2.1. Agglomeration Economies
A part of the literature of agglomeration economies follows Marshall [2
] by identifying the determinants of location, that is why the concepts input sharing, labor market pooling, and knowledge spillovers, are recurrent in the work on the mechanisms through which the agglomeration economies act. However, the concepts of sharing, matching, and learning, proposed by Duranton and Puga [3
], are the most used today. This is explained by the fact that Duranton and Puga [3
] developed, in relation to each mechanism, one or more models that they call “nuclear”, to theoretically support the economies of urban agglomeration.
With this objective in mind, Duranton and Puga are based on the works of Abdel-Rahman and Fujita [39
], Helsley and Strange [40
], Glaeser [41
], Scotchmer [42
], etc. According to these authors, the sharing mechanism is explained from four aspects. The first, relates to the indivisibility in the provision of certain goods whose cost is only possible to pay among several individuals or firms such as local infrastructure, which encourages the agglomeration of individuals in cities. The second is inherent to the possibility of obtaining gains from a wide variety of suppliers of inputs that can be sustained by a large industry of final goods. The third relates to the gains that result from the specialization of workers in a more limited set of tasks that can be sustained over time with greater production and sharing risks; and, the fourth with the possibility of sharing a common labor market i.e., a pool of workers with similar skills.
The matching mechanism relates to the expectation of correspondence in the relations between employers and employees that applies to those between buyers and suppliers or business partners [4
]. Specifically, with respect to the labor market, it is the correspondence of the professional qualification demanded in the labor market with the one offered. The information asymmetries existing in both groups regarding the requirements in terms of skills for a vacancy that the employer offers, generates training costs and remuneration of salaries that do not correspond to workers’ abilities. This incompatibility, according to the theory is reducible in an urban environment given that this (as a space of economic agglomeration) promotes the quality of each encounter and increases the opportunities for these to occur.
Finally, the learning mechanism attributes the gains from the generation, dissemination, and accumulation of knowledge. Duranton and Puga [3
] argued that it is an important activity from two perspectives. For the resources that are allocated to it, and for its contribution to economic development. However, they indicate that there is not a sufficiently developed theoretical understanding of learning in cities.
For its part, in the empirical field, the evaluation of the magnitude of urban agglomeration economies has prevailed over the works that have tried to identify the mechanisms that support them. Rosenthal and Strange [6
] and Combes and Gobillon [7
] gave an account of this. Rosenthal and Strange [6
] analyzed the empirical works related to the nature and sources of agglomeration economies, including among the latter, others not defined by Marshall [2
] and which include the effects of domestic markets, consumer opportunities, and the search for rents.
More recently, studies emerged that focus on the mechanisms through which agglomeration economies operate. Combes and Gobillon [7
] incorporated into their discussion of the empirical literature about agglomeration economies, those works that attempt to separately identify the role of the three types of mechanisms that underlie them, according to Marshall [2
], distinguishing between those that define concentration or co-agglomeration indices as a variable of interest and the birth of new firms. It is possible to present the empirical literature from two perspectives. One that seeks to demonstrate the existence of each of the micro foundations of agglomeration separately; and another in which what is sought to find the incidence of the three mechanisms jointly.
Regarding the sharing mechanism, the works of Holmes [43
] highlighted the fact that for the US, a more intensive use of inputs purchased within the same industry is shown. Amiti and Cameron [44
] showed that, for Indonesia, the benefits which come from the proximity of suppliers are quickly reduced with distance. López and Suedekum [45
] showed that, in Chile, significant positive effects of intra industrial spillovers related to input-output links exist.
Regarding the matching, the work of Diamond and Simon [46
], which showed for the US that individuals living in more specialized industrial cities face a higher probability of unemployment but in compensation earn higher wages, is relevant. Costa and Kahn [47
] who showed that couples where both spouses have higher education are located in large metropolitan areas. Gautier et al. [48
] used data from Denmark to contrast a model in which cities play an important role as marriage markets. According to their model, cities are dense areas where singles can meet more potential partners than in rural areas. To enjoy these benefits, they are willing to pay a premium in terms of higher housing prices. Once married, the benefits of meeting more potential partners fade and married couples leave the city. Gautier’s empirical results confirm the predictions of his marriage market model. On the other hand, Freedman [49
], Wheeler [50
] and, Beakley and Lin’s [51
] studies focused on the gains of the agglomeration that comes from an increase in labor mobility and a better correlation between employees and firms.
Likewise, the authors who have investigated whether the matching between workers and firms is more productive in the large and dense areas are Wheeler [52
] and Anderson et al. [53
] for the US; Figueiredo et al. [54
] for Portugal, and Andini et al. [55
] from Italy. Complementarily, among the jobs that lead the specialization of tasks as a source of urban agglomeration economies are Duranton and Jayet [56
] and Kok [57
], for France and Germany, respectively. The first provides evidence that the division of labor is limited to the extension of the local market. The second argues that in large cities, workers focus more on their basic tasks and develop fewer subtasks than workers in small cities.
