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Pergamon Journal of Trans~wrr Gen~rq,hy Vol. 4. No 2. pp. 77LY2. IYYh

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Road transport infrastructure and regional economic development

The regional development effects of the M25 London orbital motorway

Brian Linneker

Department of Geography, London School of Economics and Political Science, Houghton Street, London WC2A 2A E, UK

Nigel Spence

Department of Geography, Queen Mary and Westfield College, University of London, Mile End Road, London El 4NS, UK

The M25 London Orbital Motorway has affected levels of accessibility in Britain and this has been discussed in an earlier paper. Changes so caused are thought likely to affect regional development and the objective of this research is to evaluate the nature of this relationship. The methodology involves the construction of a series of measures of both regional development, as the impact or dependent variable, and accessibility, as the policy or control variable. The regressions also include a number of other potential explanatory factors. Accessibility is measured using time, distance and cost impedance functions for heavy goods vehicle movements. The results are somewhat different from those found in previous research involving different spatial and temporal circumstances. During the 1980s places with the highest general levels of accessibility (including that arising from the construction of the M25) score poorest on the changing development indicators. However, when the component of accessibility change which is caused by the actual construction of the M25 motorway is isolated, then it can be demonstrated that such changes are positively related to changing levels of economic development, at least for some impedance functions. Copyright @ 19% Elsevier Science Ltd

Keywords: motorways (ME), London, accessibility, market potential, regional development

The nature of the relationship between road transport infrastructure and regional economic development has been the subject of debate amongst politicians, planners and researchers for many years (Blum, 1982). The road network supports a whole variety of dependent economic activity and serves to integrate the economic system and facilitate its transactions in geographical space (Diamond and Spence, 1989).

The M25 London orbital motorway ring possesses probably the best accessibility found anywhere in the country, and regionally may have major implications for whether new investment locates inside London, in

the outer metropolitan area, or in other areas of the South East or beyond. The M25 is being perceived as a major catalyst for change in the South East by planners and property developers (Proctor, 1988), yet the motorway is already heavily congested, carrying in excess of 144 000 vehicles per day in some parts, compared with the expected capacity of 85 000 vehicles a day (Department of Transport, 1989a). Following developments such as the out-of-town shopping centre at Lakeside Thurrock, commercial development in the pipeline in the Home Counties is substantial and the M25 is likely to experience even heavier traffic demands

77

78 Road transport infrastructure and regional economic development: B Linneker and N Spence

in the future. Serious congestion problems affecting the efficiency and accessibility of the South East economy seem destined to continue despite the substantial and costly schemes for widening the M25 currently being implemented. Figure 1 maps the motorway route in relation to the main pattern of urbanization of South East England and further details can be found in

were used for the South East region, including all inner and outer London Boroughs. These account for 129 areas. The rest of Britain was divided up into 50 county-level areas. For each area a zone centroid was defined based on the main town or city, and the range of accessibility measures detailed below was calculated.

Linneker and Spence (1992a). The present research attempts to view the British

road network as it was in 1989, conceiving of it as both with and without the M2.5 link in place. The former is used as a surrogate for the post-M25 network and the latter the pre-M25 situation. This is the fundamental basis used here for judging the market potential accessibility implications of building this major piece of infrastructure (Linneker and Spence, 1992b).

The whole of Britain is considered here as the geographical study area, with spatially more detailed analysis undertaken in the South East. Britain was divided into 179 geographical areas, again detailed in the previous paper. Local Authority district-level data

Methodology for evaluating the regional economic development effects of the M25

All major road schemes are subject to cost-benefit analysis and economic assessment, involving a financial constraint. However, the analysis is purely of a pre- project transport sector focus and no account is taken of the effects of the project on the economic development of an area. The economic development implications of road schemes will be manifest outside the transport sector and are often regarded as indirect effects. Indirect effects also need to be related to the road project in a causal way and ultimately included in the wider post-project evaluation. To do this the

OXFORDSHIRE ‘-i.~._,

HAMPSHIRE E. SUSSEX

Built-up area Green Belt (1968) 0 miles 75

v Motorways (1990) I 1 , I 0 km 15

Figure 1 The M25 London Orbital Motorway

Road transport infrastructure and regional economic development: B Linneker and N Spence 79

economic development implications need to be determined.

Evaluation problems

The operational definition of an indirect indicator of regional economic development involves two problems. The first concerns the contents of the net-indirect benefits term, and what criteria should be included in it. Second, even assuming that the components of the net-indirect benefits can be defined, for operational purposes they have to be calculated. It is necessary to establish the causal and quantitative relationship between change in road transport supply and the variations in the component criteria indicators of the indirect regional economic development effects which are attributed to this change. Unfortunately there is no general agreement on the relevant framework or appropriate methodology for evaluating the economic development implications of major road investments such as the M25. Nor is there yet a standard methodology that can link these effects to policy objectives and goals for evaluative purposes.

Regioncll economic development criteria

Major road infrastructure schemes are constructed in order to change the accessibility of a regional system. Although the project is location-specific it will change both the absolute local accessibility and also the relative accessibility of an area compared with that of other areas. The regional economic development implications of such accessibility changes will be manifest in a number of interrelated criteria which to some extent will be indicative of the economic development implications of the scheme.

Outside the transport sector itself in terms of traffic generation, a number of economic development indicators at the plant or firm level could be used such as output, investment and productivity. The problem of data availability of these criteria at an appropriate geographical scale often prevents their use. Employ- ment figures are, however, available at reasonably detailed geographical scales and have been used as regional economic development indicators on several previous occasions.

Size of road project

Thomson (1978) distinguishes between incremental evaluation and strategic evaluation. In the former individual projects are treated as small improvements to an existing network. Any long-term consequences on the spatial structure of an economy will be negligible and can be ignored. On the other hand the long-term effects of larger schemes may well be significant and these are thought to require strategic evaluation. The M2.5 is listed in Department of Transport documentation as a number of individual component links, but taken together it comprises 192 km of motorway which

encircles the Greater London conurbation at a distance of between 19 and 32 km from central London. It has 33 junctions which include all the main radial routes into the capital. The long-term consequences of its construction are unlikely to be negligible.

