescue plan to help the state-owned Mexican banks that had financed road building on non-market terms (Foote 1997).

After 2000, the opposition party, the _Partido de Acción Nacional_ , eventually gained power. Road were nevertheless in with which con- building policies pursued continuity past policies, sidered the construction of roads as a symbol of modernization. President Vicente Fox (in office between 2000 and 2006) mobilized hundreds of millions of pesos for road building through a program for basic infrastructure, and his successor, President Felipe Calderón (in office between 2006 and 2012), constructed and renovated more than 23,000 km of roads as part of a program to address rural poverty. Eventually, in April 29, 2014, the federal government launched the National Infrastructure Program<sup>2</sup> 2014-2018 (nip), projecting a substantial increase in investment compared to the last twenty years (Pérez-Cervantes and Sandoval-Hernández, 2015). The most ambitious part of this new program focuses on the south of the country, while no large project is planned in the north and while the center of Mexico, more populated and richer,

> 2Programa Nacional de Infraestructura.

4
receives smaller projects.

## **2. Roads and economic activities 3**

To our knowledge, the impact of roads on the location of economic activities in Mexico has not been studied yet. A set of studies, however, have investigated the link between infrastructure and productivity. For instance, Becerril-Torres et al. (2010) study the effect of total infrastructure (measured with an index that includes roads, ports, airports, and telecommunications) on the convergence across states in technical efficiency. They find that their infrastructure index was associated with greater regional productivity, but mainly during the Import Substitution Industrialization (isi) period (1970-1985), before Mexico entered the North American Free Trade Agreement (nafta) in 1994. Similarly, Brock and German-Soto (2013) find that lower levels of infrastructure investments had a lower effect on regional productivity during the nafta period, and conclude that continued investment in transportation will be necessary to boost industrial sector growth. Focusing specifically on the effect of road infrastructure on productivity growth in the manufacturing sector in Mexico, Duran-Fernandez and Santos (2014a and 2014b) conclude that road infrastructure had positive effects on productivity and on the average product of labor.

# **3 Data and descriptives statistics**

## **.1 3 Firms and employment**

We use panel data for employment and firm locations from two different sources published by the Mexican National Institute of Statistics (inegi).<sup>3</sup> The first source is the Economic Census for the years 1986, 1989, 1994, 1999, 2004, 2009, and 2014, which provides employment figures and breakdown by broad sector of activity, as well as total income at the level of municipalities and for formal businesses. The second source of data is the Directory of Economic Units (denue)<sup>4</sup> for the years 2004, 2009 and 2014. It is an exhaustive dataset which contains detailed microgeographic information on all formal establishments, including the 6-digit naics classification, the number of employees of the establishment, turnover, the municipality identifier, and exact geographic coordinates. For the year 2014, the database contains information on approximately 3,000,000 establishments throughout the country. Note that although the denue database has finer geographic identifiers than the Economic Census (actual coordinates vs. municipality of location) and a finer industry identifier (6-digit naisc vs. broad sectors of activity), it covers less years than the Economic Census. In the analysis, we use either one, as appropriate.

> 3Instituto Nacional de Estadísticas y Geografía.

> 4Directorio Estadístico Nacional de Unidades Económicas.

5
Table 1 presents changes in the distribution of formal jobs in Mexico between 1999 and 2014 according to Economic Censuses. As can be seen, the bulk of jobs are in services, commerce and manufacturing. Although employment has increased in all sectors over the period, the greatest increases have occurred for commerce and services which relative shares have jobs, gone up while the relative shares of all the other sectors have down. The decrease in the relative gone share of manufacturing employment from 30 t0 24 percent is particularly noticeable. In 2014, more people were actually employed in the commerce sector than in manufacturing, showing that Mexico is following a trend of tertiarization similar to that of developed economies in previous decades.

Table 1: Sectoral distribution of formal jobs in Mexico (1999 – 2014)

|Sector|1999|share|2009|share|2014|share|
|---|---|---|---|---|---|---|
|Agriculture|174,127|1.27|180,083|0.90|188,566|0.87|
|Mining|113,189|0.83|14,2325|0.71|166,548|0.77|
|Manufacturing|4,1754,00|30.47|4,661,062|23.17|5,073,432|23.51|
|Commerce|3,792,466|27.68|6134758|30.50|6,389,648|29.61|
|Services|5,257,100|38.37|8,762,918|43.56|9,537,235|44.20|
|Other|190,033|1.39|235,688|1.17|220,929|1.02|
|Total|13,702,315|100.00|20,116,834|100.00|21,576,358|100.00|

_Source_ : Economic Censuces (inegi) 1999, 2009 and 2014.

Figure 1 plots the location of establishments in the denue database in 2004 and 2014 for all firms (top panel) and for manufacturing firms (bottom panel) respectively.<sup>5</sup> It reveals a high concentration of economic activity in the center of the country and in the surroundings of Mexico City, with a greater concentration of manufacturing firms than for the other sectors, but no visually noticeable change over the past decade.

# **.2 Roads 3**

In this paper, we focus on the later waves of road construction and improvement that occurred since the mid-eighties. To have a consistent measure of road extent and road types over time, we constructed a geo-referenced database for the period 1985-2016. This was done by importing historical road type and road extent information from the American Automobile Association (aaa) paper maps into the 2014 road geometry published by DeLorme. Because the DeLorme road geometry is network-enabled and topologically correct, it allows for clean travel time computations needed to measure access to markets (see below). Using this fixed we then information on road and extent for the reference or geometry, imported type years

> 5On Figure 1, establishment locations are overlayed on roads. See the following subsection for details regarding the roads data construction.

6
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sample. As a robustness check, all the regressions that we run on our mixed sample of 2,094 municipalities and metropolitan areas are also run on the sample of 2,377 municipalities.

# **. 3 4 Measures of accessibility**

## **Market access**

Each is characterized its to markets and we resort to several acces- locality by accessibility sibility measures. The first measure of accessibility for a locality _i_ , which we refer to as market access (ma), is given by the following formula: where _Pj_ , _t_ is the population of locality _j_ at time _t_ (which proxies for the size of the local market in _j_ ), _τij_ , _t_ is the time required to travel between locality _i_ and _j_ given the state of the road network at time _t_ , and _θ_ is a measure of trade elasticity.

