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Economic impacts of highway infrastructure improvements Part 1. Conceptual framework
Pergamon c
Economic impacts of highway infrastructure improvements Part 1. Conceptual framework Pavlo s S Kanaroglou * Department
of Geography,
and Willia m P Anderson
McMaster University, Hamilton, Ontario LSS 4K1, Canada
Aleksand r Kazakov The Minis@ of Transportation of Ontario, Research and Development Branch, Downsview, Ontario M3M 158, Canada
1201 Wilson Avenue,
Assessmen t of the impact s of highwa y infrastructur e improvement s on the economies of small communitie s is an integral part of Ontario’ s environmenta l assessmen t process. This paper describes an analytica l framewor k for makin g such assessment s by takin g account of direct and indirect impact s of highwa y improvements , includin g loca l road networ k changes such as bypasses , widenin g of the collectors (connecting links), or widenin g the arteria l road in the vicinit y of the community . Such changes affect the loca l traffi c flow and busines s activities tha t are dependent on transien t traffic. This is the first of tw o related papers. Operationalizatio n of the model wit h examples from Ontari o are described in the follow-u p paper. 0 1998 Publishe d by Elsevie r Science Ltd. All rights reserved Keywords:
highway
impacts,
bypasses,
Ontario
Introduction
linked, direct impacts on certain sectors are known to trigger substantial indirect effects, even on sectors that are not affected directly. This paper proposes an analytical framework that facilitates the assessment of direct and indirect local economic impacts. The objective is to use the framework discussed in this paper as the basis for deriving operational models that can assess the economic impacts on any single community within a geographical area, such as a province in Canada. Operationalization of the proposed framework with readily available data along with examples from communities in Ontario are the subjects of a follow-up paper. The modelling approach employed in this paper is based on the observation that different economic sectors are subject to different types of local direct impacts. The model distinguishes two categories of economic sectors: highway dependent sectors and export ’ oriented sectors. For highway dependent businesses, the main negative impact is a possible diversion of traffic away from their locations which
Improvements to highway infrastructure have both systemic and local impacts. Systemic impacts are changes in the overall performance of the highway network which affect all road users, while local impacts arc those that affect only the people living in the communities along the improved section. Unlike systemic impacts, which are generally positive, some local impacts can be positive and some negative. Negative impacts most frequently occur when an infrastructure project (for example a bypass) redirects through traffic away from existing commercial centres and/or modifies the behaviour of transient travellers, resulting in reduced sales for highway oriented businesses. Positive local impacts occur when the economies of towns along improved routes benefit from the modified traffic flow and reduced transportation costs. Economic impact assessment of highway network modifications is part of the Environmental Assessment Act in Ontario. Because sectors in an economy are *Author
to shorn correspondence
should
‘Local businesses which sell their products boundary are classified as export oriented.
be addressed
203
outside
the community
204
Highway improvement impacts 1. Conceptual framework: P S Kanaroglou et al
results in loss of sales. For export oriented businesses, the main impact will be a reduction in transportation costs which improves their profitability and enhances their ability to sell in external markets. Direct impacts are therefore assessed through two independent operational models for highway oriented and export oriented sectors. Impacts on highway dependent sectors and export oriented sectors constitute direct effects that may trigger substantial indirect effects in the local economy. Methods of estimating the magnitude of indirect effects are known collectively as multiplier analysis. We employ a multiplier model that is based on estimates of the degree of sectoral interdependency in individual communities. Indirect effects may be estimated for all sectors - including those for which there are no direct impacts. The sum of all direct and indirect effects is used to produce estimates of employment change at the sectoral and aggregate levels. The remainder of this paper is divided into four sections. Following this introduction, we describe a conceptual model for estimating the impacts on highway dependent sectors. The next section describes the model for the impacts on export oriented sectors. This is followed by a general description of the inputoutput model employed to assess the indirect economic impacts. The logical sequence for using the proposed models to estimate total impacts is discussed next. The last section offers concluding remarks.
Direct impacts on highway dependent
sectors
Ideas for the model described in this section were mainly taken from Helaakoski (1991) and Andersen et al (1992).> The highway dependent sectors in a community include all those economic activities for which a substantial proportion of sales and employment are generated by customers passing through on their way to other destinations. They include gasoline and service stations, restaurants, and retail activities. Since motorists making long trips may stop over in towns along their route, hotels and motels also fall into this category. These sectors are highly vulnerable to changes in highway infrastructure that might affect the flow of traffic through the community. For example, the construction of a bypass around a community may have a negative impact on these sectors due to the reduced flow of traffic along the roads on which highway dependent firms are located. On the other hand, if a bypass is constructed as part of a larger infrastructure project which expands the capacity of the highways in the region, it may have a positive effect due to the ‘We also consulted with the following sources: Buffington and Burke (1989), Fothergill (1977).Highlands and Islands Development Board (1979), Hirschman and Henderson (1990), Horwood et al (1965). Iowa Department of Transportation (1989), Perera (1990), Seskin (1990), Stephanedes and Eagle (1986), Whitehurst (1965). Winfrey (1969).
increased flow of traffic generated in the proximity of, if not directly through, the community. The model discussed in this section applies to a variety of highway improvement projects. To simplify our arguments, however, let us consider a community that has a single highway passing through it at some point in time t. At a subsequent point in time t + 1, a bypass has been built. The entire bypass is located within the community’s municipal boundaries, but all the existing highway dependent firms are located on the bypassed segment of the original highway. There is, however, access from the bypass to the bypassed highway segment. Define S,A, as the level of sales from highway dependent sector i in community k during time period t. Since no sector is purely highway related, we divide this quantity into two components: Sikt
=
s’~kt
+
s’akt
(1)
where the first component on the right-hand side (the local component) refers to locally generated salts, and the second component (the flow component) refers to sales generated by the flow of through traftic. The local component depends upon local population, income, and other factors which might affect local purchases. We assume that since it does not depend upon flow, it is not affected directly by a bypass. The flow component is positively affected by the volume of traffic along highways in proximity to the community. At time t, the flow component is written as: S’,,, = /I,,ADTr,,
(2)
where ADTr is the total average daily trips passing through the community and [I’,~ is a sector and community specific empirical parameter. We call /j,k the capture coeficient because it represents the amount of business sector i firms in community k arc able to capture from through traffic on a per trip basis. Let us now assume that a bypass is built in the time period between t and t + 1. The total average number of trips passing through the community k is now ADTr,,, +I) which, most likely, is larger than ADTr,,. Also, a proportion of those trips now makes use of the bypass. Let ADTB,,, +,) be the total average daily trips making use of the bypass. The proportion then of the total trips using the bypass is:
Ih =
ADTB,<, , 1) (3)
ADTr,,, +,)
Notice that 01~1~ I 1. When !cr = 0, no traffic is diverted to the bypass and if 11~= 1 all the traffic goes through the bypass. But, in general, we expect 0 < /lk -C 1 . The flow component after the bypass is established can be expressed as: S’,rCti,) = P,,ADTr,(,
+,) -L.ADT&~,
, ,)
(4)
Highway improvement impacts 1. Conceptual framework: P S Kanaroglou et al We make the assumption here that the capture coefficient represents a potential for sales from through traffic, which remains unchanged after the bypass is established. However, these sales are reduced according to the amount of traffic that is diverted to the bypass. The empirical parameter yll, is sector and community specific. It represents the reduction in the propensity of a motorist using the bypass to make purchases in sector i of community k (as compared with a motorist using the original route). The range of values of Y,~is: 0 I 11,~ I pIk. If ;‘,k= 0 then cars using the bypass are as likely to make local purchases as cars using the original highway. This implies that motorists making local purchases of goods and services would use access roads from the bypass. If :‘,A= fi,,: the propensity of motorists using the bypass to make purchases in the community becomes zero - in other words, drivers using the bypass are effectively lost as customers. Subtracting eqn (2) from eqn (4) and also making use of eqn (3): S’IQ,f I, - St,,, = P,,(ADTr,,,
