Abstract
This research investigates how agglomeration affects transport research assessment. Improvements of Public transport would increase economic productivity if they enable the cities densification and growth. This may thus increase economics of agglomeration economics. It has been claimed that the potential agglomeration advantages are large and thus understanding them better could be beneficial in making proper funding decisions concerning public transport improvements. This study has reviewed theoretical and empirical literature about effects of agglomeration on evaluating project assessment. The study also reviews some studies about transportation role in agglomeration. The theoretical literature is of great benefit in providing good understanding about how transportation improvements could affect agglomeration effects on transport project assessment. Related empirical studies focus on metropolitan regions and areas to generalize measure of transportation cost. Public transport effects on agglomeration are possibly to be similar from the impacts of road investment. This study tries to conduct a systematic review to generate desirable results for the topic under investigation.
Keywords: public transport improvement, agglomeration, transport project assessment.
- Introduction
Infrastructure investments of public transport could lead to higher density of employment clusters and even to denser, larger, and more diverse cities. By enabling agglomeration economies, these changes might, in turn, enhance firm productivity and improve consumer welfare. There is piece of evidence that there is a possible benefit for agglomeration effects in transport project assessment. It was reported by (Graham, 2007, p:12) that “application in the UK of guidance based on recent research resulted in an estimate of agglomeration benefits amounting to a 25% increase over the standard benefit estimate” Vickerman (2008, p: 45) reported that “because agglomeration externalities projects are potentially significant, but could vary a great deal among proposed projects, there may be a good reason for including estimates of their magnitude when deciding which public transport projects to fund. This could in fact be of great benefit in helping assess transport project”. Agglomeration externalities are extra to the travel time saving that is sometimes calculated when assessing proposals. In the US Small Starts and Federal Transit Administration’s programmes provide much funding for new projects of bus and rail rapid transit. It is in fact significant for applicant agencies to assess costs of construction, travel time savings, effects on land development and environmental impacts (Federal Transit Administration, 2010). Nevertheless, effects of agglomeration are not included. Some accurate estimates of agglomeration externalities are made for some essential projects in some countries such as the UK as discussed below. For this particular paper, I’ve reviewed empirical and theoretical literature to find out how public transport improves agglomeration and how this affects transport project assessment. There is a large body of relevant academic studies having used, various dependent variables to test productivity (e.g. changes in firm wages, land values and revenues), various independent variables such as density changes and accessibility changes). Strikingly, the relevant literature may not contain a direct study of public transport assessment, although there is a number of studies related to the topic under investigation.
- Review of Theoretical and Empirical Literature
“Agglomeration” refers to the density and size of cities. It sometimes refers to the density and size of groups of firms i.e. specialized industrial clusters or central business districts. It was argued by (Venables, 2007, p:31) that “firms sometimes cluster spatially with other firms in the same industry and sometimes with firms to which they have some cross-industry or supplier relationship. Households locate near firm clusters, and retailers locate
with each other in clusters near customers”.
In fact Organizations, and workers in these organizations, are sometimes more creative in agglomerations
Because of increasing returns to scale in production. These firms are also known as external agglomeration economies. This is why each firm that locates in the cluster contributes to benefits which are shared by the other businesses. Under such conditions, agglomeration size or density is smaller than the ceteris paribus and the social optimum.
Agglomeration external of economies are potentially essential in elaborating urban growth, urban spatial structure, regional competitiveness, and industrial productivity. As reported by (Rosenthal & Strange, 2008, p:22), “travel time is an important determinant of the interactions that give rise to agglomeration economies in two ways”. First, the reduction in travel time from a transport investment or service improvement can increase connectivity between and among firms and households. Second, travel time reductions can cause spatial densification over time near network nodes as firms and households make location choices seeking travel time advantages”. Public transport assessments have a potentially essential role to play in improving agglomeration growth.
The agglomeration outcomes of transportation improvements are of great benefit to transport project assessment. This may thus decrease travel time and make transportation easier in different ways. These service
Improvements are very relevant for public transport because each aspect of travel experience has public inputs, while for road use by automobiles, for instance, a significant part of the experience is provided. Anas et al. (1998, p:17) defined “economies of agglomeration” as “the decline in average cost as more production occurs within a specified geographical area”. Another definition “economies of agglomeration” is a higher productivity per an input unit as agglomerations get larger. Productivity could be tested in various ways i.e., gross domestic product or wages.