Time scale

Road transport projects are in their nature long-lived and the choice of evaluation time scale is of importance since indirect effects come to light only over a long period of time in an environment which is itself continually changing. Network improvements gradually improve the accessibility of an area and a marked change in indicator trends may not be expected to occur after the completion of a single specific stretch of road. Major estuarial bridge crossings are probably exceptions to this rule since they often represent large discrete changes in accessibility. Many impact studies in the UK have therefore tended to concentrate on estuarial crossings for this reason (Cleary and Thomas, 1973; Simon, 1987; Drewett, Hoehn and Linneker, 1991).

In any major road infrastructure evaluation a dis- tinction can be made between access changes between two points in time brought about by the construction of all new roads and access changes bought about by the building of that specific major investment alone. For the M25 motorway the period under consideration is 1975 to 1986. The former approach is more complicated since it involves taking out all new road improvements and links constructed over the period. This study considers the access changes brought about only by the construction of the M25 and is not, strictly speaking, concerned with the access changes brought about during the time period over which the M2S was constructed. The M25 has been built as a series of sections all opened over approximately a l&year period. In 1975 the first section opened was between South Mimms and Potters Bar; however, by 1980 only about one-quarter of the total motorway had been built. While this may have changed the accessibility marginally between certain areas, its full impact is only likely to be felt after all the sections had been completed and the full orbital route achieved.

Scale of impacts

Analysis of developmental effects needs to identify the relative as well as the absolute magnitudes and to isolate local redistributional effects from regional and national changes. Other studies in this field have proceeded by developing an accessibility index of an area’s relative position in the road transport network and relating it to an indicator of regional economic development based on employment in an area. This research follows a similar methodology.

The hypothesis being tested is whether an area’s employment growth is in any way related to the changing relative access and transport cost advantages


associated with the construction of the new road infrastruture. The studies of Dodgson (1974) and Botham (1980) both develop a regression model in which an index of the rate of growth of employment is related to the area’s relative position in the transport network, and to other variables which may influence variation in relative employment growth rates over space.

The methodologies involve establishing from multiple regression analysis a relationship between accessibility pre-road investment and employment change. Then, accessibility post-road investment is recalculated and the regression relationship, previously established, is used to predict the possible employment implications of accessibility changes bought about by the road investment (all other things being equal).

With some modifications this approach is applied at the national level by Botham (1980) to test the employment effects of the postwar road programme between 1957 and 1972 on an inter-sectoral and inter- regional basis. The study examines the redistribution of a given amount of employment growth using market potential accessibility measures. A positive relationship is established between market potential accessibility and the differential employment shift, indicating a

Dodgson (1974) examines the employment implications of the M62 Trans-Pennine Motorway on 30 local

redistribution of employment to areas of higher access-

areas through which the M62 passes for the period 1961 to 1966. A negative relationship is established between

ibility to the maximum extent of 161 800 jobs.

an area’s access cost accessibility and its demand for labour, indicating faster employment growth in areas with lower transport costs (higher accessibility). The results are presented as tentative conclusions, with the maximum additional increase in employment due solely to the M62 of about 2900 per year (14 500 over a five- year period).

The analysis here proceeds initially along similar lines to these studies. The employment criteria used to indicate the regional economic development implications are for the period 1981 to 1987. It was thought that the employment effects of the M25 were not likely to be apparent until the project was well under way in the 1980s.

Measures of regional economic development

Differential employment shift

Changes in the distribution of employment for Botham (1980) are hypothesized to depend first on national economic forces which affect regional employment differentially owing to the regional variations in industrial structure. Second, specifically spatial factors may be expected to influence the distribution of employment through variations in labour availability and transport cost. Variations in industrial structure were removed from the dependent variable by defining

the change in a zone’s employment as the difference between an area’s actual employment change and its expected change. The expected change was calculated on the assumption that each industry in an area grows or declines at its national rate. (The Appendix gives details of the calculation of this and some other non- accessibility variables. Table 1 provides a summary of all of the variables, non-accessibility and acessibility based, used in the subsequent empirical analysis.)

The differential employment shift varied between 36 317 (Northamptonshire) to -74 849 (Merseyside). Some areas, such as the City of London, had extremely large negative differential employment shifts. The City of London actually lost 10 133 jobs between 1981 and 1987, but because of its favourable industrial structure the expected growth in employment suggests some 53 681 new jobs. The actual minus the expected therefore shows a negative differential employment shift of -63 814 jobs. Milton Keynes, on the other hand, had a large positive differentiai employment shift, since it actually gained 16 598 jobs over the period but its industrial structure suggests an expected loss in employment of 406 jobs. The actual minus expected therefore shows a positive differential employment shift of 17 104 jobs.

Demand for labour index

For Dodgson (1974) employment growth occurs as a result of the interaction between the demand for labour by firms and the supply of labour available. It is assumed that employment availability is the most important factor influencing employment growth. In addition it is assumed that an area will grow at least as fast as the natural increase in working population in that area and this is defined as the expected employment growth rate. Actual divergences from this rate are then considered and define the demand for labour index.

A positive demand for labour is thought to cause an increase in supply of labour in that area via some combination of in-migration, inward commuting, increased activity rates and a positive take up of unemployed labour from the existing working population. A negative demand for labour means that some of the expected natural increase in working population is being lost to other areas via contributions from out- migration, out-commuting, reduced activity rates and a rise in unemployment in an area’s working population.