From formula (1), it is easy to see that the market access indicator is the discounted sum of the populations of all the localities _j_ that surround locality _i_ , where the discount factor is inversely related to travel time. Travel times _τij_ , _t_ are calculated on the reconstructed countrywide road network assuming that speed is a function of road type.<sup>10</sup> As for the trade elasticity parameter, in the absence of a specific study for Mexico, we use the same value suggested by Donaldson (2016) in the case of India ( _θ_ = 3.8).<sup>11</sup> Market access, which reflects the size of domestic markets accessible from location _i_ , is frequently used in the literature in the absence of information on local incomes.

## **Market potential**

The second measure of accessibility for a locality _i_ , which we refer to as market potential (mp), is given by the following formula:

> 10We use the following speed assumptions: Multilane divided: 90 km/h; Two lanes or Divided: 80 km/h; Pavement: 70km/h; Gravel or Earth Road: 40 km/h; and unknown category (i.e., not shown on aaa paper maps but present in the 2014 DeLorme geometry): 5 km/h. These speed assumptions are consistent with the travel times published by inegi for their own road geometry.

> 11In the regressions presented in the next section, we also perform robustness checks by using alternative values of the market access indicator constructed with other values for _θ_ , the maximum value encountered in the literature being _θ_ = 8.2 (see Pérez Cervantes and Sandoval Hernández, 2015)

9
where _Yj_ , _t_ is locality _j_ ’s total income (in real terms)<sup>12</sup> at time _t_ , _TCij_ , _t_ is a transport cost function between localities _i_ and _j_ , and _σ_ is an elasticity term. Since the market potential formula has already been calibrated for Mexico, we use the same iceberg transport cost specification as the one estimated by Pérez Cervantes and Sandoval Hernández (2015), with _TCij_ , _t_ = _e_<sup>(.0557+.0024</sup><sup>_τij_,</sup><sup>_t_)</sup> for _j_ = _i_ and _σ_ = 9. Similarly to formula (1), formula (2) measures the potential demand for goods traded from location _i_ but in terms of income.

Observe that in both formulas (1) and (2), we exclude ’own locality’ to reduce endogeneity concerns.<sup>13</sup> Table 2 below details the mean of the market access and market potential indicators at the locality level over the study period. As one can see, there were large increases in both market access and market potential over the studied period.

Table 2: Mean and median market access and market potential of localities (1986 – 2014)

||M|arket acc|ess|M|arket potent|ial|
|---|---|---|---|---|---|---|
|Year|Mean|Median|sd|Mean|Median|sd|
|1986|24.41|0.00|389.89|–|–|–|
|1994|23.11|0.00|376.21|16,713.55|1,444.03|25,204.14|
|1999|24.90|0.00|393.58|445,492.00|55,543.83|617,044.60|
|2004|48.57|0.01|1,000.78|753,410.80|111,578.70|1,010,617.00|
|2009|53.09|0.01|1,093.79|1,271,577.00|220,128.80|1,680,713.00|
|2014|56.19|0.01|1,152.08|1,364,670.00|257,035.20|1,814,512.00|

_Sources_ : DeLorme, aaa and Economic Censuses (inegi). _Notes:_ Mean and median market access and market potential values are calculated over the sample of localities in Mexico.

On Figure 3, we represent market access and market potential at the locality level (overlayed on the road network) in 1986 and 2016. For comparison purposes, on both maps, the four categories correspond to the 1986 municipality quartiles of market access or market potential respectively. Although both maps show improvements along our two indicators, the contrast is starker with the market potential measure. This is understandably due to larger increases in real per capita income than in population.

> 12We use the real gross production provided by the economic census at the municipality level to proxy for local income.

> 13Also note that to increase accuracy of our measures, for metropolitan areas, we calculate market access and market potential by first calculating municipality level formulas (1) and (2) excluding all localities _j_ that belong to the same metropolitan area as municipality _i_ . We then compute a metropolitan area weighted average of these municipality indexes using municipality area as the weight.

10
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kilometers of roads within a circle of 200 km radius across localities for the 1986-2014 period. It can be seen that accessibility according to this variable has increased on average by 19.1 percent.

## **Access to external markets**

Finally, to account for access to external markets, we also construct the minimum travel time and minimum travel cost (using the Pérez Cervantes and Sandoval Hernández, 2015 formula) to one among six major Mexican ports (documented by inegi) and to one among forty-four entry ports to the u.s. (documented by the u.s Department of Transportation, see us dot, 2014). Table 12 in Appendix A provides the mean and median minimum travel time and minimum travel cost to these ports and u.s. border entry ports. Over the 1986-2014 period, access to international markets slighlty improved on average as we measure a decrease in the average minimum travel time (by 8 percent) and in the average minimum travel cost (by 3 percent) to a u.s. border entry port. The same is true for the average minimum travel time and travel cost to a major port (which respectively decreased by 6.1 and 1 percent).

# **. 3 5 Trends in industrial concentration and specialization**

## **3.5.1 Trends in industrial concentration: 2004 – 2014**

We examine how industrial concentration evolved between 2004 and 2014. Although several measures are available in the literature, we resort to the Ellison and Glaeser concentration index (see Ellison and Glaeser, 1997), henceforth denoted eg. Using locality-level plant data and both the 4-digit and 6-digit naics industrial classifications, we calculate eg indexes for all industries and for manufacturing only, for the years 2004, 2009, and 2014 (see Appendix B for the formula and details).

Table 3 reports our results, which leads to the three following comments. First, industries have become more geographically concentrated over the 2004-2014 period. This can be seen in the observed increase in the mean value of the eg index from 0.381 in 2004 to 0.430 in 2014 (a 10.3 percent increase). Second, manufacturing industries are on average more concentrated than overall industries in Mexico. The mean value of the manufacturing eg index is on average 12 to 15 percent higher than the mean value of the eg index calculated for all industries taken together. Third, applying commonly agreed upon thresholds in the literature, about 98-100 percent of industries (for overall industries and for manufacturing) can be said to be concentrated. The fraction of concentrated manufacturing industries (98 percent) is greater than the one reported for Canada (75 percent) in Behrens and Bougna (2015) and is roughly similar to the one reported for the u.s. (97 percent), France (95 percent), and the u.k. (94 percent) formulas and is made to reduce endogeneity issues (see the econometrics section below).

12
in Ellison and Glaeser (1997), Maurel and Sédillot (1999), and Duranton and Overman (2005) respectively. Fourth, the average level of concentration increases as one moves from the 4 to the 6-digit industry classification (concentration is relatively more intense among more specific industry segments). This result is consistent with Rosenthal and Strange (2003) for the u.s. case and with Behrens and Bougna (2015) in the case of Canada.