+,) - ADTr,,)
- I-wlkADTrktt + ,)
(5)
This equation gives the change in sales due to the bypass as a function of the average daily traffic before and after the bypass. If the bypass is part of a larger highway expansion project then it is likely that the total volume of traffic ADTr through the area will increase and as a result the first part of the right-hand side in eqn (5) will be positive. Intuitively, the first part in the right-hand side of eqn (5) represents an increase in sales because of a potential increase in the total volume of traffic, while the second part represents a decrease because of the diversion of traffic to the bypass. Defining: 0, =
ADTr,,, +I)
(6)
ADTr,, Equation
(5) may be rewritten
as:
S’Ik(,t 1)- S’,,, = /j,,( 0, - l)ADTr,,
~ /lk:‘,,J,ADTr,,
(7)
A more intuitive indication of what the right-hand side of this equation means can be gained by first assuming that /$k = ;:r (i.e. drivers using the bypass do not make sector i purchases in the community) and rearranging terms to show that the net change in sales will be positive only if:
(8)
or if: ADTr,k,, , I) - ADTr,,, ADTr,,,, / 11
>
ADTh,,+
I)
ADTrjk,,+ ,)
(9)
205
which means that the proportional growth of ADTr is greater than the proportion of trips diverted to the bypass. The local component of sales of a highway dependent sector i in community k is dependent on the spending patterns of the local population. As argued earlier [see discussion around eqn (l)], the presence of the bypass is unlikely to affect these spending patterns. Thus, the impact of the bypass [eqn (7)] can be written as:
Direct impacts on export oriented sectors Unlike the highway dependent sectors, export oriented sectors are not likely to be significantly affected by the changes in local traffic flows that occur due to infrastructure improvements. Such improvements, however, may reduce the transportation cost of moving materials and finished goods in and out of the community by truck, and thus improve the competitiveness of local firms in export oriented sectors. Examples of sectors affected by such changes include agriculture, construction, manufacturing, and the wholesale trade industry. In this section we describe an approach to capturing the positive effects of infrastructure improvements on export oriented sectors of local economies. Let R,, be the transportation cost for a shipment of a commodity between communities k and 1. It has been argued in Woudsma and Kanaroglou (1994) and in Kanaroglou and Woudsma (1994) that the shipment cost can be expressed as: R,, = adk/w”z
(11)
where d,, is the distance between k and 1, w is the weight of the shipment, and a, h,, and h, are parameters that can be estimated econometrically. Parameter h, represents the elasticity of the shipment cost with respect to distance and b, represents the elasticity with respect to weight. Theoretically, these parameters are restricted to a value between 0 and 1. A change in the transportation infrastructure is expected to affect the cost of the shipment in two ways. First, the new infrastructure may reduce the road distance between k and 1. Second, improvements in infrastructure which reduce congestion, accidents, driver fatigue, and other factors that affect user cost may alter the relationship between cost and distance. This can be represented by a decrease in the elasticity h, in eqn (11). We represent with R,,, and Rk,(,+,) the transportation cost of a shipment before and after the highway improvement, respectively. We will also replace the elasticity b, with b, and 6,, , ,) to represent the distance elasticity of transportation cost before and after the highway improvement, respectively. It is assumed that the effect of shipment weight on costs is unaffected.
206
Highway improvement
impacts
I. Conceptual framework: P S Kanaroglou
Thus b, is treated as a constant and we drop its subscript to simplify the notation. With this notation, for the case before the system modification, eqn (11) can be written as:
Rk,,
_
= adtiw(bb1)
(12)
W
In the econometric sense, for any pair of observed values d,, and w, eqn (12) will produce an estimated value of the rate charged per tonne of the commodity shipped. Consider now all the shipments that originate in other communities within the study area and have as a destination community k. Let D,, be the mean distance of those trips defined as: D,, = Z,d,,,iN,, where Nk is the total number of trips that terminate in k. Similarly, let W be the mean weight of those shipments. Using Dk, and W in the right-hand side of eqn (12) will yield an estimate of the mean value of rates per tonne, RW,,, charged for trips destined to k:
Rk, =
RW,,
= -
aDzW(“-
‘)
W
(13)
For the case after the bypass, b(,+,, is used instead of b, in the right-hand side of eqn (13). Also, D,, is replaced which represents the average distance with Da(,+, ,, covered by incoming shipments to k after the road improvement is established. The resulting equation expresses the post-modification value for the rate per tonne of trips to k: RW,,,, Dividing
,) = aD$;:‘,),W”‘+ ‘)
(14)
eqn (14) by eqn (13) we have: RWk(,+,j RW,,
A more intuitively
appealing
D”(t+l, k(t + I j =- D; form of this equation
is:
Equations similar to eqn (15) can be produced for shipments leaving community k, or for the combination of all the shipments that are destined to k or leave k. Thus, RW can be thought of as the average transportation cost per tonne over all the shipments that go in or out of a community. Furthermore, it is convenient to standardize RW by dividing it by the average RW over all the communities in the study area. Such a standardization will not affect eqn (15) since both the numcrator and the denominator in the left-hand side are divided by the same average of RWs. For the remainder of this section, therefore, we shall think of RW, as a transportation index for community k. A value of 1 for this index implies that the average
et al
shipment cost for community k is equal to the average over all the communities under consideration (for example, communities in Ontario). D, in cqn (15) should be thought of as the average distance travelled by all the incoming and outgoing shipments in community k. For communities with diverse economies that are dependent on input from distant communities and send their output to a large number of communities, D, will be large. On the other hand, communities with economics dependent on localised input and shipping to nearby communities will be characterized by a small D,. Equation (15) provides the ratio of RW after and before the highway improvement as a function of two average distance components, as shown in the righthand side. Those components have a specific interprctation. The first represents a distance reduction effect due to the improvement. Since the road improvement is expected to reduce the overall distance, the following relationship holds: 0 < D,(, , ,,/DA, < 1. Furthermore, this ratio is raised to a power b, which is between 0 and 1, implying that the RW ratio increases at a dccrcasing rate with the distance ratio. For two communities with substantially different D,, the same travel distance saved with the implementation of an improvement will produce different impacts on the RW ratio. The community with the larger D, will yield a larger ratio D k(,+,j/Dk, (closer to 1) than the other community, and the RW ratio will be affected less. The second term in the right-hand side of cqn (15) is the elasticity effect. Unlike the first term, this impact is entirely due to the expansion of the transportation infrastructure, such as the addition of new lanes, which will reduce congestion and make access to the community easier. If such an impact is negligible, then b (1+I) = b, and the elasticity impact vanishes. In general, however, b(,+ ,)
=
G,
+
C,,RWk,
+
GSikv
11
(16)
The dependent variable Sgk, is the salts in sector i of community k at some point in time t bcforc the version of the improvement. S,k(, ,) is a time-lagged dependent variable observed at (t - l), which is a point in time earlier than t. C,,,, C,, and CZ, are parameters that can be estimated. Each of the communities under consideration constitutes one observation. Hence, the
Highway improvement
impacts
1. Conceptual
number of observations used to estimate eqn (16) equals the number of communities in the study area. The structure of eqn (16) merits some further justification. The sectors dealt with in this section export the bulk of their output outside community k. The volume of sales for each one of those sectors is affected by a large number of factors that are related to the supply and demand of the sector’s product and operate at the regional, national and international availability and price of all factors of production, such as natural resources, labour and capital stock, may affect the volume of sales. The demand for the sector’s output can be affected by the state of commodity markets nationally and ally, the level of interest rates, and the monetary policy of In short, the volume of sales of such a sector can be affected by a large number of factors. Collecting data for such factors in order to estimate the parameters of an equation, such as eqn (16) above, is clearly beyond the scope of the mode1 described in this paper. The time-lagged variable on the right-hand side of the equation is a proxy that intends to represent all the factors described above. After eqn (16) is estimated it can be used to predict the sectoral sales for any one of the communities under study. We are interested in predicting the sales of sector i in community k after the improvement is complete. To this end, we substitute RWk(,+,, in place of RW,, in the right-hand side of eqn (16) to obtain: S Ik(l+I)=C~II+CIIRWk(I+I)+CZiSlk(t-I)
(17)
It must be emphasized that while S,k, and S++(,_,) are made up of observations at two different points in time before the improvement, S,k(,+,) is the predicted sales of sector i in community k after the improvement. Subtracting eqn (16) from eqn (17): s tk(!+
I)-S,kt
=
WRWk,,.