Most areas had a positive demand for labour over the period indicating that areas were experiencing inward migration of residents, or more people were commuting inwards, locally unemployed labour was being employed, or more likely there were various combinations of these changes. Districts of East and West Sussex, Kent, Berkshire and Buckinghamshire had the highest increase in demand for labour, with Arun in West Sussex the highest at 43.9%. Rother (40.3%), Gillingham (38.5%), Tandridge (35.3%) and

Road transport infrastructure and regional economic development: B Linneker and N Spence

Table 1 Summary specification of the non-accessibility and accessibility variables used in the correlation/regression analysis

81

DS is the differential shift in employment between 1981 and 1987 and is used to measure the performance of the local economy. This is the familiar competitive effect in shift-share analysis which measures the employment change in an area having first controlled for industrial structure or the mix effect. Census of Employment data are used in the calculation of DS. DL is the demand for labour index between 1981 and 1987. This is a measure of the difference between the actual employment change and an expected change which arises from the natural increase in the local population supplying a workforce to the area at current activity rates. If the difference is a positive demand for labour then labour supply arises from in-migration and/or in-commuting and/or increase in activity rates and/ or reduction in unemployment. If the difference is a negative demand for labour then the opposite will be the case. Census of Population data are used to determine the expected change and Census of Employment data the actual. ST is an mdustrial structure index applicable to 1981. The industrial profile of an area in relative terms is weighted by the national rate of growth between 1981 and 1987 of each industry and summed. Census of Employment data are used in the calculation of ST. CN is a congestion index applicable to 1981 and is population density based. Census of Population data are used in the calculation of CN. ED is an employment density index applicable to 1981 and is a proxy for external diseconomies of scale. Census of Employment data are used in the calculation of ED. LA is a labour availability index applicable to 1981. Tt is calculated as the difference between the economically active population and the employment in an area divided by the employment. Census of Population data are used to determine the resident working population and Census of Employment data the employment. MP is the prefix for all accessibility variables and the remaining parts of their names are to be decoded as follows. The third letter refers to the impedance measure used: MPT is time-based, MPD is distance-based and MPC is cost-based. The remaining part of the code depends on whether the access measure refers to one point in time or change in accessibility over time. For the static accessibility calculations, the next part of the code refers to whether or not the measure includes the motorway (+M25) or excludes it (-M25), this being the main mechanism for determining the road effect. For the non-cost-based impedance measures the last part of the code is the specification of the date of the employment mass factor, either E81 or E87. This allows the employment effect to be incorporated with the road effect or excluded from it. For cost-based impedance there is an additional component which refers to the date of the cost coefficients used, either CSI or C87. Again this facilitates the inclusion of the cost effect with the road and employment effect or exclusion from it. The dynamic accessibility calculations can be recognized by the inclusion of the A symbol. This is followed in the non-cost-based impedance measures by an indication as to whether the employment mass factor has been allowed to vary over time (E8187) or not (ESI), thus allowing the inclusion of the employment effect or simply the road effect alone to be discerned. For cost-based impedance there is an additional component which indicates whether the cost coefficients have been allowed to vary over time (C8187) or not (C81), again permitting the cost effect to be either included or excluded from the accessibility calculation.

Worthing (31.8%) were respectively the next highest. Of the lowest, 58 areas showed negative changes in demand for labour and these were not just areas outside the South East (11 out of the 20 lowest areas were in the South East). The lowest three areas were Harlow (-18.7%), Rochester upon Medway (-18.6%) and Barking & Dagenham (-16.2%), all in the South East, and two are in Kent and Essex, both counties through which the M25 passes.

Accessibility and non-accessibility measures

This research uses a variety of independent variables to account for the measures of regional economic development outlined above. These range from indices of industrial structure, congestion, employment density and labour availability to the critical (in terms of the objectives of this research) accessibility indices.

The concept of accessibility is usually operationalized by taking a zone’s relative position within an overall transport network and considering the scope for interaction with all of the other zones in the system covered by that network. In essence the idea is to measure the potential opportunities which exist for individuals and businesses located within zones to serve or be served by their counterparts in other zones. Accessibility measures can use the impedance effects of distance. time and generalized transport costs to produce a single index of accessibility for each location, thus providing the basis of an overall accessibility surface.

A variety of accessibility concepts have been formu- lated and these can be interpreted from a number of theoretical and real-world standpoints (Houston, 1969). Specifically, the literature contains definitions of a number of accessibility variables which attempt to represent the opportunities for economic development given by the transport network and the spatial structure of the economy. The basic Hansen-type accessibility indicator is a measure of opportunity or market potential where more distant markets provide diminish- ing influences (Hansen, 1959). Accessibility measures like these usually have a gravity-type formulation where the opportunity or potential between two areas is positively related to some measure of the size of attractive mass between two areas and negatively related to some measure of impedance between the areas (Harris, 1954; Clark, 1966; Owen and Coombes, 1983). A market potential accessibility measure is used in this research.

Market potential

If the accessibility concept is operationalized in terms of accessibility to jobs from a given location it can be thought of as the total sum of the jobs within reach of each location. but somehow giving less weight to those jobs further away. Jobs in such a conceptualization are of course being taken as a surrogate for economic activity. This market potential form of accessibility measure assumes that distant markets provide reduced opportunities. Summing between an area (i) and all other areas (j) gives an overall market potential value


for the area (i). It is a measure of the relative accessibility of that area to the markets and input sources which the area’s industries could either actually or potentially supply or be supplied from.

MPi = zj PjlCij a

where MPi = Market Potential of zone i

Pj = a measure of mass or market potential in zone j

Cij = a measure of impedance or transport costs from i to j

a = an alpha exponent often assumed to be equal to one.

This measure assumes there is no spatial monopoly over the study area. Furthermore, spatial competition exists in that there is competitive advantage to be gained by location in areas of high market potential since by doing so a firm can maximize its volume of sales. Another assumption of the model is that the level of sales falls with increasing distance or transport costs to markets and free on-board pricing is the norm in that transport costs are assigned to the buyer (Botham, 1980).

The theoretical basis of the market potential concept is thought to be weak in an economy dominated by large corporations. Even so, some authors have used the concept to derive a model which best describes the spatial distribution of all manufacturing (Kerr and Spelt, 1960; Ray, 1965). However, a great deal of calibration and data manipulation of the mass indices and alpha exponents are required to exploit its statistical explanatory power if indeed this is to be the inter- pretation put on the market potential accessibility concept. But as a general behavioural measure of access to opportunities from a given location the concept itself is of some value. Certainly other research has found the concept useful and considerable use has been made of it in studies attempting to account for variations in levels of development. In short, an overall positive relationship is thought to exist between market potential and regional economic development, although the strength of this positive relationship over time and space is questionable (Keeble and Hauser, 1971, 1972; Brown, 1973). The possible expansion in employment in areas of increased accessibility will again change the accessibility of the areas. Assessing first- and second-round employment effects from infrastructure improvements is not an easy task although some models are being developed (Rietveld, 1989).