There are three aspects that characterize economies of external agglomeration which are essential both in terms of comprehending the phenomenon and from public transport assessment and policy. The first dimension is of whether.
the economy of external economy is with in industry or (cross)industry. The second dimension is about the mechanisms by which various business interactions between companies and/or households create benefits. The third dimension is the spatial scale over which these interactions emerge. This study review covers the first two dimensions/aspects. However, there are very few studies on the third dimension.
2.1 Within-Industry Versus Between-Industry Effects
There are two different types of agglomeration economies having been known for some time. Economies of “marshallian” or “localization” explain the productivity gains from co-location which are external to the company. However, internal to a particular industry, Marshall (1997, p: 20) reported that “producers within the same industry located near each other in order to share specialized local input providers, to benefit from a pool of skilled workers, and to have ready access to specialized information that was created by other firms. The development of a local, specialized labour market is one example of such effects”. “Jacobsian” or “urbanization” economies (after Jacobs, 1969) are external to the company and to the business, but internal to the regional/urban economies.
2.2 Mechanisms of Agglomeration
Duranton and Puga (2004) spoke about the agglomeration motivations or “mechanisms” to emerge within the framework of localization and urbanization. The authors emphasized on three mechanism types: sharing, learning and matching which include both across-industry relationship, within-industry, relationships and relationships between household customers and retailers. Agglomeration effects in very essential in assessing projects because of the sharing mechanisms consisting of sharing of inseparable facilities with relatively high fixed costs and large scale economies, such as airports, rail, transit networks and ports.
As reported by (Glaeser, Kahn &Rappaport, 2008, p:8) “firms also share pools of diverse input suppliers in order to enable the narrower specialization that leads to higher efficiency and to spread the risk of doing so, resulting in an increase in the number of suppliers rather than in an increase in the scale of existing suppliers”. Becker and Murphy (1992) demonstrated analytically that while greater labour division could produce extra costs of coordination, agglomeration may be able to generate this coordination easier by mitigating the negotiation costs between suppliers and buyers . “Negotiation costs are, in turn, integral to the vertical disintegration of production” (Williamson, 1979, p:16). Holmes (1999, p: 12) found that “the most geographically concentrated manufacturing industries exhibited the highest degrees of vertical disintegration and input sharing” (Rosenthal and Strange, 2004).
It was found (Rosenthal and Strange, 2004, p:42), when “there is a larger pool of labour for employers, and a larger pool of firms for workers, lowers production costs by reducing the amount of time to match skills and tasks, the time for firms to fill vacated or new positions, and both the travel time and the search time for workers to find jobs”. Thus, companies and workers are more attracted to agglomerations providing more workplace and work options, In this case, transport project assessments are likely to be of great benefit due to the fruitful outcomes generated by agglomeration. When assets are repossessed because of the failure of the project failure, they might be easily recycled in an urban-based agglomeration as it is easier to figure out a match; similarly.
When cities become relatively larger, it could be easier for entrepreneurs to get appropriate production machinery (Helsley and Strange, 1991). Workers also get benefit since they are able to move to other companies if one firm does not succeed. They could, therefore, be more possible, when workers live near large industrial clusters or in large cities with convenient jobs.
Firms that make risky investments might benefit from the firsthand experience of what has been done by others. Young companies need more time to try to find the proper mixture of inputs and processes for a process of an optimal production. (Tabuchi & Yoshida, 2000, p:15) stressed that “networking and other socially mediated interactions requiring face-to-face contacts are also likely to benefit from agglomerations”.
2.3 Agglomeration and Transportation
Improvements of transport may enhance the agglomeration size or density and the magnitude of economies of external agglomeration. Agglomeration relies on distance trade-offs and travel time among different inputs and amenities. Reduced time of travel makes matching, sharing and learning easier for firms and households. It thus increases the spatial scale over which these interactions could readily emerge. This, therefore, shows that agglomeration has an accessibility function. Some agglomerative changes which are caused by accessibility might not lead to economies of external. Agglomeration, as embodied in matching, sharing or learning, mechanisms. (Krugman, 1991, p:17) reported that “regional industrial agglomerations can develop when industries seek advantage in serving their markets by minimizing transportation costs while maximizing internal economies of scale in production”. A transportation project assessment and investment could cause a spatial concentration of firms that seek larger market areas enabling the internal economies realization of production scale. These are economies of agglomeration increasing productivity, that gains entirely accrue to individual businesses. Such a transportation investment leads to productivity that is not included in conventional project evaluation based on travel time savings. “These firm-to-market dynamics are unlikely to be affected by public transport systems, except indirectly” (Glaeser, Kahn & Rappaport (2008, p: 4). Economies of external agglomeration could be caused by transport improvements if these improvements can cause co-location of companies to get benefit from matching, learning and sharing mechanisms.