Finally, among the studies related to knowledge spillovers, is the pioneering work of Jaffe et al. [58
] which determined that patent citations in the US are located geographically within the same state and metropolitan area. Audretsch and Feldman [59
] suggested that the propensity of innovative activity to be concentrated is attributable to the role of knowledge diffusion rather than to the geographical concentration of production. Agrawal et al. [60
] determined for the US that although spatial and social proximity increase the probability of knowledge flows between individuals, the marginal benefit of geographical proximity is greater for inventors who are not socially close. Agrawal et al. [61
] established that the inventors, hired by the large firms of the 72 most highly innovative localities in North America, were more likely to be based on the same previous inventions year after year, and the scope of the geographical impact of these inventions is also narrow. In Europe, Combes and Duranton [62
] for France, Brunello and Gambarotto [63
] and Serafinelli [64
] for Italy, Brunello and De Paola [65
] for the United Kingdom, Muehlemann and Wolter [66
] for Switzerland, investigated whether such contagions arise from the mobility of workers between firms within the same labor market. They found results consistent with what the theory proposes. Serafinelli [64
] showed that in the Veneto region, labor mobility can explain around 10% of the increase in productivity experienced by other firms, when new highly productive firms are incorporated into a local labor market.
With the aim of explaining the incidence of agglomeration mechanisms in spatial activity concentration patterns, agglomeration, and the birth of new firms, some works developed identifying mechanisms using different proxy variables. Rosenthal and Strange [67
] examined the importance of micro foundations of agglomeration economies in determining the industry concentration for the US measured by the Ellison and Glaeser index (EG), at the level of postal codes, counties, and states. The results showed a positive effect of labor pooling in the spatial concentration of the industry, while the knowledge spillovers also have a positive effect but only at the postal code level. The input sharing mechanism was also important but only at the state level and not at smaller geographic scales.
An indirect, alternative measure of labor pooling was proposed by Overman and Puga [68
] (they use the same proxies to measure the other agglomeration mechanisms that Rosenthal and Strange proposed [67
]). This was based on the hypothesis that a pool of workers with adequate skills allows firms to absorb productivity shocks more efficiently. Using panel data from the United Kingdom, they measured the importance of the concentration of labor by calculating the fluctuations in the employment of individual establishments regarding their sector, and the sector average. The authors found that industries which experience more volatility are more spatially concentrated. Barrios et al. [69
] determined that the concentration of industrial activity measured by EG, obeys different agglomeration mechanisms according to the country of analysis: Belgium, Ireland, and Portugal. In this case, their approaches to each mechanism differed from those described above. The sharing mechanism was approximated through the total purchases of goods and services. Matching was measured through the percentage of the total population that has obtained a higher education degree, and learning through total expenditure on research and development.
Other studies that use agglomeration mechanisms simultaneously to explain variables such as the growth of industrial employment, the entry of new manufacturing firms, the degree of co-agglomeration of the industry and the location of firms, correspond to Dumais et al. [70
], Glaeser and Kerr [71
], Ellison et al. [72
] and Viladecans-Marsal [73
], Jofre-Montsey et al. [74
] and Artz et al. [75
], respectively. The pioneering work of Dumais et al. [70
] stated that knowledge spillovers are the mechanism with the greatest incidence in the growth of industrial employment. On the contrary, Glaeser and Kerr [71
] concluded that the entry of new firms in an industry was higher in cities where industries that employ similar workers are more abundant. The co-agglomeration patterns of the industry as a function of the agglomeration mechanisms were examined in the work of Ellison et al. [72
]. In doing so, they calculated an index of co-agglomeration between two industries and then explained it from variables that approximate the links between each pair of firms (input sharing, labor pooling, and knowledge spillovers). The result suggested a great input sharing effect, followed by labor pooling.
] and Kerr and Komines [36
] conducted similar exercises. Kolko [76
] used as additional measures of the links between industries, variables related to the volume of inter industrial trade, evidence among other aspects, that the concentration of labor did not affect the localization decisions of services as it does in manufacturing, as well as the information technologies encourage the co-agglomeration of services that carry out transactions among themselves at the postal code level and discourage it at the state level. While they encourage the co-agglomeration of manufacturing at the zip code and county levels, they have no effect at the state level. This suggests that services are more urbanized but less agglomerated than manufacturing. Kerr and Komines [36
] calculated the spatial concentration index of Duranton and Overman (DO) for different industries and distances on which the incidence of work pooling and knowledge spillovers mechanisms was estimated. They suggested that establishments in industries with minor knowledge spillovers, or more labor pooling, are more concentrated. Specifically, for the case of Ecuador Torres et al. [38
], a concentration index was calculated and they found that the economic activity tends to cluster in relatively few cities, mainly Guayaquil and Quito, those cities that since colonial times have maintained their supremacy over others and, therefore, perform important functions economic, regional, and international.