Only a much shortened treatment of the issues involved in understanding accessibility is presented here. Two previous papers explore these in this particular empirical context in more depth (Linneker and Spence, 1992a, 1992b). Another paper, published in this journal, presents accessibility results of the same methodology in the wider context of the national motorway network (Spence and Linneker, 1994).

Industrial structure index (ST)

This variable is hypothesized by Dodgson (1974) to influence the demand for labour in an area. It is developed to indicate whether an area has a relatively high or low proportion of its employment in nationally growing or declining industries. Even though total employment declined nationally by 85 161 jobs between 1981 and 1987, the most rapid growth was in banking and finance, with a 33.4% growth rate. The worst hit divisional class was energy and water supply industries with a national employment decline of 28.2%. The distribution of the industrial structure index varied considerably over the zones, the highest being the City of London (116.73) and the lowest Barking & Dagenham (92.05).

Congestion index (CN)

To represent the influence of external diseconomies of scale a congestion index was derived based on population density using 1981 Census data. However, it has been argued sensibly by Dodgson (1974) that overall density figures do not reflect the relative extent of congestion in the urban core. As a result, for zones outside the South East which were based at county level, the density was calculated for the Local Authority district containing the zone centroid.

Employment density (ED)

Employment density is hypothesized by Botham (1980) to influence the differential employment shift in a negative way. It is similar to the congestion variable in that it represents the influence of external diseconomies of scale and was found to be a significant variable by Keeble and Hauser (1972) in an employment change analysis over the 1959-71 period, accounting for some 45% of the variation in the manufacturing differential employment shift. It represents the influence of high land and factory rents, high housing and journey to work costs along with the general congestion of the major conurbations. The distribution of the employment density variable for 1981 varied between the City of London (1171 jobs per hectare) to the Highland zone of Scotland (0.03 jobs per hectare).

Labour availability (LA)

Many firms in micro-level studies suggest that availability of suitable trainable labour is an important constraint on expansion and location of economic activity. Botham (1980) finds a significant role for labour availability in his study, and a similar variable is used here. Quite simply, labour availability is the difference between resident working population and total employment in an area expressed as a ratio of the latter. The distribution of this variable for 1981 varies between Castle Point in Essex (1.92) to the City of London (-0.99). Some 26 areas show negative values indicating labour shortages, and these are mainly in the

Road transport infrastructure and regional economic development: B Linneker and N Spence x3

South East. The variable includes in-commuters to an area.

Calculating accessibility measures and their interrelationships

Impedunce functions

In accessibility measures impedance restricting the interaction between areas has to be estimated. This may bc considered in terms of distance, time or generalized transport cost between zone centroid origins and destinations. Dodgson (1974) estimated freight transport costs pre-road improvement, from a functional relationship found in Bayliss and Edwards (1970). This was based on road distance and represents the actual costs of freight transport over varying distances for a ‘neutral’ commodity weighing one hundredweight. Post-road improvement costs were estimated from TRRL commercial vehicle operating costs formulae and these were then used to calculate post-road access cost accessibility. The difference between pre- and post-road accessibility surfaces indicates how the marginal change in transport costs with the road improvement affected accessibility over the zones of the study.

Botham (1981) uses a generalized transport cost function based on time and distance to estimate the marginal transport cost changes of road improvements. The pre-road improvement transport cost matrix was obtained by removing all motorway links and replacing them with the links that previously existed and their assumed free-flow speeds on various classes of road. Shortest cost routes over the pre- and post-road networks were calculated from the TRAMP suite of transport models developed by John Wootton and Partners. In a similar way the present study estimated not shortest but quickest routes between origins and destinations, and their generalized transport costs, pre- and post-M25.

The SIA road network model

In a previous paper full details of the SIA road network model were given (Linneker and Spence, 1992a), as were the official Department of Transport’s bases (Nichols. 1975; Department of Transport, 1987, 1989b) for calculating vehicle operating costs and as a result what follows is a shortened version essential for the coherence of this paper. SIA operates the Department of Transport’s computer record of the links in, and improvements of, the British road network. The SIA Routefinder model is a computer program capable of selecting cheapest, quickest and shortest routes through this network for cars and goods vehicles for daytime off-peak journeys between over 30 000 named origins and destinations in the UK (SIA, 1989).

Using the SIA model of the British road network as of 1989 it was possible to calculate the quickest

journeys via the road network between each origin and destination selected. The result is a time and distance measure for each trip. By combining the time and distance data between the areas selected for this research, together with the costs of overcoming distance and of expending time, it was possible to estimate generalized transport costs for every trip in the matrix. Using zonal employment data, market potential accessibility variables based on time, distance or generalized transport cost can then be calculated.

It has just been stated that the SIA Routefinder model has a facility for calculating the quickest routes between all origins and destinations in the matrix via the road network as it existed in 1989. By specifying the avoidance of the M2.5 in the model, quickest journey time and distance values can also be calculated between all origins and destinations on the road network which excludes the M2.5. In this research this represents an estimate of what the pre-M25 distance and travel times would have been and thus it was possible to calculate an estimate of the change in accessibility represented by the construction of the M25. Time, distance and generalized cost data with and without the M25, for HGV commercial vehicles only, are reported on in this research. The same analysis has been undertaken for cars and the results as expected, while different at the margins, are not dissimilar.