Table 3: Mean and median eg indices, naics 4- and 6-digit industries (2004 – 2014)

||2004||2009|2014||
|---|---|---|---|---|---|
||Manufacturing|Overall|Manufacturing<br>Overall|Manufacturing|Overall|
||||naics 6-digit Industries|||
|Mean|0.381|0.256|0.388<br>0.26|0.43|0.282|
|Median|0.284|0.146|0.297<br>0.156|0.319|0.154|
|Minimum|-0.436|-0.739|0.045<br>-0.168|0.031|0.005|
|Maximum|1.001|1.006|1<br>1.006|1.353|1.013|
|Share_<_0|1.23|0.95|–<br>0.27|–|–|
|Share_∈_(0, 0.05]|1.63|20.14|2.1<br>20|1.61|21.85|
|Share >0.05|97.14|78.91|97.9<br>79.73|98.39|78.15|
||||naics4-digit Industries|||
|Mean|0.344|0.222|0.348<br>0.216|0.359|0.236|
|Median|0.264|0.116|0.273<br>0.12|0.285|0.118|
|Minimum|-0.085|-0.225|0.033<br>0.004|0.02|0.005|
|Maximum|1|1|0.992<br>0.997|0.999|1|
|Share_<_0|1.16|0.38|–<br>–|–|–|
|Share_∈_(0, 0.05]|2.33|25.95|3.49<br>24.23|3.49|25.84|
|Share_>_0.05|96.51|73.66|96.51<br>75.77|96.51|74.16|

_Notes:_ Mean and median values for 248 (resp. 245 in 2004, and 238 in 2009) 4-digit and 6- digit naics classification of industries. Share _<_ 0 means ‘not clustered’. Share _∈_ (0, 0.05] means ‘weakly clustered’. Share _>_ 0.05 means ‘strongly clustered’. See Ellison and Glaeser (1997) for details.

Figure 4 plots the distributions of the eg indices for the 6-digit industries in 2004, 2009, and 2014, for all industries (left panel) and for manufacturing only (right panel). As can be seen, these distributions are skewed towards 0.05, which shows that many industries are highly agglomerated, whereas only few of them are weakly agglomerated (the eg index is positive but smaller than 0.05).

# **3.5.2 Trends in specialization : 2004 – 2014**

We now examine how specialization evolved between 2004 and 2014. For each localilty and for the years 2009 and 2014, we calculate the Krugman specialization index (henceforth denoted 13
Figure 4: Distribution of the eg Index (naics 6-digit) in 2004 (top panel), 2009 (middle panel), and 2014 (bottom panel). All industries (left panel) and manufacturing (right panel).

<!-- Start of picture text --> EG index of all industries (6−digits) in 2004 EG index of manufacturing industries (6−digits) in 2004<br>−1 −.5 0 .5 1 1.5 −1 −.5 0 .5 1 1.5<br>EG index EG index<br>EG index of all industries (6−digits) in 2009 EG index of manufacturing industries (6−digits) in 2009<br>−1 −.5 0 .5 1 1.5 −1 −.5 0 .5 1 1.5<br>EG index EG index<br>EG index of all industries (6−digits) in 2014 EG index of manufacturing industries (6−digits) in 2014<br>−1 −.5 0 .5 1 1.5 −1 −.5 0 .5 1 1.5<br>EG index EG index<br>200 60<br>150<br>40<br>100<br>Number of industries Number of industries 20<br>50<br>0 0<br>200 60<br>150<br>40<br>100<br>Number of industries Number of industries 20<br>50<br>0 0<br>200 60<br>150<br>40<br>100<br>Number of industries Number of industries 20<br>50<br>0 0<br><!-- End of picture text --> 14
KSI) at the 4-digit naics level, both for employment and output levels (see Appendix B for the formula and details). As standard in the literature, we consider as highly specialized localities for which the specialization index is greater than 0.75 and as not specialized, localities for which the specialization index is below 0.35. Our results, shown in Table 4, can be summarized as follows. First, localities are more specialized in terms of output than in terms of employment. The average output specialization index is on average 10 to 12 percent higher than the average employment specialization index. Second, there is an increasing trend toward specialization over the period, both in terms of output and employment specialization.

Table 4: Mean and median specialization indices in Mexico : 2004 – 2014

||2004<br>Employment|Output|2009<br>Employment|Output|2014<br>Employment|Output|
|---|---|---|---|---|---|---|
|Mean|0.117|0.126|0.155|0.174|0.199|0.209|
|Median|0.014|0.030|0.021|0.063|0.046|0.087|
|Minimum|0.000|0.000|0.000|0.000|0.000|0.000|
|Maximum|1.972|1.978|1.963|1.983|1.984|1.973|
|Share⩽0.35|91.750|90.72|88.90|88.20|85.20|84.73|
|Share_∈_(0.35, 0.75]|2.200|3.28|2.71|4.15|4.19|6.05|
|Share_>_0.75|6.040|6.00|8.40|7.65|10.61|9.22|

_Notes:_ Mean and median values of the Krugman Specialization Index (ksi) for the 316 metropolitan area and 1,832 standalone municipalities in Mexico. ksi ⩽ 0.35 means ‘not specialized’. ksi _∈_ (0.35, 0.75] means ‘weakly specialized’. ksi _>_ 0.75 means ‘highly specialized’.

Figure 5 maps the employment and output specialization indices in 2004 and 2014. These show the trend towards as can be seen the dots maps increasing locality specialization by switching to yellow (weakly specialized) or red (highly specialized).

To identify the factors that drive these changes in the employment and specialization of localities, we will resort to a multivariate analysis.

# **4 Econometric specification and identification issues**

## **.1 4 Econometric specification**

We take advantage of the panel nature of our data to estimate the effects of road improvements on the localization of economic activities and on the specialization of localities in Mexico. Our main empirical specification is the following ’market access’ model: 15
<!-- Start of picture text --> N<br>~e oe . w E<br>a a o<br>e e *e% e ° . $<br>. ° Ont0° 80Para:<br>. TP ott °° e aN > Te a a et<br>° 5 ooee+hee°,a80,0ce 0 8 Coe40oi eee eo28egeoRosefeo S ST8a. SESof ener Sauces8ee| acreeteP%Ty o 8 PS oeege H.<br>| WEEErat CaeSpA PC PnARs ign DAaieatest<br>fewenatiedeetema)oy rae . “:VIERRe aadaRze"<br>Aa RS a ~<br>5<br>KSI, Employment (2004) egcoht sectee Ay ‘ °<br>* isic03s Noes<br>aaa ee hat Saad<br>Soeteg e et rhe a Oe a<br>© 0.35 <ksis 0.75 “hfe sete fee gee, oes<br>s Ksiso7s ~ = 5oteaeRies a,tT Bosa age OSeee<br>Road Type (2004) a: “eea me ote2 a<br>° Sour"<br>TwotanesMultiane divided orivieed feooo *yee ae| oh ae° 2 Nee‘goe<br>PavementGravelravel or Earth road “etPoN ..Ogace° 5tHe%9 0 x * 20 750 500, 1000 Kilometers<br><!-- End of picture text -->