I, -
RWkt)
(18)
This equation reveals the nature of parameter C,,, which is sector dependent and is expected to be negative. Of course, a reduction in RW due to an improvement is expected to be a small value. By definition, when RW, = 1, community k has transportation cost equal to the average over all the communities considered. RW, = 0.5 means that the average transportation cost for the community k is half the average over all the communities under study. From eqns (1.5) and (18) we now get the final equation:
D(h(t+I)r”t’_1 kl
framework:
P S Kanaroglou
et al
207
b, are to be established through econometric estimation. RW,, and D, are community specific indices that can be calculated from existing data. Estimates of the ratio Dk(,+,,/Dk, should be obtained on the basis of the type of improvement implemented or sought.
Indirect impacts of highway infrastructure improvements3 Multiplier analysis Beyond the direct effects discussed in the previous two sections, a highway improvement may have significant indirect effects as well. As an example of an indirect effect, consider a reduction in retail sales (a highway dependent sector) which comes about as a direct effect of a bypass. If the retailers in question purchase some or all of their inventory from a wholesaler in the community, that wholesaler’s business will also be reduced. Thus, the total loss in sales and employment will be the sum of the losses in the retail sector (direct effect) and the wholesale sector (indirect effect). As another example, consider the case of a manufacturer whose sales increase because of improved highway accessibility (manufacturing is an export oriented sector). If the manufacturer makes local purchases of business services such as maintenance, cleaning, or book-keeping, and transportation services, such as warehousing and shipping, its demand for those services will expand with its sales. Thus, the total gain in sales and employment will be the sum of the gains in the manufacturing sector (direct effect) and the service sector (indirect effect). These examples illustrate two things. The first is that if only direct effects are considered, the actual economic impacts of a highway project could be significantly underestimated. The second is that such a project has impacts that extend beyond highway dependent sectors and export oriented sectors (note that business services do not fall into either of these categories). Thus, the need to extend our analysis to incorporate multiplier effects is clear. The term multiplier is often used in connection with the calculation of indirect effects. A multiplier for a particular sector is the ratio of the total (direct and indirect) impact to the direct impact. For example, if for every five new jobs created in manufacturing there is one additional job created in business services and one additional job created in transportation and storage services, the multiplier for manufacturing is 7/5 = 1.4. This would mean that excluding indirect effects from an employment impact analysis would underestimate actual employment changes by 40%.
1 (19)
Equation (19) is used to calculate the estimated difference in sales in sector i of community k as a result of the improvement. Values for parameters C,,, b(,+,, and
‘This section discusses multiplier and input-output analysis in the specific context of highway infrastructure improvement impacts. An expanded discussion on these topics can be found in any standard input-output analysis text, such as Miller and Blair (1985).
208
Highway improvement
impacts
1. Conceptual,fiamework:
The problem with indirect effects is that they are generally more difficult to estimate than direct effects. This is because they depend not only on the relationship between the local economy and the highway, but also upon the rather complex pattern of relationships among the different sectoral components of the local economy - i.e. they depend upon the local economic structure. This requires a modelling framework that is able to take account of the relationships between all sectors simultaneously. The input-output model is one example of such a framework. It would require a great deal of detailed data collection to be able to meet the data requirements of the input-output model on a community-by-community basis. Fortunately, there are established methods making local approximations for input-output data based on easily obtainable information. We now turn to examine input-output methods as they relate to multiplier analysis and to describe a method for estimating local input-output data based on a combination of national or provincial inputoutput data and community level sectoral employment data.
Input-output
methods for multiplier analysis
Consider an economy that may be divided into n mutually exclusive and exhaustive sectors, each producing a variety of similar or closely related goods and/or services. All the output of each sector is destined to satisfy either intermediate or final demand. Intermediate demand is sales of a good or service from one firm to another as part of a chain of production relations. Final demand is sales of goods and services to its final purchasers. Final demand by convention includes sales to private consumers, to the government, net exports, net inventory changes, and sales of all investment goods. As an example, the saIes of steel, glass, electronic components, mechanical components, paint, etc. to an auto assembly plant count as intermediate demand, while the sales of fully assembled cars to consumers are counted as final demand. Define z,, as the intermediate sales from firms in sector i to firms in sector j within the economy, and y, as sales from firms in sector i to all categories of final demand. The aggregate output of sector i (x,) is: x, = c z,+y, 1
(20)
This information defined for each of the n sectors makes up a set of input-output accounts. In order to derive an input-output model based on a set of input-output accounts, it is necessary to make the following important assumption: the amount of sales from sector i to sector j is a constant linear function of the level of sector j’s output. A well-known, direct implication of this assumption is that scale economies and input substitution do not alter this function. Under
this assumption, coeficient:
P S Kanaroglou
et al
we may define
the constant
Gi
(21)
a,, = 5 and transform
technical
eqn (20) to:
x, = CI a,,x, + y,
(2-9
The information in eqn (22), defined for each of the n sectors, is sufficient for the definition of a basic inputoutput model. To develop this model, it is convenient to recast this expression in matrix notation. Define x and y as n-element column vectors containing the levels of output and final demand respectively for all the sectors. Now define A as a square (n x n) matrix of technical coefficients, such that a,, is the clement in the ith row of the jth column. The combined output expression of all n sectors is now: x=Ax+y
(23)
The most popular form of input-output attempts to predict changes in total sectoral outputs based on changes in final demand alone. This is because the elements of final demand (consumer demand, government demand, exports, etc.) can be usefully treated as exogenous, as they depend upon factors that arc outside the structure of the production system represented in the model. An increase to final demand for the goods and services of any sector will trigger a number of increases in intermediate sales. This is because, in order to meet the new level of final demand, the sector in question will have to increase its purchases from a number of other sectors. Those sectors will, in turn, need to increase their intermediate purchases, and so on until a new equilibrium is reached, in which each sector has an appropriate balance of purchases and sales. This equilibrium can be found by a straightforward algebraic manipulation of eqn (23) to yield: x=(1-A)
‘y
(24)
where I is an (n x n) identity matrix, and the ‘ - 1’ indicates matrix inversion. For the sake of notational convenience, define: M=(I-A)
’
(25)
where M is sometimes called the ‘Leontief inverse matrix’. The typical element m,, is the total sales from sector i which is stimulated by a $1 increase in the final demand for the output of sector j. Note that since this includes both direct and indirect effects, m,, > 1 for all sectors j. The overall multiplier of sector j, as defined above, can be calculated by simply summing across all sectors:
209
Highway improvement impacts 1. Conceptual framework: P S Kanaroglou et al
m, = C mi,
(26)
One further point about this model is worth noting. Since the model is linear, it can be used to project not only the aggregate levels of sectoral outputs but also the changes in total sectoral outputs that come about due to changes in final demand: Ax = M.Ay where A represents period means that, given knowledge to know the initial values of simulate the impact of a change.