Clearly there are a number of, assumptions involved with this procedure, not least of which are terminal- date fixed traffic volumes, vehicle speeds and the issue of reassignment. Any new road will obviously tend to increase vehicle speeds over the network assuming no increase in volume. But of course volumes also tend to increase and new road provision will involve a complex process of assignment as well as reassignment. This has implications for the specification of the ‘without road hypothetical situation’. Strictly speaking the without M25 average free-flow speeds would have to be calculated with the trips reassigned to the road network as it existed pre-M25 and with those additional trips generated by the M25 taken away. This is not a trivial task and was not attempted in this research. Travel times used here are based on daytime off-peak vehicle speeds on various types of road including, and not including, the M25. This research takes no account of increases or decreases in congestion or any other aspect of actual traffic flow. A fuller treatment of the logic involved in the specification of the ‘without motorway counterfactual’, together with more details of the generalized cost formulae used, can be found in Linneker and Spence (1992a).

Accessibility variables calculated

Some of the static absolute accessibility variables pre- and post-M25 are based on 1981 and 1987 employment levels in order that the dynamic road effect and employment effects on accessibility can be calculated.


The static and dynamic accessibility variables used in this analysis are listed in Tables 2 and 3 and will be referred to subsequently. (A key to how their names can be interpreted can be found in Table I .) Only brief mention can be made here concerning their spatial distribution; for more detail, again refer to Linneker and Spence (1992a). For the pure M25 road effects on accessibility (fixed 1981 employment mass), whatever form of impedance function is used, be it time, distance or generalized transport cost, Inner London has the highest static absolute market potential accessibility and Scotland the lowest.

For market potential accessibilities based only on time, the relative average percentage changes pre- and post-M25 are positive, indicating a rise in accessibility in most areas owing to the travel time savings of the M25. The only exceptions to this are changes in average accessibility in Inner London which show accessibility falls, reflecting the effects the M2.5 has had on travel times here and the dominating effect of falls in average vehicle speeds inside London, despite the construction of the M25. Market potential time accessibility increases are greatest in the south-eastern inner parts of the wider metropolitan South East region, an area through which the M25 predominantly passes.

For accessibilities based only on road distance pre- and post-M25, all areas had a fall in accessibility, reflecting the greater road distance on what prove to be the quickest routes which use the M25. The largest average percentage decreases in distance accessibility are to be found in the south-western inner zone just beyond the London boundary.

Table 2 HGV static market potential accessibility correlations

Accessibility variable Demand for labour

Differential shift

Time MPT + M25E81 MPT - M25E81 MPT + M25E87

-0.183” -0.228” -0.203” -0.252” -0.162h -0.208”

Distance MPD + M25E81 MPD - M25E81 MPD + M25E87

-0.154h -0.295” -0.152h -0.289” -0.144b -0.286”

Cost MPC + M25E81C87 MPC - M25E81C87

-0.164h -0.278” -0.164h -0.276”

MPC - M25E81C81 MPC + M25E87C87

-0.164h -0.276” -0.151h -0.266”

Notes: “Denotes significance at the 99% level. ‘Denotes significance at the 95”/0 level only.

The code names used in this table should be interpreted as in the following examples: MPT + M25E81 = market potential measure (MP) based on time impedance (T) plus (+) the M25 using a 1981 distribution of employment (E81). MPC - M25E81C87 = market potential measure (MP) based on generalized costs impedance (C) minus (-) the M25 using a 1981 distribution of employment (EXl) and 1987 cost coefficients (C87).

Table 3 HGV dynamic market potential accessibility correlations

Accessibility variable Demand for labour

Differential shift

Time MPTAE81 MPTAE8187

1.281” 0.179” 0.412” 0.296“

Distance MPDAE81 -0.129h -0.12gh MPDAE8187 0.334” 0.246”

Cost MPCAE81C87 -0.009 -0.046 MPCAE81C8187 -0.009 -0.046 MPCAE8187C87 0.565” 0.430’ MPCAE8187C8187 0.565” 0.430”

Notes: “Denotes significance at the 99% level. hDenotes significance at the 95% level only. The code names used in this table should be interpreted as in the following examples: MPTAE81 = market potential measure (MP) based on time impedance (T) measured over time (A) using a 1981 distribution of employment (E81). MPCAE8187C8187 = market potential measure (MP) based on generalized costs impedance (C) measured over time (A) using a changing 1981-87 distribution of employment (E8187) and changing 1981-87 cost coefficients (C8187).

When time and distance effects of the M25 are brought together in the form of generalized transport cost measures of accessibility pre- and post-M25, the results are perhaps unexpected. The cost accessibility changes are all negative, reflecting a rise in generalized transport costs and a fall in accessibility with the construction of the M25. The largest average percentage decrease in cost accessibility is in the south-western part of the inner metropolitan area. The cost increases incurred in greater road distances via the M25 are dominating the travel time savings, leading to a rise in generalized transport costs post-M25 and falls in accessibilities for HGVs.

Clearly these results depend on the relative weights attached to the time and vehicle operating cost coefficients used in the calculation of generalized transport cost. The accessibility changes are all based on fixed values of these cost coefficients pre- and post- M25. When 1981 values of time and vehicle operating cost coefficients are used in the pre-M25 case the average absolute and percentage changes in market potential cost accessibility are all negative, indicating accessibility falls. What accessibility benefits the M25 has given have been eroded by the rise in the value of time and vehicle operating costs coefficients through time.

The dynamic accessibility changes of areas are also influenced by the changing employment masses used in the calculation of the accessibility measures. Access- ibility variables in Table 3 with an ‘E8187’ in their name are based on 1981 employment levels pre-M25 and 1987 employment levels post-M25, and therefore include not


only an M25 road effect but also an employment effect in the change values. Similarly for changing cost coefficients over time, variables with C8187 in their name therefore include an additional cost effect in the change values. Again the way in which the variable names have been developed can be seen in Table I.