By objectively quantifying the incidence of external economies in the location of firms measured by industrial employment, Viladecans Marsal [73
] added to the economies of location and urbanization, the analysis of technology transfers and the presence of suppliers. They determined that these two mechanisms are significant in explaining the location of industrial employment in Spain. By contrast, the work of Jofre Montsey et al. [74
] focused its analysis on determining the effects of agglomeration mechanisms in the location of new firms. In particular, what was estimated was the number of new enterprises by industry and city as a function of local employment levels in industries that (a) share input—output (input) relations, (b) use similar workers (labor market pooling), and (c) use a similar technology (knowledge spillovers). The results indicated that all these mechanisms are determinant, although their incidence differs depending on the geographic scale of analysis.
Furthermore, Artz et al. [75
] found that agglomeration economies matter in the decisions to locate new companies in the urban and rural markets of Iowa and North Carolina. Firms are more likely to locate in markets with an existing cluster of firms in the same industry, with greater concentrations of upstream suppliers or downstream customers, and with a larger proportion of college educated workers in the local labor supply. Their results also suggest that there is no additional advantage to agglomeration in urban areas relative to rural areas that have higher agglomeration endowments. However, rural communities that do not have a critical mass of firm clusters, upstream suppliers, downstream customers, and educated labor will find it difficult to attract new startups.
2.2. The Role of History
Externalities imply that history is important. According to the World Bank [77
], modern location patterns for the industry are highly influenced by the historical industrial environment of cities and by localization economies. Such intangibles include the local stock of knowledge relevant to an industry or workforce with specific skills acquired. In this context, the role of history in locating industries in cities has been analyzed from different perspectives in the context of agglomeration economies. Krugman [5
] illustrated past dependence on economic geography by describing the case of the US belt industry. He referred to history as a long shadow evident over location at all scales, from the smallest to the largest. According to Glaeser et al. [78
], the historical industrial environments of cities are important. From a study for 170 American cities between 1956 and 1987, they determined that, in fairly mature cities, urbanization economies stimulate industrial growth. Rauch [79
] argued theoretically that, in the post-World War II period, history reduces the mobility of industries from older, higher cost sites to new, low cost ones. Feldman and Florida [80
] found that in the US, among other aspects, there are particular places that have acquired comparative advantages for innovation and economic development of a product with a historical legacy of sustained investment.
Henderson et al. [81
] established the importance of preexisting conditions for industrial location. In this sense, cities with historical concentrations of an industry, and a related accumulation of local knowledge, offer a more productive environment for establishments in that industry than those without these preconditions. Consequently, such establishments will be able to compete better and eventually retain firms and employment in that industry. Henderson et al. [82
] established, for Korea, that the localization patterns are not dominated by accidents of history, but that they obey a comparative advantage inherent in the geography and history of each city. This term, “accidents of history” refers to the fact that by accident a lower location, from the beginning, can attract a small concentration of industry, attracting henceforth more new firms due to the accumulated information environment, even though the location generally has poor local attributes for most firms Henderson et al. [82
In Ecuador, as indicated above, the economic activity tends to agglomerate in relatively few cantons: Quito and Guayaquil, primary cantons since the mid nineteenth century a phenomenon that would not occur in any other Latin American country until the second half of the 20th century. Quito was an important urban center in the time of the Incas, which was further enhanced as such in the colonial era with the desire of the Spanish for control of space. In contrast, Guayaquil was used in the colony as a point of connection between Quito and Spain as a coastal city of the Pacific. It was not until the nineteenth century (with the establishment of the Republic, in 1830) when Guayaquil acquired a remarkable development, surpassing Quito in population volume. This trend continues to this day. Since then, these cities have important economic, regional, and international functions that suggest centuries of economic and political forces are intrinsic to them.
In this context, a natural question emerges, “Does history matter in decisions to locate new firms?”. Although it is not possible to examine in detail why such dynamism occurs in places with characteristics such as those described, it is possible to solve this question by introducing an inherent measure of productivity that reflects the access of the canton to the relevant markets for industries and other geographical, institutional, and cultural conditions. These affect attitudes towards the location of new firms. That is, each canton has a history with an inherent comparative advantage accumulated by each industry, which is represented by a variable that equals 1; when industry j in city c has been located between 1570 and 1900, and is zero, if the opposite happens.
Between 1500 and 1900, according to the time when the cantons hosted the industrial location highlight: Santo Domingo (1570, elaboration of food products); Sigsig (1870, other manufacturing industries; 1875, manufacture of clothing); Cuenca (1882, manufacture of food products, 1900, manufacture of chemical substances and products); Ambato (1883, manufacture of machinery-and equipment n.c.p., 1884, repair and installation of machinery and equipment); Quito (1885, manufacture of fabricated metal products, except machinery and equipment); Yaguachi (1890, manufacture of food products); Ibarra (1896, manufacture of food products); Guayaquil (1900, manufacture of food products); El Empalme (1900, manufacture of textile products); Ambato (1900, manufacture of clothing); Loja (1900, manufacture of clothing); Azogues (1900, manufacture of metal products, except machinery and equipment), and Girón (1900, manufacture of furniture).
Henceforth, the decades 1901–1910 and 1911–1920 represent, until now, the periods of less localization of the industry, since in these, only two and three industries were formed, respectively. As of 1921, this number increased considerably every decade, among which, the analysis period 2001–2010 has been the most fruitful (Figure 1