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Specifically, we regress the log value of employment and/or of the specialization index of locality _m_ and year _t_ on the log of the market access (ma) or of the market potential (mp), so that our coefficients estimates can be interpreted as elasticities. Standard errors are clustered at the locality level to adjust for heteroskedasticity and within-locality correlation over time. The regression sample is a balanced panel of 2,094 localities with employment data for the years 1986, 1994, 1999, 2004, 2009, and 2014. The specialization and concentration indices are calculated at the establishment and locality levels. Since the latter micro-geographic data only span the 2004-2014 period, we will therefore restrict our analysis on specialization to this time period.

In addition to the ’market access’ specification, we also estimate an ’infrastructure model’ where we substitute market access with our measure of efficient roads within a fixed radius surrounding a locality’s centroid. In that model, we also incluse a measure of access to international markets (measured by the minimum time or travel cost to a port or port of entry into the u.s.).<sup>15</sup> Table 5 presents the summary statistics for the key variables of our analysis and the overall, between and within standard deviations.<sup>16</sup>

# **.2 4 Identification issues**

The panel structure of the data allows us to control for location-specific time-invariant factors and general macroeconomic trends. However, we need to account for the three following problems: (i) omitted variables bias, (ii) the non-random road placement of roads, and (iii) the structural recursion problem with the use of market access regressors.

**Omitted variables bias** . In our framework, the problem of omitted variables is mitigated by the panel structure of the data. We include locality fixed effects in the panel estimation, which absorb all time-invariant local characteristics such as initial wealth. Therefore, the estimated effects of market access and market potential cannot be attributed to time-invariant locality differences. This is an advantage over a cross-sectional framework, where unobservable location characteristics that explain outcomes could be correlated both with surrounding population or income and transport infrastructure that enter into the calculation of MA and MP indicators.

Despite the use of panel data and the different controls included, our model is still potentially subject to problems of simultaneity bias or reverse causality. In this respect, the two main empirical challenges with our model specification are the non-random placement of roads

> 15It is not desirable to introduce our measures of access to external markets in the market access specification because of their potentially high correlation with our market access or market potential indicators.

> 16The latter decomposition confirms the presence of time variation in our data (although most of the variation remains cross-sectional).

17
Table 5: Key variables and summary statistics

|**Variables**|**Observations**|**Mean**|**S**<br>**Overall**|**tandard Devi**<br>**Between**|**ation**<br>**Within**|
|---|---|---|---|---|---|
|Total employment|12,564|6,812.303|90,112.890|85,792.800|27,619.900|
|Manufacturing employment|12,564|1,998.672|23,597.920|23,277.810|3,901.393|
|Commerce employment|12,564|2,149.258|26,322.070|25,274.430|7,369.452|
|Service employment|12,564|2,229.871|37,222.180|33,232.070|16,779.740|
|Mining employment|12,564|62.447|1,149.786|642.266|953.764|
|Market access|12,564|22.496|399.887|396.782|50.361|
|Market potential|10,470|365,276.400|573,321.300|430,809.700|378,379.700|
|Employmentksi|5,286|0.237|0.445|0.359|0.266|
|Outputksi|5,286|0.305|0.495|0.397|0.303|
|Education (literacy rate)|12,408|0.046|0.049|0.034|0.035|
|Population|12,564|47,149.370|445,092.100|442,553.500|48,283.380|
|Effciency roads within200km|11394|12.440|0.419|0.411|0.081|
|Minimum travel time to port|12,544|6.885|0.551|0.544|0.090|
|Minimum travel time to border|12,544|6.300|0.543|0.531|0.113|
|Minimum travel cost to port|12,544|0.795|0.136|0.134|0.024|
|Minimum travel cost to border|12,544|0.648|0.121|0.119|0.020|

_Note_ : the above descriptive statistics are calculated on a sample of 12,564 observations covering 2,094 localities over 6 different dates. The standard deviation is decomposed into between and within components, which measure the cross sectional and the time series variation, respectively.

and a recursion problem inherent with regressions of local employment or income variables on market access or market potential measures. We discuss below how we address these issues.

**Non-random road placement and the construction of the ‘doughnut’ IV.** The expansion of the road network surrounding a locality can be endogenous and this may create a correlation between increases in local market access or local market potential and our left-hand side variable, for instance employment. This may occur if road construction occurs in localities that would otherwise have experienced relative increases or decreases in average employment (our review of road investments in Mexico suggests that roads may have been built to increase accessibility in lagging regions during the studied period). To deal with that problem, we adopt the so-called ’doughnut’ instrumentation strategy as proposed by Jedwab and Storeygard (2017). The basic idea behind this approach is to instrument the market access or market potential with similar measures excluding surrounding areas within a fixed radius (in addition to ‘own locality’ exclusion). Variations in this instrument are more likely to be exogenous. Following the identification strategy of Jedwab and Storeygard (2016) also implemented by Blankespoor et al. (2017), we construct the counterfactual measures of market access and market potential

18
excluding all localities _j_ located within a 25, 50 or 75 km radius of locality _i_ , hence the reference to a ‘doughnut’.<sup>17</sup>

**The ’recursion problem’ or the structural endogeneity of market access and market potential** . When considering an explained variable which is correlated with other variables that enter the calculation of the accessibility regressor in formulas (1) or (2)—for instance when regressing total employment on market access in Equation (3)—a recursion problem is bound to emerge, even when excluding ‘own locality’ from the measure of accessibility. To see this, notice that locality _i_ ’s market access is a function of locality _j_ ’s population, which in turn is correlated with locality _j_ ’s employment, which is a function of locality _j_ ’s market access, and thus of locality _i_ ’s population. Locality _i_ ’s employment is then structurally correlated with its own market access in the absence of causal effect of market access on Fol- any employment. lowing Baum-Snow et al. (2017), we address this problem by calculating either counts of road intersections or a ‘doughnut’ of efficient road lengths in a given radius (in a similar way to the market access and market potential ‘doughnuts’).<sup>18</sup> We then use the road count or efficient road variable instead of the market access or market potential indicator, or use it to instrument market access or market potential. Sources of variations in ‘accessibility’ are then only due to variations in roads, thus avoiding the recursion problem.