Before proceeding, some attention needs to be given to the seemingly illogical derivation of declines in accessibility resulting from the construction of the motorway. Surely it follows that if this was the case then the road would be little used - patently not the case. To recap it must be made clear exactly what has been discovered about changing accessibility from the methodology used here. First of all, it must be remembered that the routeing model assumes minim- ization of time not costs. When time is the impedance factor, the building of the motorway in general produces increases in accessibility. For most places it is quicker to use the motorway. The opposite is the case when distance is the impedance factor - for most places using the road for quicker trips means increasing journey lengths. Calculations become more complex when generalized costs are involved, as these are based on a combination of both time and distance measure- ments for each trip. The outcome of the cost calculations indicates accessibility falls for HGVs (interestingly not the case for cars in research previously reported on). Does this mean that rational HGV users accept greater road distance costs to save time which they value less? The answer of course is no; other factors must be involved. It could well be that the coefficients used by the Department of Transport do not fully and accurately reflect the true costs, especially of time, as perceived by users of the M25. However, perhaps of more importance is the fact that this research is not concerned with actual trips of any kind. For example, there are not measures of traffic volumes, over- crowding, bottlenecks, safety, ease of driving, motorway facilities, reliability of journey times, or anything else. User preference, then, is clearly more complex than the methodology here can cope with. This research considers only two of the aspects which influence the choice of route. First, the quickest trip times between the locations of the study at daytime off- peak speeds on the variety of types of road in the network, and second, the estimates made by the Department of Transport of the costs of undertaking these journeys. Of course, the latter could be altered arbitrarily to increase the value of time and reduce the costs of overcoming distance, so generating accessibility increases, but there would seem to be little point in this. The most helpful way of interpreting these findings is to view them as exactly what they are. They are an indication of accessibility changes induced by some of the important factors thought likely to influence such accessibility changes - but clearly not all of them. A fuller discussion of these issues can be found in Linneker and Spence (1992b).

Intercorrelations between non-accessibility measures

The demand for labour index is positively, but not particularly highly, correlated with the index of industrial structure (0.365), indicating that areas with a favourable industrial structure have a stronger demand for labour, as would be expected. Demand for labour is negatively correlated with congestion (-0.214), again as is expected, indicating lower employment growth in higher density areas. The differential employment shift is negatively correlated with employment density (-0.393), again indicating lower employment growth in higher density areas, and positively correlated with the labour availability variable (0.203).

HGV static accessibility correlations

Accessibility measures alone are certainly not the prime determinants of the development measures used here. This is shown by the low order of magnitude of the correlations in Table 2. Generally, the static market potential accessibility variables, be they based on time, distance or cost, are all negatively correlated with both the demand for labour index and the differential shift variable. Areas with higher accessibility, then, are those with lower demand for labour and a lower differential shift. In other words, employment growth has generally been in areas with low accessibility. This general conclusion runs counter to the research of both Dodgson (1974) and Botham (1980), which confirmed a positive relationship between accessibility and employment growth.

Of course, it makes little sense to infer that the nature of the relationship between accessibility and growth itself has changed. Clearly, other things being equal, the benefits in terms of cost savings to industry of higher rather than lower levels of accessibility remain convincing. No, what seems to be the case is that accessible locations seem to have become associated with a number of disbenefits which together must have assumed an importance greater than the benefits generated by high accessibility. Manifestations of such disbenefits of central locations include high rents and lack of available land, high labour costs and lack of suitably qualified labour, and, in wider accessibility terms, high congestion costs generally. Central locations of high accessibility also correspond with the largest metropolitan areas in the whole urban system which in the last two decades have seen their municipal and industrial fabric decay and fall into disrepair. The most accessible places, then, are also fast becoming some of the most costly places in which to do business.

Market potential accessibility appears to be more strongly related to the differential shift dependent variable than the demand for labour index. Although the correlation coefficients are weak for those based on time impedance, the pre-M25 accessibility variables are more strongly correlated than those post-M25. For accessibility variables based on distance impedance, accessibility post-M25 is more strongly correlated than


pre-M25 accessibility variables. For accessibility interpreted as generalized transport costs, the post-M25 accessibility variables, based on 1981 employment and 1987 value of time and vehicle operating cost coefficients, seem to produce the strongest correlations but by small margins. The value of time and vehicle- operating cost coefficients, be they 1981 or 1987, seem to make no difference to the correlation coefficients. However, the inclusion of the 1987 employment data in the calculation of accessibility post-M25, i.e. the ‘true’ post-M25 accessibility surface, is the most poorly correlated accessibility variable of this type.

HGV dynamic accessibility correlations

The dynamic accessibility variables are produced by expressing the post-M25 accessibility as a percentage of the pre-M25 accessibility. All the variables ending in ‘E81’ or ‘E81C87’ show the pure M25 road effect on the accessibility of areas since the employment levels in each area are held constant in the calculations, being fixed at the 1981 level and using a fixed value of vehicle operating cost as of 1987. All the percentage changes in accessibility variables ending in ‘E8187’ or ‘E8187C87’ or ‘E8187C8187’ include the M25 road effect and the employment effect, since these variables are based on 1981 employment pre-M25 and 1987 employment post- M25.

For those based on time impedance, the percentage changes in market potential time accessibility variables are all positively correlated with both the demand for labour and the differential shift (Table 3). This indicates that those areas showing the greater percentage increase in accessibility also show the greater employment increases. The dynamic time variables are all more strongly correlated with the demand for labour index than the differential employment shift. The dynamic distance variables are more difficult to interpret, but generally show the same relationships. For the dynamic accessibility changes based on generalized transport cost, the use of different value of time and vehicle-operating cost coefficients in the calculation of these variables makes no difference to the correlation coefficients. The pure road effect as measured by relative cost accessibility changes is almost neutral on the employment-based output measures. The inclusion of the employment effect changes the strength and the sign of the correlation to one which is positively correlated with both demand for labour and differential shift, the stronger correlation being that with demand for labour.

Regression relationships between regional economic development and accessibility and non- accessibility measures

HGV static accessibility regression results

The variety of accessibility and non-accessibility

independent variables outlined above were regressed against the dependent variables demand for labour (DL) and differential employment shift (DS) and the results are given in Tables 4-7.

The demand for labour index was regressed against the index of industrial structure (ST), the congestion index (CN) and the accessibility variables in a model similar to that of Dodgson (1974). The industrial structure index is positively related to the demand for labour index indicating that areas with a favourable industrial structure have faster employment growth. The congestion index is negatively related to the demand for labour index indicating a low rate of employment growth in high-density areas. Both these non-accessibility independent variables are statistically significant in all equations.