E:/Ek
(27)
to period changes. This of M, it is not necessary either x or y in order to particular final demand
Estimating local technical coefficients The major practical limitation of input-output analysis is that its data requirements are not modest. This is because it requires not only measures of output and employment in individual sectors, but also measures of the degree of interaction between n* sectoral pairs, all of which have to be obtained via surveys of individual businesses. Input-output data at some level of sectoral aggregation are available for most countries. Only in a few countries, however, are input-output data available at lower levels of geographical aggregation. In Canada, we are fortunate to have relevant data for each of the individual provinces. For our application we require local data at the community level that are not available, and would be very expensive to obtain by survey. It is therefore necessary to estimate the local data. The goal of the method we have chosen is to define technical coefficients al; at the community level based on observed national or provincial technical coefficients a;. The at represent the sales of firms in sector i within community k to firms in sector j, also within the community. It is true that: aG
able to meet all local intermediate demands, we may assume that ai = a;. We base our estimate of the adequacy of local supply on local sectoral employment data, which are used to calculate a location quotient for each sector. Define the location quotient for sector i in community k as:
(28)
This is because the provincial coefficient represents the sectoral flow when all provincial firms are taken into account. The local coefficient will exclude intermediate sales that come from firms outside the community. Thus, a large proportion of the sales that are included in the calculation of provincial coefficient should not be included in the estimation of the local coefficient. The basic problem then is to know by how much the provincial coefficients should be scaled down to estimate the local coefficients. In principle, we know that for many classes of intermediate demand, there will not be sufficient local supply to meet local demand. Thus the value of a; depends upon the local supply of goods and services from sector i. If sector i is not represented at all in the local economy, we can assume that aI; = 0. If, on the other hand, sector i is well represented in the local economy, and is therefore
(29)
q’ = sector i’s
where EF E” and total employment
the local and Ek EP the local and the location quotient is ratio of sector i’s the local its employment the provincial level. If location quotient is greater than 1, sector has a strong presence in community able to meet all local intermediate demand for goods and less than has a smaller and therefore will be able meet local intermediate The basic rule adopted set local technical the provincial the location quotient is greater than 1, to scale it down proportionately in those the location quotient is less than 1. It is convenient
(30)
qf = 1 if q! > 1 Estimates calculated
of as:
local
technical
qra;
(30) are
then
(31)
Total impacts of highway infrastructure improvements In this section we summarise the series of steps that are necessary in order to obtain estimates of the total impact on sales of all sectors in the economy of a community k. The first task will be to identify such sectors and classify them as highway dependent, export oriented or other. For each of the highway dependent sectors we apply eqn (lo), assuming we have first taken care of identifying values for the community and sector dependent parameters in the right-hand side of this equation. The result will be a direct impact value sector i A& = S,x,r+11-S,,, for each highway dependent in community k. The same process is repeated for each of the export oriented sectors by applying eqn (19). Highway improvements will have no direct impact on the sales of sectors that are neither highway dependent nor export oriented. Thus, for such sectors AS,, = 0.
210
Highway improvement
impacts
1. Conceptual
To estimate the total impact on all sectors we need to apply eqn (27). To this end, we form a column vector A& from the direct impacts AS, for all sectors i in the economy. This vector corresponds to Ay in cqn (27). We also need to calculate a local multiplier matrix Mk with the help of eqns (21), (25), (29) and (31). The total impact vector ATS, will correspond to AX: ATS, = M,.AS,
(32)
It is worth noting that elements of ATS, that correspond to sectors that are neither highway dependent nor export oriented will not necessarily be zero. This is because of the economic interaction of such sectors with the affected sectors.
Conclusion This paper describes a conceptual framework designed to predict the direct and indirect economic impacts of a road infrastructure improvement on a small community located along the improved route. The model recognises that different types of businesses are affected in different ways by specifying different direct impact mechanisms for firms in highway dependent and export oriented economic sectors. As a result, the model will predict qualitatively different direct impacts for communities with different economic structures. The model also predicts indirect impacts. These occur, for example, when an increase or decrease in some firm’s business due to road improvement results in an increase or decrease in the purchases it makes locally. It is important to note that there is one category of economic impacts that the model does not predict. These arc the construction phase impacts, which come about due to employment and purchases necessary to improve the road. Our examination of this issue indicated that the purchases of services and materials are not generally made locally, and thus these impacts are better assessed at a province wide level. The model described in this paper was developed under a contract between the Ministry of Transportation of Ontario and the McMaster Institute for Energy Studies. Operationalization of this model with applications for Ontario communities arc described in the follow-up paper by Kanaroglou et al (1996). The material of both papers along with a detailed review of the literature on the economic impacts of highway infrastructure improvements appears in the final report to the Ministry (Kanaroglou et al, 1995).
jhamework:
P S Kanaroglou
et al
References Andersen, S. J., Mahmassani, H. S., Walton, C. M., Euritt. M. A. and Harrison, R. ( 1992) Trufic umi sputiul irnpucts untl the clo.ss{/icu/ion of’ smull higllwuy-bypasserl cities. Center for Transportation Research, The University of Texas at Austin, Austin. TX. Buffington, J. L. and Burke, D. (1989) Enzploy~~nt und income itnpuct.s of’ high wuy cqxditures on hypuxs. loop und rudiul high wuy irnprol~emen/s. Texas Transportation Institute. A&M University System. College Station, TX: Fothcrgill. E. J. C1977) The impact of highway hvnasses on business acti&es in smill towns. Thesis for the-degree ;;f Master of Urban and Regional Planning. Queen’s University, Kingston, Ont., Canada. Helaakoski, R. (IYYI) Economic effects of highway by-passes on business activities in small cities. Thesis for the Degree of Master of Science in Engineering, University of Texas at Austin, Austin, TX. Highlands and Islands Development Board (lY79) The canzonzic e/fkts qf’ h_vpu.~x~s on locul business. Highlands and Islands Development Board, Inverness. Hirschman, 1. and Henderson. M. (1990) Methodology for assessing local land USC impacts of highways. Trunspo~rution Reseurch Record 1274. Horwood, E. M., Zellner, C. A. and Ludwig. R. L. (1965) Community consequc’ncc5 of‘ high wuy itnprol~ement,s. Nutional Cooperutive Highwuy Reseurch Rq’orf IN. Highway Research Board, Washington, DC. Iowa Department of Transportation (1989) A literuture review of urbun bypu.ss studic,s. Oftice of Project Planning, Iowa Department of Transportation, Iowa. Kanaroglou. P. S. and Woudsma, C. G. (1904) Regulatory reform and the structure of for-hire motor carrier revenues in Ontario: 1987- 1989. Geogruphicul Anulysis 23(3), 246-260. Kanaroglou, P. S., Anderson, W. P. and Kazakov. A. (1995) Asse.s.Gng lhe rconomic impucl.s qf‘ hypusses on ~~rmrllcommunities- in Onturio. ECN-95-01. Ontario Ministry of Transportation, Technical Publications, Room 320. Central Building. 1201 Wilson Avenue, Downsvicw, Ont. M3M IJX, Canada. W. P. and Kazakov, A. (1996) Kanaroglou, P. S., Anderson, Economic impacts of highway infrastructure improvements: operationalization with application to communities in Ontario. UnpubMcMaster lished manuscript, Department of Geography, University, Hamilton, Ont., Canada. Miller, R. and Blair, P. (1985) Inpu-output Amrlysis. Prentice Hall, Englcwood Cliffs, NJ. Perera, M. H. (1990) Framework for classifying and evaluating economic impacts caused by a transportation improvement. Trrn.vportulion Reseurch Record 1274. Sakin, S. N. (1990) Comprehensive framework for highway economic impact assessment: methods and results. Trunsporfution Reseurch Record 1274. Stephanedes, Y. J. and Eagle. D. M. (1986) Time-series analysis of interactions between transportation and manufacturing and retail employment. Trunsportufion Rexurch Record 1074. Whitehurst, C. H. (1965) The rod uround: N study of’ the economic impuct of’ highway by-pux~es on rurul south Curolinu cities und towns. Clemson College, South Carolina. Winfrey. R. (1969) Econonk Anulysis f;>r Nighwtrys. International Textbook Company, Scranton, Pennsylvania. Woudsma, C. G. and Kanaroglou, P. S. (1994) Deregulation of the motor carrier industry: a Canadian example. Environmozt und Plunning A 26, 343-360. Woudsma, C. G. and Kanaroglou. P. S. (1995) Motor carrier policy evolution and rate issues in Ontario. IY83-Y I. Journul of Trunsport Ckgruphy 3(2), 1 I7- 126.