- Relevant Empirical Studies
In this section, we talk about two kinds of empirical studies: those that treat particular agglomeration mechanisms likely to be related for public transport assessment and improvements. The study sheds also light on a small number of studies having investigated productivity and agglomeration with a focus on the travel time role or travel investments. A large body of empirical studies on agglomeration economies have focused on manufacturing.
The most obvious clustering examples were seen historically. Notable exceptions include studies by Ciccone (2002), Ciccone and Hall (1996), Bru¨ lhart and Mathys (2008) and, Graham (2007a, 2007b), where the entire
Sub-sectors or economy of both services and manufacturing were examined.
In empirical studies, there is some evidence of the necessary sharing mechanisms on the role played in agglomerations by consequent risk reduction and labour pooling for household unemployment spells and firm firing decisions. Simon (1988, p:15) showed that “frictional unemployment is higher in more specialized cities using a Herfindahl index measure”, while Diamond and Simon (1990, p:19) showed that “wages are higher.
in more specialized cities, consistent with the theory that workers will demand higher wages as compensation for the increased risk of unemployment”. Public transport assessment and improvements are affected by agglomeration economies.
Agglomeration economies may increase by public transport improvements and assessment by facilitating labour market pooling and inducing city growth, particularly where “accessibility is increased between residences of low-wage workers and skill matchedJobs” (Glaeser et al. (2008, p: 17). Learning mechanisms, such as “knowledge spillovers”, are regarded one of the most essential sources of localization economies, however, they are not thought to be informal because they are uneasy to connect directly to agglomeration and thus poorly understood and challenge empirical testing (Rosenthal and Strange, 2004). (Rice, Venables & Patacchini 2006) investigated the geographical reach and magnitude of agglomeration economies in the UK employing a simulation framework and an employment measure of accessibility or “effective density”, which is consistent with (Venables ,2007). They authors analyzed different regional differences in average earning controlling for differences in techniques and sectoral composition and tried to elaborate the residual variations utilizing proximity to “urban mass” as tested by auto journey times. They did find that “a modest agglomeration elasticity of 0.05, with firms benefiting from proximity to other firms up to 80 min away. A 10% reduction in all driving times across the UK was estimated to deliver a productivity gain of 1.2%” (ibid, p:43).
Graham (2007a) formulated a model of productivity of firm-level as an agglomeration function defined as employment accessibility. The author calculated employing firm-level revenue data and aggregated to the ward level. Transport investments were modelled as reductions in travel time between wards. Graham (ibid, p:16) reported that there are “negative agglomeration elasticities for industries such as rubber-related products and medical and precision equipment and positive elasticities for other industries such as publishing and food manufacturing. The average elasticity was 0.129 for all-employment accessibility; for manufacturing and for services, the estimates were 0.07 and 0.20, respectively”.
Shefer and Aviram (2005) examined the benefits of potential agglomeration of a Light-rail public transport system in metropolitan area of the Tel metropolitan area. They applied agglomeration assessments which were taken from previous studies. Their model employed elaborated engineering-based calculations of the potential ridership and capacity of the system along with the various estimates of the employment gains in the CBD.
The recent ridership was induced travel rather than shifts of agglomeration economies mode and improvements of public improvements agglomeration. The agglomeration effects were based on the comparison of two scenarios. The first scenario was the incremental growth that occurred with economies of modest agglomeration because of the smaller center size. The other scenario was the employment growth that was abled to occur within the CBD. This created less job growth and a larger CBD outside that centre. The authors did calculate the potential economic benefits resulting from economics of agglomeration utilizing a main Cobb–Douglas production function as an extra $73–$355 million; which was around 22% of the total calculated benefits.