The differential employment shift was regressed against the three independent variables which Botham (1980) found most significant: employment density (ED), labour availability (LA) and the accessibility variables. (The industrial structure index is not used in this model since the differential employment shift removes from the dependent variable any employment changes due to the industrial structure of an area.) Employment density is negatively related to the differential employment shift, and is statistically significant in all equations. However, labour availability, although positively related to the differential shift, was in no equation statistically significant and should be dis- counted.

Pre-M25 accessibility based on 1981 employment

Table 4 shows that for the static accessibility surface variables pre-M25 based on 1981 employment no market potential time, distance or cost accessibility variables are significant when regressed against the differential employment shift, but some are when regressed against the demand for labour index. The only time-based variable in this category significantly related to the demand for labour is MPT - M25E81 with an R* of 30.1% . The only distance-based variable significantly related to the demand for labour index is MPD - M25E81 with an R2 of 30.5%. This result, together with the other more noteworthy findings of these sections, will be evaluated in the concluding remarks. When the cost-based variables are regressed against the demand for labour index, the use of differing 1981 and 1987 value of time and vehicle operating cost coefficients makes no difference to the explained variances but does marginally affect the access variable regression coefficients. The only cost- based variable significantly related to the demand for labour index is MPC - M25E81C81 and MPC - M25E81C87 with an R2 of 35%.

Post-M25 accessibility based on 1981 employment

Table 5 shows that for the static accessibility surface variables post-M25 based on 1981 employment, again


c’ C C = I I I I

+ + + +

II II II II

+ + +

II II II

L2 6 $


+ + + m \o 0 d 2 m I I -i II II II

+ + + G % 7, (r. -

7 -i -i II II II

Ei ei Ei


no market potential time, distance or cost accessibility

variable is significantly related to the differential

employment shift, but some are significantly related to

the demand for labour index. The only time-based variable in this category is MPT + M25E81 with an R2

of 29.2%. The only distance-based variable significantly related to the demand for labour index is MPD + M25E81 (R’ = 30.6%). Lastly, only one cost-based

variable, MPC + M25E81C87, seems to be significantly related to demand for labour producing an explained

variance of 30.5%. If these calibrations are repeated, but instead using the 1987 distribution of employment

in the accessibility calculations, much the same results are produced.

HGV dynamic accessibility regression results

The dynamic accessibility change variables with the M25 were also regressed against the demand for labour

index and differential employment shift variables.

Pure M.3 road effect

Table 6 shows that no dynamic market potential

variable based on distance or cost is significantly related to the dependent variables. The dynamic

market potential time variable MPTAE81 is significantly related to the demand for labour index with an R*

of 26.790.

M25 road and employment effect

Table 7 shows that in this category of dynamic

accessibility change variable all, whatever the impedance function, are significantly related to both the

economic development measures. All dynamic change accessibility variables perform better in terms of higher explained variances when regressed against the demand

for labour index. The market potential time-based variable MPTAE8187, the distance based variable

MPDAE8187, and the cost impedance variable MPCAE8187C87 all are significant in equations

accounting for R’ values of 31.6%, 34.0% and 45.7%

respectiv.ely. However, all these accessibility change variables have employment change built into them as

well as the M25 road effect change, so there is bound to be a degree of double counting involved.

Conclusions

From the correlation and regression results it can be concluded that between 1981 and 1987 there is a

negative relationship between accessibility measured at specific points in time and employment change. Areas which have high accessibility relative to other areas are losing employment and vice versa. This is confirmed by the static accessibility correlations and regressions where market potential accessibility measures are negatively related to both the demand for labour index and the differential employment shift. Static market

potential accessibility variables based on time, distance

and cost pre- and post-M25, using 1981 and 1987

employment, perform ‘better’ in the regression models

when regressed against the demand for labour index.

Although these static regression models only explain in the region of 30% or less of the variation in the dependent variable these static accessibility variables are all statistically significant at the 99% level and again

are of course negatively related to the demand for labour.

This underlying negative relationship between accessibility and employment growth runs counter to that found by both Dodgson (1974) and Botham

(1980). In Dodgson’s study of accessibility and employ-

ment change between 1961 and 1966 a negative relationship is established between access cost access-

ibility and the demand for labour. This means the

smaller the access cost values the larger the employment growth found in the area. In Botham’s study a positive

relationship is found between market potential accessibility and the differential employment shift. Both of

these studies support the conclusion that the higher the accessibility of an area the higher the employment growth. This conclusion is not supported by the evidence of this research. Generally, high-accessibility

areas have been losing employment between 1981 and 1987 despite the construction of the M25. The ‘best’

performing regression model found by Dodgson (1974)

had an R2 of only 26%. This was based on absolute

accessibility using 1961 employment only, and measured by the access cost form of accessibility, not by market

potential. The dynamic accessibility change with the construc-

tion of the M25 (pure road effect on accessibility) is positively related to demand for labour but is only

statistically significant for accessibility variables based on time. This indicates that, despite the general

underlying loss of employment in higher accessibility

areas, those areas to have shown the highest self-

relative percentage increases in accessibility with the construction of the M25 are showing higher employment

growth or much reduced employment loss. This

positive dynamic effect is additional and opposite to the general underlying negative relationship between

accessibility and employment change. When the employment change implications are built

into the dynamic accessibility change, additional to the

pure M25 road effect, all the market potential change accessibility measures based on time, distance and cost

become statistically significant. Explained variances in this category of variable are all increased, the highest explained variation being again in models in which dynamic accessibilities are regressed against the demand for labour index. The highest R2 is 45.7% given by the dynamic market potential cost variable (MPCAE8187C87) in the demand for labour model. However, this result contains a certain amount of employment change double counting and should be


cautiously interpreted. When the employment change otherwise. It has been suggested that impacts will effect on dynamic accessibility change is taken out, only depend on the economic relationships between areas the variable based on the time savings of the M25 and exactly how the infrastructure is used in the (MPTAE81) is significant when related to demand for assembly of raw materials and the distribution of labour. products (Vickerman, 1991).