Economic impacts of highway infrastructure improvements Part 1. Conceptual framework Pavlo s S Kanaroglou * Department
of Geography,
and Willia m P Anderson
McMaster University, Hamilton, Ontario LSS 4K1, Canada
Aleksand r Kazakov The Minis@ of Transportation of Ontario, Research and Development Branch, Downsview, Ontario M3M 158, Canada
1201 Wilson Avenue,
Assessmen t of the impact s of highwa y infrastructur e improvement s on the economies of small communitie s is an integral part of Ontario’ s environmenta l assessmen t process. This paper describes an analytica l framewor k for makin g such assessment s by takin g account of direct and indirect impact s of highwa y improvements , includin g loca l road networ k changes such as bypasses , widenin g of the collectors (connecting links), or widenin g the arteria l road in the vicinit y of the community . Such changes affect the loca l traffi c flow and busines s activities tha t are dependent on transien t traffic. This is the first of tw o related papers. Operationalizatio n of the model wit h examples from Ontari o are described in the follow-u p paper. 0 1998 Publishe d by Elsevie r Science Ltd. All rights reserved Keywords:
highway
impacts,
bypasses,
Ontario
Introduction
linked, direct impacts on certain sectors are known to trigger substantial indirect effects, even on sectors that are not affected directly. This paper proposes an analytical framework that facilitates the assessment of direct and indirect local economic impacts. The objective is to use the framework discussed in this paper as the basis for deriving operational models that can assess the economic impacts on any single community within a geographical area, such as a province in Canada. Operationalization of the proposed framework with readily available data along with examples from communities in Ontario are the subjects of a follow-up paper. The modelling approach employed in this paper is based on the observation that different economic sectors are subject to different types of local direct impacts. The model distinguishes two categories of economic sectors: highway dependent sectors and export ’ oriented sectors. For highway dependent businesses, the main negative impact is a possible diversion of traffic away from their locations which
Improvements to highway infrastructure have both systemic and local impacts. Systemic impacts are changes in the overall performance of the highway network which affect all road users, while local impacts arc those that affect only the people living in the communities along the improved section. Unlike systemic impacts, which are generally positive, some local impacts can be positive and some negative. Negative impacts most frequently occur when an infrastructure project (for example a bypass) redirects through traffic away from existing commercial centres and/or modifies the behaviour of transient travellers, resulting in reduced sales for highway oriented businesses. Positive local impacts occur when the economies of towns along improved routes benefit from the modified traffic flow and reduced transportation costs. Economic impact assessment of highway network modifications is part of the Environmental Assessment Act in Ontario. Because sectors in an economy are *Author
to shorn correspondence
should
‘Local businesses which sell their products boundary are classified as export oriented.
be addressed
203
outside
the community
204
Highway improvement impacts 1. Conceptual framework: P S Kanaroglou et al
results in loss of sales. For export oriented businesses, the main impact will be a reduction in transportation costs which improves their profitability and enhances their ability to sell in external markets. Direct impacts are therefore assessed through two independent operational models for highway oriented and export oriented sectors. Impacts on highway dependent sectors and export oriented sectors constitute direct effects that may trigger substantial indirect effects in the local economy. Methods of estimating the magnitude of indirect effects are known collectively as multiplier analysis. We employ a multiplier model that is based on estimates of the degree of sectoral interdependency in individual communities. Indirect effects may be estimated for all sectors - including those for which there are no direct impacts. The sum of all direct and indirect effects is used to produce estimates of employment change at the sectoral and aggregate levels. The remainder of this paper is divided into four sections. Following this introduction, we describe a conceptual model for estimating the impacts on highway dependent sectors. The next section describes the model for the impacts on export oriented sectors. This is followed by a general description of the inputoutput model employed to assess the indirect economic impacts. The logical sequence for using the proposed models to estimate total impacts is discussed next. The last section offers concluding remarks.
Direct impacts on highway dependent
sectors
Ideas for the model described in this section were mainly taken from Helaakoski (1991) and Andersen et al (1992).> The highway dependent sectors in a community include all those economic activities for which a substantial proportion of sales and employment are generated by customers passing through on their way to other destinations. They include gasoline and service stations, restaurants, and retail activities. Since motorists making long trips may stop over in towns along their route, hotels and motels also fall into this category. These sectors are highly vulnerable to changes in highway infrastructure that might affect the flow of traffic through the community. For example, the construction of a bypass around a community may have a negative impact on these sectors due to the reduced flow of traffic along the roads on which highway dependent firms are located. On the other hand, if a bypass is constructed as part of a larger infrastructure project which expands the capacity of the highways in the region, it may have a positive effect due to the ‘We also consulted with the following sources: Buffington and Burke (1989), Fothergill (1977).Highlands and Islands Development Board (1979), Hirschman and Henderson (1990), Horwood et al (1965). Iowa Department of Transportation (1989), Perera (1990), Seskin (1990), Stephanedes and Eagle (1986), Whitehurst (1965). Winfrey (1969).
increased flow of traffic generated in the proximity of, if not directly through, the community. The model discussed in this section applies to a variety of highway improvement projects. To simplify our arguments, however, let us consider a community that has a single highway passing through it at some point in time t. At a subsequent point in time t + 1, a bypass has been built. The entire bypass is located within the community’s municipal boundaries, but all the existing highway dependent firms are located on the bypassed segment of the original highway. There is, however, access from the bypass to the bypassed highway segment. Define S,A, as the level of sales from highway dependent sector i in community k during time period t. Since no sector is purely highway related, we divide this quantity into two components: Sikt
=
s’~kt
+
s’akt
(1)
where the first component on the right-hand side (the local component) refers to locally generated salts, and the second component (the flow component) refers to sales generated by the flow of through traftic. The local component depends upon local population, income, and other factors which might affect local purchases. We assume that since it does not depend upon flow, it is not affected directly by a bypass. The flow component is positively affected by the volume of traffic along highways in proximity to the community. At time t, the flow component is written as: S’,,, = /I,,ADTr,,
(2)
where ADTr is the total average daily trips passing through the community and [I’,~ is a sector and community specific empirical parameter. We call /j,k the capture coeficient because it represents the amount of business sector i firms in community k arc able to capture from through traffic on a per trip basis. Let us now assume that a bypass is built in the time period between t and t + 1. The total average number of trips passing through the community k is now ADTr,,, +I) which, most likely, is larger than ADTr,,. Also, a proportion of those trips now makes use of the bypass. Let ADTB,,, +,) be the total average daily trips making use of the bypass. The proportion then of the total trips using the bypass is:
Ih =
ADTB,<, , 1) (3)
ADTr,,, +,)
Notice that 01~1~ I 1. When !cr = 0, no traffic is diverted to the bypass and if 11~= 1 all the traffic goes through the bypass. But, in general, we expect 0 < /lk -C 1 . The flow component after the bypass is established can be expressed as: S’,rCti,) = P,,ADTr,(,
+,) -L.ADT&~,
, ,)
(4)
Highway improvement impacts 1. Conceptual framework: P S Kanaroglou et al We make the assumption here that the capture coefficient represents a potential for sales from through traffic, which remains unchanged after the bypass is established. However, these sales are reduced according to the amount of traffic that is diverted to the bypass. The empirical parameter yll, is sector and community specific. It represents the reduction in the propensity of a motorist using the bypass to make purchases in sector i of community k (as compared with a motorist using the original route). The range of values of Y,~is: 0 I 11,~ I pIk. If ;‘,k= 0 then cars using the bypass are as likely to make local purchases as cars using the original highway. This implies that motorists making local purchases of goods and services would use access roads from the bypass. If :‘,A= fi,,: the propensity of motorists using the bypass to make purchases in the community becomes zero - in other words, drivers using the bypass are effectively lost as customers. Subtracting eqn (2) from eqn (4) and also making use of eqn (3): S’IQ,f I, - St,,, = P,,(ADTr,,,
+,) - ADTr,,)