The validity of our three instruments (both for market access and market potential) is tested by means of the Kleibergen-Paap statistics, which is robust in the presence of heteroskedasticity. After performing this test, we reject the hypothesis of weak instruments as the rule of thumb suggested by Staiger and Stock (1997) that the statistic must be larger than 10 is satisfied. As for the exogeneity of the instruments, we rely on the Hansen-J statistics and strongly accept the hypothesis of exogenous instruments.<sup>19</sup> The coefficients on market access and market potential are positive and statistically significant in all second stage regressions.

To differentiate the role of access to domestic and external markets, we also estimate the infrastructure model as previously discussed. In this model, domestic market access is proxied

> 17Because of the mixed nature of our sample, for metropolitan areas, we first compute these counterfactual measures at the municipality level, excluding both municipalities within a same metropolitan area and municipalities within our distance threshold. We then define the metropolitan area ‘doughnut’ as the weighted average of these municipality indices, using municipality area as weights.

18In this ‘doughnut’, the exclusion of surrounding areas addresses endogeneity of road placement. 19See Baum et al. (2017) for a detailed explanation of test implementation in stata and for references. It is worthwhile noticing that, besides being efficient, our estimation results are also consistent with respect to the absence of heteroskedasticity or autocorrelation because of the Stock-Yogo weak identification employed for the estimation. In all our iv regressions, the first stage f-statistic is large, suggesting that the instrument is strong (Stock and Yogo, 2005).

19
by efficient road length<sup>20</sup> and access to external markets by the minimum travel cost or travel time to a major port and to the u.s. border. One key advantage of this approach is that the infrastructure measure road does not have a structural on (efficient length) dependence population (like the market access) or on income (like the market potential). However, this infrastructure measure may still suffer from non-random placement. To deal with this problem, we use as an instrument the ‘doughnut’ road efficient length. As a robustness check, we also report the results from cross-section regressions where the efficient road length of the corresponding year is instrumented with the 1949 efficient road length.<sup>21</sup>

# **Estimation Results 5**

We exploit changes in the transportation network over the 1986 – 2014 period to estimate the effect of market access and market potential on localities employment and specialization in Mexico.

## **.1 5 The effect of market access and market potential on employment**

Table 6 reports estimates from the regression of employment on market access, in which our market access measure is instrumented in three ways: using the doughnut market access, the road count within a 10 km circle, and the efficient road length within a 100km radius. The first column (ma only) reports our basic estimates without any controls. The second column reports the ols estimates of the full model, and the last three columns report result of the instrumented regression with each one of our three instruments. As can be seen from the instrumented regressions, there is a positive and significant causal effect (iv) of market access on employment. Note that first stage coefficients (not shown) are positive and significant, which means that each market access measure is predicted by the appropriate instrument.<sup>22</sup> Results indicate that between 1986 and 2014, a 10 percent increase in market access resulted in a 1.6 to 2.1 percent increase in employment.

Note that we use as interaction terms, population and education variables (dummies equal to one if the locality has population or education above the median of the initial 1986 levels for the market access regression or above the median of the initial 1994 levels for the market potential regression). Also note that more urbanized locations benefited more from an increase

> 20Efficient road length is calculated for different radii: 50, 75, 100, 150, 200, and 300 km.

> 211949 is the earliest date for which we could recover information on road extent and road types from paper maps. This historic measure can be argued be exogenous to economic outcomes observed more than 40 years later.

> 22These first stage results are available upon request.

20
in market access, while areas with a more educated population benefited less from an increase in market potential. This suggests a stronger impact of accessibility in larger places with lower skilled workers.

The estimates also provide evidence that the positive impact of improved domestic market access on employment was partially attenuated after nafta came into effect (the net impact is still positive though). One explanation of this result could be that under nafta, access to international markets started playing a relatively more important role, especially the northern localities given their proximity to the u.s.. Therefore, our result may point towards a substitution effect of international market access to domestic market access, an issue we explore in further detail below, in the section describing the infrastructure regressions. Finally, it is also noticeable that improvements in market access had large beneficial and statistically significant effects on localities with oil reserves (mostly located in the South).

Table 6: The effect of market access on employment

||||Total em|ployment||
|---|---|---|---|---|---|
|Variables|maonly|ols|<br>iv (Doughnut)|<br>iv (Road count)|iv (Effcient roads)|
|Market Access (ma)|0.176***|0.149***|0.208***|0.163***|0.170***|
||(0.0153)|(0.0111)|(0.0144)|(0.0202)|(0.0219)|
|max Metropolitan dummy||0.0717***|0.0528***|0.0629***|0.0574***|
|||(0.0117)|(0.0146)|(0.0154)|(0.0164)|
|max Education||-0.0468***|-0.0486***|-0.0474***|-0.0452***|
|||(0.00334)|(0.00403)|(0.00329)|(0.00339)|
|maxnaftadummy||-0.114***|-0.119***|-0.115***|-0.117***|
|||(0.00233)|(0.00251)|(0.00213)|(0.00219)|
|max Capital city||-0.0225|-0.0294|-0.0228|-0.0237|
|||(0.0226)|(0.0390)|(0.0250)|(0.0251)|
|max Oil dummy||0.839**|0.756*|0.832**|0.809**|
|||(0.385)|(0.403)|(0.399)|(0.401)|
|Above median education||0.155***|0.124**|0.155***|0.146***|
|||(0.0581)|(0.0550)|(0.0522)|(0.0508)|
|Above median population||0.851***|0.736***|0.805***|0.779***|
|||(0.0803)|(0.0823)|(0.0918)|(0.0959)|
|Constant|6.836***|5.790***||||
||(0.0844)|(0.0755)||||
|Observations|11379|11379|9778|11251|10293|
|Adj. R-squared|0.052|0.423|0.398|0.424|0.440|

_Note_ : * denotes significance at the 10% level, ** denotes significance at the 5% level, and *** denotes significance at the 1% level. The doughnut iv is calculated by excluding all localities within a 25km circle. The road count IV is the number of roads intersecting a circle of 10km radius. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 25 and 100 km radii.