Although they run counter to previous research in this area these results should not be surprising to informed observers.

A considerable effort in recent times has been expended in describing where the prime dynamics of employment growth and decline are to be found in the British urban and regional system. One feature is clear: proximity to centres of high population densities, notably in the centres of the prime urban agglomera- tions, almost guarantees the most dismal of employment performances (Frost and Spence, 1991). Certainly the patterns of counterurbanization in employment growth in the 1980s are by no means as marked as those in the 1970s but it is still clear that, despite their overall high accessibility levels, high-order central places are not the most efficient of locations for new business expansion.

Finally, it must be remembered that this research deals only with undifferentiated notions of accessibility and economic development. In reality it might be conceded that accessibility in relation to the myriad of economic activities takes various forms. Accessibility is not the same for retailing or for manufacturing industry or for financial services or for high-technology activities or indeed distribution (Rydin in Damesick et al, 1986). Each of these economic activities is playing a role of varying importance in producing levels of economic development. It may well be that the MW-induced accessibility effects are partially camouflaged by such an undifferentiated approach adopted in this initial research. The real sensitivities in the infrastructure- development relationship might be some way off being discovered (Newman and Vickerman, 1993).

Again, compared with the 197Os, a significant feature of the 1980s’ patterns of employment change is the enhanced regional differentiation in performance which is becoming apparent. The South East, East Anglia and the South West were the growth regions of the country and provided the foundations of the widening ‘north-south’ divide. In an attempt to control for this feature the whole analysis reported on in this paper was recalibrated for the South East region on its own and most certainly the regression results do seem to ‘perform better’ in terms of their strength and statistical robustness. These results will be reported on elsewhere as will the calibrations for accessibility based on the car mode.

Acknowledgement

The authors wish to acknowledge the support of SIA Computer Services Limited in this academic research. The company undertook several computer runs of the model at nominal cost according to the specifications of the authors.

References

So what does it all mean? A safe bet is to conclude that accessibility improvements are not mono-causal and unilateral in their impact. It is not always the case that the higher the accessibility of an area the higher the economic development potential. Accessibility is more likely to be dual-causal and bilateral in its impact. The improved accessibility of an area due to the construction of a major new road has two types of potential effects. It may facilitate local firms to expand their market areas by penetrating more distant markets, potentially increasing employment in the area with improved accessibility. Or it may facilitate expansion in the reverse direction as stronger firms external to the area penetrate the area whose accessibility has been relatively improved, and thus any expansionary developmental effects such as employment growth may occur in areas other than those in which accessibility has largely been improved. Critical to which of these two types of potential effects actually is dominant is the resultant competitive position over space. The issue here is that even though relative accessibility has been improved for an area, economic development potential is not necessarily realized in that area, intended or

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Appendix

Differential employment shift (DS)

Division-level employment data for 1981 and 1987 were used to define the differential employment shift over time.

DSi = _Z’k[(E k, i, t + 1 -Ek,i,t)-((Ek,i,tXGk) - Ek, i, t)]

where

DSi = differential employment shift in area i E = total employment

Gk = national percentage growth rate in division k employment 1981-87

k = industrial division (0-9) i = zone t = 1981

t+ 1=1987

These data were obtained from the Census of Employ- ment via the Department of Employment’s National Online Manpower Information System.

Demand for labour index (DL)

Employment change in an area over a given time period is split into the following components.

AE =Zi’aiAPi+~iaiii+AC+Z’iAaiPi +AU

where AE = total employment change in the area

ai = base year activity rate for age/sex group i APi = natural increase in population in age/sex

group i Mi = net migration into or out of the area of

sex group i AC = net change in commuting to or from the

area

Aai = change in activity rate for age/sex group i

AU = net change in numbers unemployed

The term 2 i a i AP i is the natural increase in the working population of an area and is the expected increase in employment in an area. The term AE - 2 i a i AP i is the employment change due to the relative intensity of demand for labour in an area. This index of the strength of demand for labour (DL) is expressed as a percentage of the area’s base-year employment.

For 1981 to 1987 the demand for labour index is operationalized as follows. First, the 1981 resident population figures for each area were projected forward


six years. Second, these figures were then multiplied by average death rates for each age/sex group to find the survivors. These figures were then multiplied by the average male and female activity rates based on Standard Regions to derive an estimate of the expected working population in each area in 1987.

The expected natural increase in working population was derived from 1981 Census figures for each resident age/sex group in an area and projecting them forward six years. However, because the 1981 Census figures are specified in five-year age bands (O-4, 5-9, 10-14, 15, 16-19, and so on up to 80-84, and 85+) only a certain proportion of the 1981 resident age class counts would lie in the age class counts in 1987. Thus it had to be assumed that the resident population count within each age class in 1981 was evenly distributed. Project- ing the 1981 resident population class counts forward six years to derive a 1987 age class count involved summing the various proportions of 1981 age classes. Therefore the 1987 age class of 25-29 would contain one-quarter of the 1981 class count of 16-19, plus four- fifths of the 1981 class count of 20-24-year-olds.

The expected resident population figures for 1987 derived as above were multiplied by the average death rates for each age/sex group (Office of Population Census and Surveys, 1983). The figures are given for

the annual number of deaths per thousand of the population for age/sex classes for 1981 and this enabled the probability of dying to be calculated for each age/ sex class. These figures were multiplied by the previous population projections to estimate the number of deaths over the projection period. The numbers of deaths in each age/sex group were then subtracted from the original projections for 1987 to estimate the survivors. The resulting figures were then multiplied by the average economic activity rates for males and females in the Standard Regions for 1981 (Central Statistical Office, 1987).

Industrial structure index (ST)

This takes the form

STi=Z’k(WkixGk)

where ST i = industrial structure index of area i

W ki = proportion of total employment in area i in industry k, 1981

Gk = percentage growth or decline nationally between 1981 and 1987 of employment in industry k

k = industrial division (O-9)

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