- I-wlkADTrktt + ,)
(5)
This equation gives the change in sales due to the bypass as a function of the average daily traffic before and after the bypass. If the bypass is part of a larger highway expansion project then it is likely that the total volume of traffic ADTr through the area will increase and as a result the first part of the right-hand side in eqn (5) will be positive. Intuitively, the first part in the right-hand side of eqn (5) represents an increase in sales because of a potential increase in the total volume of traffic, while the second part represents a decrease because of the diversion of traffic to the bypass. Defining: 0, =
ADTr,,, +I)
(6)
ADTr,, Equation
(5) may be rewritten
as:
S’Ik(,t 1)- S’,,, = /j,,( 0, - l)ADTr,,
~ /lk:‘,,J,ADTr,,
(7)
A more intuitive indication of what the right-hand side of this equation means can be gained by first assuming that /$k = ;:r (i.e. drivers using the bypass do not make sector i purchases in the community) and rearranging terms to show that the net change in sales will be positive only if:
(8)
or if: ADTr,k,, , I) - ADTr,,, ADTr,,,, / 11
>
ADTh,,+
I)
ADTrjk,,+ ,)
(9)
205
which means that the proportional growth of ADTr is greater than the proportion of trips diverted to the bypass. The local component of sales of a highway dependent sector i in community k is dependent on the spending patterns of the local population. As argued earlier [see discussion around eqn (l)], the presence of the bypass is unlikely to affect these spending patterns. Thus, the impact of the bypass [eqn (7)] can be written as:
Direct impacts on export oriented sectors Unlike the highway dependent sectors, export oriented sectors are not likely to be significantly affected by the changes in local traffic flows that occur due to infrastructure improvements. Such improvements, however, may reduce the transportation cost of moving materials and finished goods in and out of the community by truck, and thus improve the competitiveness of local firms in export oriented sectors. Examples of sectors affected by such changes include agriculture, construction, manufacturing, and the wholesale trade industry. In this section we describe an approach to capturing the positive effects of infrastructure improvements on export oriented sectors of local economies. Let R,, be the transportation cost for a shipment of a commodity between communities k and 1. It has been argued in Woudsma and Kanaroglou (1994) and in Kanaroglou and Woudsma (1994) that the shipment cost can be expressed as: R,, = adk/w”z
(11)
where d,, is the distance between k and 1, w is the weight of the shipment, and a, h,, and h, are parameters that can be estimated econometrically. Parameter h, represents the elasticity of the shipment cost with respect to distance and b, represents the elasticity with respect to weight. Theoretically, these parameters are restricted to a value between 0 and 1. A change in the transportation infrastructure is expected to affect the cost of the shipment in two ways. First, the new infrastructure may reduce the road distance between k and 1. Second, improvements in infrastructure which reduce congestion, accidents, driver fatigue, and other factors that affect user cost may alter the relationship between cost and distance. This can be represented by a decrease in the elasticity h, in eqn (11). We represent with R,,, and Rk,(,+,) the transportation cost of a shipment before and after the highway improvement, respectively. We will also replace the elasticity b, with b, and 6,, , ,) to represent the distance elasticity of transportation cost before and after the highway improvement, respectively. It is assumed that the effect of shipment weight on costs is unaffected.
206
Highway improvement
impacts
I. Conceptual framework: P S Kanaroglou
Thus b, is treated as a constant and we drop its subscript to simplify the notation. With this notation, for the case before the system modification, eqn (11) can be written as:
Rk,,
_
= adtiw(bb1)
(12)
W
In the econometric sense, for any pair of observed values d,, and w, eqn (12) will produce an estimated value of the rate charged per tonne of the commodity shipped. Consider now all the shipments that originate in other communities within the study area and have as a destination community k. Let D,, be the mean distance of those trips defined as: D,, = Z,d,,,iN,, where Nk is the total number of trips that terminate in k. Similarly, let W be the mean weight of those shipments. Using Dk, and W in the right-hand side of eqn (12) will yield an estimate of the mean value of rates per tonne, RW,,, charged for trips destined to k:
Rk, =
RW,,
= -
aDzW(“-
‘)
W
(13)
For the case after the bypass, b(,+,, is used instead of b, in the right-hand side of eqn (13). Also, D,, is replaced which represents the average distance with Da(,+, ,, covered by incoming shipments to k after the road improvement is established. The resulting equation expresses the post-modification value for the rate per tonne of trips to k: RW,,,, Dividing
,) = aD$;:‘,),W”‘+ ‘)
(14)
eqn (14) by eqn (13) we have: RWk(,+,j RW,,
A more intuitively
appealing
D”(t+l, k(t + I j =- D; form of this equation
is:
Equations similar to eqn (15) can be produced for shipments leaving community k, or for the combination of all the shipments that are destined to k or leave k. Thus, RW can be thought of as the average transportation cost per tonne over all the shipments that go in or out of a community. Furthermore, it is convenient to standardize RW by dividing it by the average RW over all the communities in the study area. Such a standardization will not affect eqn (15) since both the numcrator and the denominator in the left-hand side are divided by the same average of RWs. For the remainder of this section, therefore, we shall think of RW, as a transportation index for community k. A value of 1 for this index implies that the average
et al
shipment cost for community k is equal to the average over all the communities under consideration (for example, communities in Ontario). D, in cqn (15) should be thought of as the average distance travelled by all the incoming and outgoing shipments in community k. For communities with diverse economies that are dependent on input from distant communities and send their output to a large number of communities, D, will be large. On the other hand, communities with economics dependent on localised input and shipping to nearby communities will be characterized by a small D,. Equation (15) provides the ratio of RW after and before the highway improvement as a function of two average distance components, as shown in the righthand side. Those components have a specific interprctation. The first represents a distance reduction effect due to the improvement. Since the road improvement is expected to reduce the overall distance, the following relationship holds: 0 < D,(, , ,,/DA, < 1. Furthermore, this ratio is raised to a power b, which is between 0 and 1, implying that the RW ratio increases at a dccrcasing rate with the distance ratio. For two communities with substantially different D,, the same travel distance saved with the implementation of an improvement will produce different impacts on the RW ratio. The community with the larger D, will yield a larger ratio D k(,+,j/Dk, (closer to 1) than the other community, and the RW ratio will be affected less. The second term in the right-hand side of cqn (15) is the elasticity effect. Unlike the first term, this impact is entirely due to the expansion of the transportation infrastructure, such as the addition of new lanes, which will reduce congestion and make access to the community easier. If such an impact is negligible, then b (1+I) = b, and the elasticity impact vanishes. In general, however, b(,+ ,)
=
G,
+
C,,RWk,
+
GSikv
11
(16)
The dependent variable Sgk, is the salts in sector i of community k at some point in time t bcforc the version of the improvement. S,k(, ,) is a time-lagged dependent variable observed at (t - l), which is a point in time earlier than t. C,,,, C,, and CZ, are parameters that can be estimated. Each of the communities under consideration constitutes one observation. Hence, the
Highway improvement
impacts
1. Conceptual
number of observations used to estimate eqn (16) equals the number of communities in the study area. The structure of eqn (16) merits some further justification. The sectors dealt with in this section export the bulk of their output outside community k. The volume of sales for each one of those sectors is affected by a large number of factors that are related to the supply and demand of the sector’s product and operate at the regional, national and international availability and price of all factors of production, such as natural resources, labour and capital stock, may affect the volume of sales. The demand for the sector’s output can be affected by the state of commodity markets nationally and ally, the level of interest rates, and the monetary policy of In short, the volume of sales of such a sector can be affected by a large number of factors. Collecting data for such factors in order to estimate the parameters of an equation, such as eqn (16) above, is clearly beyond the scope of the mode1 described in this paper. The time-lagged variable on the right-hand side of the equation is a proxy that intends to represent all the factors described above. After eqn (16) is estimated it can be used to predict the sectoral sales for any one of the communities under study. We are interested in predicting the sales of sector i in community k after the improvement is complete. To this end, we substitute RWk(,+,, in place of RW,, in the right-hand side of eqn (16) to obtain: S Ik(l+I)=C~II+CIIRWk(I+I)+CZiSlk(t-I)
(17)
It must be emphasized that while S,k, and S++(,_,) are made up of observations at two different points in time before the improvement, S,k(,+,) is the predicted sales of sector i in community k after the improvement. Subtracting eqn (16) from eqn (17): s tk(!+
I)-S,kt
=
WRWk,,.