Table 7 reports estimates from the regression of employment on market potential, under ols and iv. Overall, we find a positive and significant causal effect of market potential on employment. The first column (mp only) reports our basic estimate without any controls and 21
shows a positive and statistically significant estimated coefficient: a larger market potential is associated with greater employment. Instrumentation shows that the effect remains large: A 10 percent increase in market potential is associated with a 2.9 to 6.5 percent increase in total employment. Interestingly, more urbanized locations benefited less from increases in market whereas areas with more educated more. These results are at potential, population benefited odds with what we found in the market access regression. This indicates that market access and market potential are not completely substitutable. One potential channel to explain the positive market potential impact on employement growth in more skilled localities may be that greater market potential (which is a function of surrounding incomes) increases the demand for goods which have stronger skill contents.

Table 7: The effect of market potential on employment

||||Total emp|loyment||
|---|---|---|---|---|---|
|Variables|mponly|ols|<br>iv (Doughnut)|<br>iv (Road count)|iv (Effcient roads)|
|Market Potential (mp)|0.192***|0.351***|0.322***|0.292**|0.645***|
||(0.00324)|(0.0401)|(0.0484)|(0.148)|(0.0342)|
|mpx Metropolitan dummy||-0.0180***|-0.00188|-0.00954|-0.0680***|
|||(0.00652)|(0.00822)|(0.0232)|(0.0144)|
|mpx Education||0.0168***|0.0188***|0.0195***|0.000723|
|||(0.00207)|(0.00286)|(0.00705)|(0.00270)|
|mpxnaftadummy||-0.0591***|-0.0557***|-0.0424|-0.141***|
|||(0.0127)|(0.0140)|(0.0423)|(0.00928)|
|mpx Capital city||-0.00822*|-0.0113**|-0.0108**|-0.0109*|
|||(0.00430)|(0.00450)|(0.00427)|(0.00646)|
|mpx Oil dummy||0.0227|0.0168|0.0248|0.0135|
|||(0.0157)|(0.0190)|(0.0172)|(0.0229)|
|Above median education||0.187***|0.151***|0.185***|0.203***|
|||(0.0417)|(0.0404)|(0.0374)|(0.0359)|
|Above median population||0.406***|0.219**|0.302|0.973***|
|||(0.0872)|(0.101)|(0.267)|(0.171)|
|Constant|3.749***|2.134***||||
||(0.0382)|(0.334)||||
|Observations|10229|10229|8168|10098|9248|
|Adj. R-squared|0.463|0.503|0.511|0.503|0.471|

_Note_ : * denotes significance at the 10% level, ** denotes significance at the 5% level, and *** denotes significance at the 1% level. The doughnut iv is calculated by excluding all localities within a 25km circle. The road count IV is the number of roads intersecting a circle of 10km radius. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 25 and 100 km radii.

# **.2 5 Spatial and sectoral heterogeneous effects**

Market access and market potential have heterogeneous effects on employment in different sectors and different economic regions. We report the estimated results for the three main sectors, namely services, manufacturing, and commerce. The results from the iv estimations 22
are reported in Table 8. The first three columns show the estimated coefficients for market potential and the last three columns for market access. Compared to other sectors, it is the services sector that benefits most from an increase in market access or market potential.<sup>23</sup> A 10 percent increase in market potential is associated with 1.2 percent, 3.0 percent, and 4.1 percent increases in employment in the manufacturing, commerce, and services sectors respectively. Similarly, a 10 percent increase in market access is associated with 1.6 percent, 1.8 percent, and 2.5 percent increases in employment in manufacturing, commerce, and services respectively.

Results also show that more urbanized or more educated localities benefit more from an increase in market potential, and less from an increase in market access.<sup>24</sup> Finally, localities seem to benefit less from an increase in domestic market access or domestic market potential during the nafta period. As mentioned earlier, the net effect is positive, and the result could be explained by the increasingly important role of international market access at the expense of domestic market access under trade openness.

Table 8: Sectoral heterogeneous effects of market access and market potential

||Market Pot|ential: Emplo|yment|Market Ac|cess: Employ|ment|
|---|---|---|---|---|---|---|
|**Variables**|Manufacturing|Commerce|Services|Manufacturing|Commerce|Services|
|Market Potential or Market Access|0.119*|0.303***|0.413***|0.158***|0.176***|0.246***|
||(0.0623)|(0.0418)|(0.0807)|(0.0184)|(0.0125)|(0.0807)|
|mpormax Metropolitan dummy|0.0332**|0.00657|-0.0148|0.0363*|0.0711***|-0.0148|
||(0.0139)|(0.00681)|(0.0161)|(0.0193)|(0.0133)|(0.0161)|
|mpormax Education|0.0266***|0.0195***|0.0217***|-0.0399***|-0.0407***|0.0217***|
||(0.00451)|(0.00241)|(0.00518)|(0.00590)|(0.00342)|(0.00518)|
|mpormaxnaftadummy|-0.0146|-0.0596***|-0.0744***|-0.0850***|-0.101***|-0.0744***|
||(0.0173)|(0.0122)|(0.0221)|(0.00356)|(0.00206)|(0.0221)|
|mpormax Capital city|-0.00819*|-0.0117***|-0.00924|-0.0470**|-0.0121|-0.00924|
||(0.00427)|(0.00379)|(0.00568)|(0.0228)|(0.0363)|(0.00568)|
|mpormax Oil dummy|-0.0200|0.0163|-0.000259|-0.391|0.683***|-0.000259|
||(0.0237)|(0.0253)|(0.0276)|(0.485)|(0.205)|(0.0276)|
|Above median education|0.197***|0.125***|0.152***|0.234***|0.0795*|0.152***|
||(0.0648)|(0.0295)|(0.0492)|(0.0714)|(0.0441)|(0.0492)|
|Above median population|-0.299*|0.166*|0.438**|0.513***|0.829***|0.438**|
||(0.171)|(0.0863)|(0.192)|(0.102)|(0.0779)|(0.192)|
|Observations|7839|8144|7726|9165|9747|7726|
|Adj. R-squared|0.168|0.534|0.412|0.162|0.403|0.412|

_Note_ : * denotes significance at the 10% level, ** denotes significance at the 5% level, and *** denotes significance at the 1% level. The estimates are from the instrument regression with the doughnut iv calculated by excluding all localities within a 25km circle. Results are robust to others iv strategies (road count and efficient road units).

Table 14 and Table 15 in Appendix C show the results for the instrumented regressions of market access and market potential on total employment for different subsamples stratified by region (border, capital, central, North and South). As can be seen, all things else equal, the North and the states from of market access or im- capital benefit greater impact improved 23The services sector accounts for about 60 percent of Mexico gdp 24This result is consistent with the use of a different human capital proxy i.e., locality literacy.