The UK provided official “guidance” for the agglomeration effects in transport project assessment. The UK also explained how the overall evaluation of the “extra” benefits of the investment of transportation, including the benefits of agglomeration (UK Department for Transport, 2010). The guidance has been standardized across various transportation types of projects. The guideline in fact consists of a mode-specific advice where there have been technical modelling problems and in parameters such as the time value. The effective density and employment accessibility measures were employed and spatial densification was ignored. The guidance in fact encompasses benefits of agglomeration as well as many other benefits including safety, economic impact, accessibility, safety effects and environmental impacts. This was done by using both qualitative and quantitative measures, which are assessed against the national objectives.
The UK guidance is very clearly formulated, non-proprietary, and accurately and precisely connected to economic theory. The guideline is utilized by local authorities and consultants within the UK. Similarly, Australia is applying the same methods. However, the most noticeable disadvantage of this guideline for analyzing projects of public transport outside Britain is that the elasticity estimates of agglomeration are based on the UK data. Agglomeration benefits in the UK approach are calculated based on estimates relating employment accessibility to economic output measures.
(UK Department for Transport, 2010) reported that “the accessibility measure accounts for the concentration of employment within a zone and interactions with every other zone, discounting by an exponential function of travel time or, more precisely, by the generalized cost of travel taken from a travel demand model with and without the specified project. This generalized cost measure gives weights to different modes according to current trip patterns. In places where auto trips dominate, as in most cities in the USA, such a measure would not represent public transport’s potential impacts on accessibility via agglomeration, and elasticity estimates applied
based on such travel time estimates would be incorrect”.
Understanding Agglomeration Mechanisms
Bru¨ lhart & Mathys (2008) reported that there is a lot to examine about the agglomeration-related mechanisms of economic growth regarding how the infrastructure of public transport affects agglomeration and how this directly affects transport project assessment. There are several reasons to be careful about generalising the few estimations available. Despite the availability of the abundant empirical evidence that industrial output is higher in denser and more reachable regions, the exact reasons for such relationships have not yet well-explained and thus understood (Costa & Kahn, (2000). There are instead various hypotheses having not been yet adequately tested.
Duranton & Overman (2005, p:14) reported that “agglomeration mechanisms is important, particularly if future development will not have the same characteristics as current development or if a study area economy is significantly different from that to which estimates are to be applied”.
Graham and Van Dender (2009, p:34) also concluded that “research aimed at understanding the specific sources of agglomeration mechanisms is needed to fully understand the effect of transport improvements”. Various mechanisms of agglomeration are able to be at work in case there is a transport assessment for public transport projects and improvements.
Glaeser (2009) emphasised that input sharing does not appear to be a possible mechanism since assessments of public transport projects and improvements seldom enhance freight transport, except indirectly, in case there is a reduction in congestion on parallel streets. Public transport assessment and investments might enhance workers’ access to employment clusters. This results in higher productivity from a better occupation matching and a quicker vacancy filling.
- Conclusion
It is in fact uncertain whether and when improvements of public transport could cause fruitful agglomeration economies and it is uncertain that how this could possibly affect transport project assessment. Clearly, the potential outcomes will definitely depend on economics, industrial conditions, land use, local and national policy context as well as some specific public transport projects. One would predict large outcomes in gigantic cities which have significant transport obstacles and virtually have no effects in smaller “public transport improvements” and agglomeration economies which largely affect transport project assessment/evaluation. The challenges are plenty in carrying out research to specify whether and when public transport improvement increases agglomeration economies and how this could possibly affect transport project assessment. The possible mechanisms of agglomeration at work entail a dizzying array of probable methods and measures. Tracing the connection between agglomeration and public transport (as measured in various methods) and their direct impact of assessing transport projects is an essential research area having not been explored yet. There might be outstanding benefits from interviews or from other qualitative research methods to better comprehend whether and how public transport improvements may lead to location decisions by companies that, in turn, might lead to diversified agglomeration that positively enhances and develops transport project assessment at both private and governmental sectors. In doing so, it is helpful to bear in mind that the matching, learning and sharing mechanisms are expected to cause higher productivity, and to go beyond studies showing that there are some correlations of productivity with density. This is to start understanding what such correlations would imply substantively and how these correlations play a mediating role in in improving public transport by agglomeration and how this could possibly play a role in assessing transport project.
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