I, -
RWkt)
(18)
This equation reveals the nature of parameter C,,, which is sector dependent and is expected to be negative. Of course, a reduction in RW due to an improvement is expected to be a small value. By definition, when RW, = 1, community k has transportation cost equal to the average over all the communities considered. RW, = 0.5 means that the average transportation cost for the community k is half the average over all the communities under study. From eqns (1.5) and (18) we now get the final equation:
D(h(t+I)r”t’_1 kl
framework:
P S Kanaroglou
et al
207
b, are to be established through econometric estimation. RW,, and D, are community specific indices that can be calculated from existing data. Estimates of the ratio Dk(,+,,/Dk, should be obtained on the basis of the type of improvement implemented or sought.
Indirect impacts of highway infrastructure improvements3 Multiplier analysis Beyond the direct effects discussed in the previous two sections, a highway improvement may have significant indirect effects as well. As an example of an indirect effect, consider a reduction in retail sales (a highway dependent sector) which comes about as a direct effect of a bypass. If the retailers in question purchase some or all of their inventory from a wholesaler in the community, that wholesaler’s business will also be reduced. Thus, the total loss in sales and employment will be the sum of the losses in the retail sector (direct effect) and the wholesale sector (indirect effect). As another example, consider the case of a manufacturer whose sales increase because of improved highway accessibility (manufacturing is an export oriented sector). If the manufacturer makes local purchases of business services such as maintenance, cleaning, or book-keeping, and transportation services, such as warehousing and shipping, its demand for those services will expand with its sales. Thus, the total gain in sales and employment will be the sum of the gains in the manufacturing sector (direct effect) and the service sector (indirect effect). These examples illustrate two things. The first is that if only direct effects are considered, the actual economic impacts of a highway project could be significantly underestimated. The second is that such a project has impacts that extend beyond highway dependent sectors and export oriented sectors (note that business services do not fall into either of these categories). Thus, the need to extend our analysis to incorporate multiplier effects is clear. The term multiplier is often used in connection with the calculation of indirect effects. A multiplier for a particular sector is the ratio of the total (direct and indirect) impact to the direct impact. For example, if for every five new jobs created in manufacturing there is one additional job created in business services and one additional job created in transportation and storage services, the multiplier for manufacturing is 7/5 = 1.4. This would mean that excluding indirect effects from an employment impact analysis would underestimate actual employment changes by 40%.
1 (19)
Equation (19) is used to calculate the estimated difference in sales in sector i of community k as a result of the improvement. Values for parameters C,,, b(,+,, and
‘This section discusses multiplier and input-output analysis in the specific context of highway infrastructure improvement impacts. An expanded discussion on these topics can be found in any standard input-output analysis text, such as Miller and Blair (1985).
208
Highway improvement
impacts
1. Conceptual,fiamework:
The problem with indirect effects is that they are generally more difficult to estimate than direct effects. This is because they depend not only on the relationship between the local economy and the highway, but also upon the rather complex pattern of relationships among the different sectoral components of the local economy - i.e. they depend upon the local economic structure. This requires a modelling framework that is able to take account of the relationships between all sectors simultaneously. The input-output model is one example of such a framework. It would require a great deal of detailed data collection to be able to meet the data requirements of the input-output model on a community-by-community basis. Fortunately, there are established methods making local approximations for input-output data based on easily obtainable information. We now turn to examine input-output methods as they relate to multiplier analysis and to describe a method for estimating local input-output data based on a combination of national or provincial inputoutput data and community level sectoral employment data.
Input-output
methods for multiplier analysis
Consider an economy that may be divided into n mutually exclusive and exhaustive sectors, each producing a variety of similar or closely related goods and/or services. All the output of each sector is destined to satisfy either intermediate or final demand. Intermediate demand is sales of a good or service from one firm to another as part of a chain of production relations. Final demand is sales of goods and services to its final purchasers. Final demand by convention includes sales to private consumers, to the government, net exports, net inventory changes, and sales of all investment goods. As an example, the saIes of steel, glass, electronic components, mechanical components, paint, etc. to an auto assembly plant count as intermediate demand, while the sales of fully assembled cars to consumers are counted as final demand. Define z,, as the intermediate sales from firms in sector i to firms in sector j within the economy, and y, as sales from firms in sector i to all categories of final demand. The aggregate output of sector i (x,) is: x, = c z,+y, 1
(20)
This information defined for each of the n sectors makes up a set of input-output accounts. In order to derive an input-output model based on a set of input-output accounts, it is necessary to make the following important assumption: the amount of sales from sector i to sector j is a constant linear function of the level of sector j’s output. A well-known, direct implication of this assumption is that scale economies and input substitution do not alter this function. Under
this assumption, coeficient:
P S Kanaroglou
et al
we may define
the constant
Gi
(21)
a,, = 5 and transform
technical
eqn (20) to:
x, = CI a,,x, + y,
(2-9
The information in eqn (22), defined for each of the n sectors, is sufficient for the definition of a basic inputoutput model. To develop this model, it is convenient to recast this expression in matrix notation. Define x and y as n-element column vectors containing the levels of output and final demand respectively for all the sectors. Now define A as a square (n x n) matrix of technical coefficients, such that a,, is the clement in the ith row of the jth column. The combined output expression of all n sectors is now: x=Ax+y
(23)
The most popular form of input-output attempts to predict changes in total sectoral outputs based on changes in final demand alone. This is because the elements of final demand (consumer demand, government demand, exports, etc.) can be usefully treated as exogenous, as they depend upon factors that arc outside the structure of the production system represented in the model. An increase to final demand for the goods and services of any sector will trigger a number of increases in intermediate sales. This is because, in order to meet the new level of final demand, the sector in question will have to increase its purchases from a number of other sectors. Those sectors will, in turn, need to increase their intermediate purchases, and so on until a new equilibrium is reached, in which each sector has an appropriate balance of purchases and sales. This equilibrium can be found by a straightforward algebraic manipulation of eqn (23) to yield: x=(1-A)
‘y
(24)
where I is an (n x n) identity matrix, and the ‘ - 1’ indicates matrix inversion. For the sake of notational convenience, define: M=(I-A)
’
(25)
where M is sometimes called the ‘Leontief inverse matrix’. The typical element m,, is the total sales from sector i which is stimulated by a $1 increase in the final demand for the output of sector j. Note that since this includes both direct and indirect effects, m,, > 1 for all sectors j. The overall multiplier of sector j, as defined above, can be calculated by simply summing across all sectors:
209
Highway improvement impacts 1. Conceptual framework: P S Kanaroglou et al
m, = C mi,
(26)
One further point about this model is worth noting. Since the model is linear, it can be used to project not only the aggregate levels of sectoral outputs but also the changes in total sectoral outputs that come about due to changes in final demand: Ax = M.Ay where A represents period means that, given knowledge to know the initial values of simulate the impact of a change.