23
proved market potential. Interestingly, there is no significant effect of an increase in the market potential in the North, while the effect is important in the South (which is less developed).<sup>25</sup> This result thus sheds light on the potential benefit to enhance economic opportunities through investment in transport infrastructure in lagging regions. It is also in line with the evidence that Southern Mexico has a greater potential to generate positive spatial spillovers (Deichmann et al., 2004, Baylis et al., 2012 and Alvarez et al., 2017).

# **. 5 3 The effect of market access and market potential on specialization**

Table 9 presents the causal effect of market potential and market access on locality specialization. The first four columns present the impact on specialization in terms of output, whereas the last four columns present the impact on specialization in terms of employment.

A 10 percent increase in market access causes a 7 percent increase in output specialization and a 3.4 percent increase in employment specialization. In contrast, we find no significant impact of market potential on specialization, neither in terms of employment nor in terms of output. More urbanized locations were not differently affected than less urbanized locations.

Table 9: The effect of market access and market potential on specialization

||Kr|ugman Specializa|tion index:|Output|Krug|man Specializati|on index: E|mployment|
|---|---|---|---|---|---|---|---|---|
||Mark|et Potential|Mar|ket Access|Mark|et Potential|Mar|ket Access|
||ols|iv (Doughnuts)|ols|iv (Doughnuts)|ols|iv (Doughnuts)|ols|iv (Doughnuts)|
|Market Potential or Market Access|2.913***|0.154|0.455***|0.704***|1.884***|0.984|0.321**|0.339**|
||(0.494)|(1.863)|(0.176)|(0.231)|(0.436)|(0.815)|(0.128)|(0.160)|
|mpormax Metropolitan dummy|-2.245***|0.464|-0.127|-0.286|-1.433***|-0.590|-0.0410|-0.0833|
||(0.507)|(1.772)|(0.146)|(0.187)|(0.441)|(0.820)|(0.0942)|(0.0995)|
|mpormax Education|0.0694|0.172**|-0.259***|-0.291***|0.0752*|0.122**|-0.269***|-0.214**|
||(0.0448)|(0.0774)|(0.0768)|(0.0919)|(0.0432)|(0.0528)|(0.0798)|(0.105)|
|mpormax Capital city|0.00864|0.00877|0.0458*|0.0837***|-0.00180|-0.00183|0.0955***|0.102**|
||(0.00871)|(0.00988)|(0.0239)|(0.0304)|(0.0138)|(0.0135)|(0.0170)|(0.0439)|
|mpormax Oil dummy|-0.423|-0.577|1.009|0.950|-0.243|-0.233|1.785|1.756|
||(0.494)|(0.470)|(2.109)|(1.945)|(0.458)|(0.538)|(1.968)|(2.478)|
|Above median education|1.313***|1.264***|1.214***|1.286***|0.492|0.414|0.231|0.508|
||(0.353)|(0.431)|(0.390)|(0.418)|(0.403)|(0.424)|(0.423)|(0.390)|
|Above median population|29.22***|-4.382|1.503|0.994|18.32***|7.713|0.780*|0.476|
||(6.422)|(22.33)|(1.017)|(0.966)|(5.581)|(10.35)|(0.472)|(0.447)|
|Constant|-42.77***||-4.412***||-29.06***||-4.017***||
||(6.165)||(0.985)||(5.454)||(0.504)||
|Observations|4599|3628|4303|3628|4491|4149|4203|3556|
|Adj. R-squared|0.0649|0.0498|0.0234|0.0233|0.0385|0.0381|0.0200|0.0106|

_Note_ : * denotes significance at the 10% level, ** denotes significance at the 5% level, and *** denotes significance at the 1% level. The estimates are from the instrument regression with the doughnut iv calculated by excluding all localities within a 25km circle. Results are robust to others iv strategies (road count and efficient road units).

> 25Despite improvements, the per capita income in the northern states is two or three times higher than in the southern states, and the disparities in terms of other social and infrastructure indicators are even more dramatic.

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# **. 5 4 The effect of access to infrastructure on employment and specialization**

We now turn to the estimation of the direct impact of local road improvements using measures of access to infrastructure that do not involve population or income. The focus on infrastructure only allows us to avoid the structural endogeneity problem of market access and market potential regressors. In these regressions, we also introduce measures of access to international markets to contrast the effects of local and international accessibility. Following Baum-Snow et al. (2017) and Emran and Hou (2013), we regress our dependent variable—employment or specialization—on both measures of accessibility. For the local access variable, we consider our measure of road efficiency units. For access to external markets, we calculate two complementary measures: the minimum travel time or the minimum travel cost to a major port and to the u.s. border.<sup>26</sup> To correct for the non-random placement of roads, we also instrument local accessibility by applying the doughnut approach (i.e., we instrument our road efficiency measure with a doughnut of efficient roads). Finally, we also run a cross-section regression where our infrastructure metric is instrumented by the 1949 efficient road length.

As in the previous section, first stage estimated coefficients are positive and significant, which means that our efficient road length measure is predicted by the appropriate instrument. Table 10 reports the estimates in the regression of employment on road length efficiency, where road is instrumented the Better local access efficient length using doughnut approach. to roads has a positive and statistically significant effect on employment. These effects are large: a 10 percent increase in efficient road length leads to a 6.7 percent increase in total employment. There are also heterogeneous effects across sectors: the increase in employment in the commerce sector (7.1 percent) is greater than in the services sector (6.0 percent) or in manufacturing (4.3 percent).

As for access to external markets, a decrease in travel costs to the nearest u.s. border is positively associated with an increase in employment in each sector. We find that a 1 percent reduction in travel costs to the nearest u.s. border leads to a 1.9 percent increase in manufacturing employment, a 1.2 percent increase in commerce, and a 0.7 increase in services. These results are supported by the fact that the United States is, by far, Mexico’s leading partner in merchandise trade, and most of the Mexico-u.s. trade is through trucking. Note, however, that the impact of a reduction in travel costs to the nearest port is not positively associated with an increase in employment but with a decrease (for manufacturing in particular). An could be that travel costs to the u.s. facilitates and attracts em- explanation reducing exports ployment, whereas reducing travel costs to ports exposes industries to more competition and has a depressing impact on employment, especially in the manufacturing sector. Interestingly, when interacting with the nafta dummy (see Table 16 in Appendix C), we find that these two

> 26Minimum travel cost is calculated using the same calibrated trade cost function as in our market potential formula.

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opposite effects are magnified, consistently with the idea that trade openness results in local benefits for places that export but negative impacts for places that are exposed to competition with imported goods.

Table 10: The effect of road infrastructure on employment

||Total emp|loyment|Manufa|cturing|Serv|ices|Com|merce|
|---|---|---|---|---|---|---|---|---|
|Variables|ols|iv|ols|iv|ols|iv|ols|iv|
|Road Effciency|5.501***|6.633***|3.570***|4.301***|5.007***|6.044***|5.833***|7.061***|
||(0.0941)|(0.0911)|(0.144)|(0.141)|(0.0793)|(0.0719)|(0.110)|(0.104)|
|Min travel cost to border|-3.271***|-0.404|-3.808***|-1.887***|-3.354***|-0.730**|-4.242***|-1.222***|
||(0.431)|(0.404)|(0.669)|(0.634)|(0.363)|(0.318)|(0.470)|(0.437)|
|Min travel cost to port|1.522***<br>(0.467)|0.493<br>(0.406)|2.242***<br>(0.737)|1.509**<br>(0.660)|1.683***<br>(0.383)|0.743**<br>(0.333)|1.567***<br>(0.527)|0.573<br>(0.468)|
|Above median education|0.224***<br>(0.0460)|0.238***<br>(0.0416)|0.339***<br>(0.0681)|0.348***<br>(0.0630)|0.159***<br>(0.0363)|0.171***<br>(0.0325)|0.130***<br>(0.0475)|0.145***<br>(0.0438)|
|Above median population|0.427***<br>(0.0448)|0.404***<br>(0.0405)|0.314***<br>(0.0782)|0.304***<br>(0.0666)|0.415***<br>(0.0419)|0.394***<br>(0.0363)|0.420***<br>(0.0575)|0.396***<br>(0.0510)|
|Constant|-61.14***||-38.67***||-55.82***||-65.98***||
||(1.341)||(1.978)||(1.139)||(1.609)||
|Observations|11200|11200|10619|10614|11184|11184|10583|10575|
|Adj. R-squared|0.549|0.537|0.210|0.206|0.607|0.594|0.497|0.486|

_Note_ : * denotes significance at the 10% level, ** denotes significant at the 5% level, and *** denotes significance at the 1% level. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 150 and 300 km radii.

Finally, looking at the effects of road infrastructure on specialization, Table 11 shows that better local accessibility has a positive and statistically significant causal effect on specialization. A 1 percent increase in efficient road length leads to a 5.9 percent increase in the locality’s output specialization, and a 4.5 percent increase in the locality’s employment specialization. Interestingly, reducing travel costs to u.s. borders or ports has not significant impact on specialization, neither in terms of output nor in terms of employment.

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Table 11: The effect of road infrastructure on specialization

||Output Sp|ecialization|Employm|ent Specialization|
|---|---|---|---|---|
||||||
|**araes**|ols|iv|ols|iv|
|Road Effciency|4.092***<br>(0.906)|5.935***<br>(1.261)|2.735***<br>(0.829)|4.468***<br>(1.187)|
|Minimum travel cost to border|-2.089|0.340|-2.332|-0.0469|
||(3.075)|(3.217)|(2.399)|(2.694)|
|Minimum travel cost to port|6.172*|5.003|4.489*|3.382|
||(3.201)|(3.354)|(2.531)|(2.707)|
|Above median education|0.907**<br>(0.415)|0.917**<br>(0.394)|0.471**<br>(0.228)|0.480**<br>(0.234)|
|Above median population|0.977**|0.953**|1.033**|1.012**|
||(0.415)|(0.395)|(0.476)|(0.432)|
|Constant|-57.43***<br>(11.79)||-39.02***<br>(10.84)||
|Observations|4671|4517|4578|4410|
|Adj. R-squared|0.012|0.011|0.011|0.001|

_Note_ : * denotes significance at the 10% level, ** denotes significant at the 5% level, and *** denotes significance at the 1% level. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 150 and 300 km radii.

# **6 Conclusion**

Transport investments have the potential to stimulate growth through trade, structural transformation, agglomeration, and productivity. This paper uses extensive micro-geographic data and geo-referenced digitized maps of the transport network in Mexico over three decades to provide empirical evidence of the causal effect of improved access to markets following road improvements on local employment and specialization. In addition to shedding light on the effects of transport infrastructure (roads) on local economic development, this paper also provides evidence of the respective roles of domestic and external market access on the geography of economic activities.

To estimate the causal effect of roads on localities’ economic activity and their level of specialization, this paper addresses two important endogeneity issues, namely the potentially non-random placement of roads and the structural endogeneity of market access and market potential regressors, also called the recursion problem. For this, we adopt three separate idenWe use the so-called iv to address the common tification strategies. first ’doughnut’ strategy non-random placement of roads problem, by excluding surrounding localities up to 25 km. Second, we address the structural endogeneity of market access and market potential by using two raw measures of access to infrastructure (road count and road efficient length) that do not involve population or income (either as substitutes for market access indicators or as instruments).

Our empirical exercise sheds light on the causal effect of road improvements on the increas27
ing trends in specialization and geographical concentration of employment in Mexico, and confirms that transport investments can have large effects on local economic activity through The effects are A 10 increase in market ac- improved accessibility. quantitative large. percent cess results in a 1.6 to 2.1 percent increase in total employment, a 7 percent increase in output specialization, and a 3.4 percent increase in employment specialization. In a country where the share of the manufacturing sector has been declining, we find heterogeneity in sectoral effects, with employment in commerce and services benefiting more from road improvements than employment in manufacturing. We also provide evidence of the potential benefits of the investments in transport infrastructures in lagging regions. There is no significant effect of an increase in the market potential in the North, while the effect is large and important in the South. Results also show that localities in the South are the most specialized and the large effort of the Mexican government to increase accessibility in that region has played an important role.

Finally, we show that access to external markets matter but in an heterogeneous way. Reducing travel costs to u.s. borders stimulates employment, whereas reducing travel costs to ports depresses employment, especially since nafta came into effect. This is consistent with both theories of exports to the u.s. stimulating local development, and trade openness exposes localities to competition from imports.

Further investigation, however, will be needed to understand the possible mechanisms (tertiarisation of the economy, expansion of trade, or foreign direct investment) for the increase in specialization and employment caused by Mexico’s road improvement. For example, we do not know whether improved connectivity (through new or better roads) plays a role through improved accessibility to output markets, to intermediate inputs, to the pool of workers, or simply through reduction in travel times. We leave these important issues for further research.

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