E:/Ek
(27)
to period changes. This of M, it is not necessary either x or y in order to particular final demand
Estimating local technical coefficients The major practical limitation of input-output analysis is that its data requirements are not modest. This is because it requires not only measures of output and employment in individual sectors, but also measures of the degree of interaction between n* sectoral pairs, all of which have to be obtained via surveys of individual businesses. Input-output data at some level of sectoral aggregation are available for most countries. Only in a few countries, however, are input-output data available at lower levels of geographical aggregation. In Canada, we are fortunate to have relevant data for each of the individual provinces. For our application we require local data at the community level that are not available, and would be very expensive to obtain by survey. It is therefore necessary to estimate the local data. The goal of the method we have chosen is to define technical coefficients al; at the community level based on observed national or provincial technical coefficients a;. The at represent the sales of firms in sector i within community k to firms in sector j, also within the community. It is true that: aG
able to meet all local intermediate demands, we may assume that ai = a;. We base our estimate of the adequacy of local supply on local sectoral employment data, which are used to calculate a location quotient for each sector. Define the location quotient for sector i in community k as:
(28)
This is because the provincial coefficient represents the sectoral flow when all provincial firms are taken into account. The local coefficient will exclude intermediate sales that come from firms outside the community. Thus, a large proportion of the sales that are included in the calculation of provincial coefficient should not be included in the estimation of the local coefficient. The basic problem then is to know by how much the provincial coefficients should be scaled down to estimate the local coefficients. In principle, we know that for many classes of intermediate demand, there will not be sufficient local supply to meet local demand. Thus the value of a; depends upon the local supply of goods and services from sector i. If sector i is not represented at all in the local economy, we can assume that aI; = 0. If, on the other hand, sector i is well represented in the local economy, and is therefore
(29)
q’ = sector i’s
where EF E” and total employment
the local and Ek EP the local and the location quotient is ratio of sector i’s the local its employment the provincial level. If location quotient is greater than 1, sector has a strong presence in community able to meet all local intermediate demand for goods and less than has a smaller and therefore will be able meet local intermediate The basic rule adopted set local technical the provincial the location quotient is greater than 1, to scale it down proportionately in those the location quotient is less than 1. It is convenient
(30)
qf = 1 if q! > 1 Estimates calculated
of as:
local
technical
qra;
(30) are
then
(31)
Total impacts of highway infrastructure improvements In this section we summarise the series of steps that are necessary in order to obtain estimates of the total impact on sales of all sectors in the economy of a community k. The first task will be to identify such sectors and classify them as highway dependent, export oriented or other. For each of the highway dependent sectors we apply eqn (lo), assuming we have first taken care of identifying values for the community and sector dependent parameters in the right-hand side of this equation. The result will be a direct impact value sector i A& = S,x,r+11-S,,, for each highway dependent in community k. The same process is repeated for each of the export oriented sectors by applying eqn (19). Highway improvements will have no direct impact on the sales of sectors that are neither highway dependent nor export oriented. Thus, for such sectors AS,, = 0.
210
Highway improvement
impacts
1. Conceptual
To estimate the total impact on all sectors we need to apply eqn (27). To this end, we form a column vector A& from the direct impacts AS, for all sectors i in the economy. This vector corresponds to Ay in cqn (27). We also need to calculate a local multiplier matrix Mk with the help of eqns (21), (25), (29) and (31). The total impact vector ATS, will correspond to AX: ATS, = M,.AS,
(32)
It is worth noting that elements of ATS, that correspond to sectors that are neither highway dependent nor export oriented will not necessarily be zero. This is because of the economic interaction of such sectors with the affected sectors.
Conclusion This paper describes a conceptual framework designed to predict the direct and indirect economic impacts of a road infrastructure improvement on a small community located along the improved route. The model recognises that different types of businesses are affected in different ways by specifying different direct impact mechanisms for firms in highway dependent and export oriented economic sectors. As a result, the model will predict qualitatively different direct impacts for communities with different economic structures. The model also predicts indirect impacts. These occur, for example, when an increase or decrease in some firm’s business due to road improvement results in an increase or decrease in the purchases it makes locally. It is important to note that there is one category of economic impacts that the model does not predict. These arc the construction phase impacts, which come about due to employment and purchases necessary to improve the road. Our examination of this issue indicated that the purchases of services and materials are not generally made locally, and thus these impacts are better assessed at a province wide level. The model described in this paper was developed under a contract between the Ministry of Transportation of Ontario and the McMaster Institute for Energy Studies. Operationalization of this model with applications for Ontario communities arc described in the follow-up paper by Kanaroglou et al (1996). The material of both papers along with a detailed review of the literature on the economic impacts of highway infrastructure improvements appears in the final report to the Ministry (Kanaroglou et al, 1995).
jhamework:
P S Kanaroglou
et al
References Andersen, S. J., Mahmassani, H. S., Walton, C. M., Euritt. M. A. and Harrison, R. ( 1992) Trufic umi sputiul irnpucts untl the clo.ss{/icu/ion of’ smull higllwuy-bypasserl cities. Center for Transportation Research, The University of Texas at Austin, Austin. TX. Buffington, J. L. and Burke, D. (1989) Enzploy~~nt und income itnpuct.s of’ high wuy cqxditures on hypuxs. loop und rudiul high wuy irnprol~emen/s. Texas Transportation Institute. A&M University System. College Station, TX: Fothcrgill. E. J. C1977) The impact of highway hvnasses on business acti&es in smill towns. Thesis for the-degree ;;f Master of Urban and Regional Planning. Queen’s University, Kingston, Ont., Canada. Helaakoski, R. (IYYI) Economic effects of highway by-passes on business activities in small cities. Thesis for the Degree of Master of Science in Engineering, University of Texas at Austin, Austin, TX. Highlands and Islands Development Board (lY79) The canzonzic e/fkts qf’ h_vpu.~x~s on locul business. Highlands and Islands Development Board, Inverness. Hirschman, 1. and Henderson. M. (1990) Methodology for assessing local land USC impacts of highways. Trunspo~rution Reseurch Record 1274. Horwood, E. M., Zellner, C. A. and Ludwig. R. L. (1965) Community consequc’ncc5 of‘ high wuy itnprol~ement,s. Nutional Cooperutive Highwuy Reseurch Rq’orf IN. Highway Research Board, Washington, DC. Iowa Department of Transportation (1989) A literuture review of urbun bypu.ss studic,s. Oftice of Project Planning, Iowa Department of Transportation, Iowa. Kanaroglou. P. S. and Woudsma, C. G. (1904) Regulatory reform and the structure of for-hire motor carrier revenues in Ontario: 1987- 1989. Geogruphicul Anulysis 23(3), 246-260. Kanaroglou, P. S., Anderson, W. P. and Kazakov. A. (1995) Asse.s.Gng lhe rconomic impucl.s qf‘ hypusses on ~~rmrllcommunities- in Onturio. ECN-95-01. Ontario Ministry of Transportation, Technical Publications, Room 320. Central Building. 1201 Wilson Avenue, Downsvicw, Ont. M3M IJX, Canada. W. P. and Kazakov, A. (1996) Kanaroglou, P. S., Anderson, Economic impacts of highway infrastructure improvements: operationalization with application to communities in Ontario. UnpubMcMaster lished manuscript, Department of Geography, University, Hamilton, Ont., Canada. Miller, R. and Blair, P. (1985) Inpu-output Amrlysis. Prentice Hall, Englcwood Cliffs, NJ. Perera, M. H. (1990) Framework for classifying and evaluating economic impacts caused by a transportation improvement. Trrn.vportulion Reseurch Record 1274. Sakin, S. N. (1990) Comprehensive framework for highway economic impact assessment: methods and results. Trunsporfution Reseurch Record 1274. Stephanedes, Y. J. and Eagle. D. M. (1986) Time-series analysis of interactions between transportation and manufacturing and retail employment. Trunsportufion Rexurch Record 1074. Whitehurst, C. H. (1965) The rod uround: N study of’ the economic impuct of’ highway by-pux~es on rurul south Curolinu cities und towns. Clemson College, South Carolina. Winfrey. R. (1969) Econonk Anulysis f;>r Nighwtrys. International Textbook Company, Scranton, Pennsylvania. Woudsma, C. G. and Kanaroglou, P. S. (1994) Deregulation of the motor carrier industry: a Canadian example. Environmozt und Plunning A 26, 343-360. Woudsma, C. G. and Kanaroglou. P. S. (1995) Motor carrier policy evolution and rate issues in Ontario. IY83-Y I. Journul of Trunsport Ckgruphy 3(2), 1 I7- 126.