This article presents the recommended approach for productivity impact calculation, and criteria for determining whether or not the evaluation of productivity effects is relevant for a given type of project. It also discusses how productivity effects can be incorporated into the major measurement methods used for decision-making: multi criteria analysis, economic impact analysis and benefit cost analysis.

Defining the Elements of Productivity Impact

There are several ways to view and measure productivity. It concluded that the most practical measure for assessing the impact of an individual project is in terms of productivity per worker. It concluded that multifactor productivity is too difficult to routinely calculate for project impact evaluation, though this can be done through some regional economic simulation and forecasting models.

It is possible to identify effects of transportation projects on the achievable output or cost of operation for directly affected firms. It grouped those effects into four categories of transportation impact measurement – traditionally-measured transportation cost effects, plus three impact categories that are not traditionally part of the standard traveler benefit calculations: reliability effects, accessibility (agglomerations) effects and intermodal connectivity effects.

Taken together, this leads to a general formula. The formula decomposes the effect of transportation investments on productivity into (1) impact elements that can lead to productivity impacts, and (2) broader measures of overall direct effects on productivity impact.

formula for Defining the Elements of Productivity Impact

The preceding formula for productivity impact elements differs from the concept of social welfare benefit that is used in benefit-cost analysis in that it counts only effects that reduce business operating costs or increase business output (revenue) in a given region. That includes direct business savings associated with changes in vehicle operating costs and worker time for truck freight travel and car business travel. It can also include savings in commuting costs (which is normally considered to be borne by drivers) to the extent that employers are paying a wage premium or direct subsidies to partially compensate workers for excess travel time and expenses.

The productivity elements intentionally do not include the willingness to pay value of personal time savings and all household cost savings (which may be redirected to other forms of spending, but will not affect business productivity), as well as the value of consumer surplus (unless there is some ultimate impact on business costs). Safety impacts are typically not counted except to the extent that they affect business expenses for fleet vehicle repairs or actually lead to changes in insurance costs for personal injury. On the other hand, wider benefits associated with reliability, market access and intermodal connectivity are added.

The productivity elements can lead, through a variety of mechanisms, to direct changes in business operating cost, as well as broader effects on business efficiency. The formula is shown below

Direct Productivity Effect formula

Note: If productivity increases are localized, and if there are supply side constraints on the availability of labor, land or capital inputs to the production process, then it is possible that value added growth could be reduced as other economic activities may be crowded out. However, a regional economic model is normally required to make those adjustments.

Determining the Relevancy of Productivity Impacts

The elements of productivity impact measurement that are itemized in the preceding formulas are useful because they also enable guidance regarding when it is useful to calculate productivity impacts. After all, not all transportation projects will have additional productivity impacts to be measured. In fact, many will not. Projects that affect productivity in the economy are those that affect the unit cost of business operations – changing the amount of labor or capital required to produce a given product, or the volume of product that can be produced for a given set of labor and capital resources. It is thus useful to classify transportation improvement projects into three categories: (1) those that typically have no productivity impact, (2) those that have productivity impacts, but they are already captured by current benefit measurement practices, and (3) those that have additional productivity impacts beyond those already captured by current practices. Each category is explained further below:

The first category is projects that have essentially no productivity impact. This tends to include projects that are focused on addressing safety factors, environmental factors, social or community cohesion factors, or primarily affect recreational and personal trips rather than business related travel. In each of these cases, there may be clear benefits to transportation system users and/or affected parties in the surrounding area, and those benefits may be assigned a dollar value (based on “willingness to pay” surveys). And in some cases, they may affect the economy by shifting the spending patterns of households or the patterns of revenue among businesses (for example, situations where local businesses stand to gain sales from expanded tourism coming into the affected area). However, these types of projects typically have no direct effect on the unit cost of production for any sector in the economy, and hence there is no productivity change in the economy. Examples of this first category of projects are shown in Table 1, part A.

The second category is projects that have cost efficiency effects but have no wider effects. This tends to include projects or policies that enable business-related travel with higher capacity vehicles, higher frequency operations, reduced vehicle operating costs or the ability of long distance trips to bypass local bottlenecks. In each of these cases, there are likely to be direct cost savings to transportation service operators and/or business-related users. By directly reducing capital costs and directly increasing the efficiency of vehicle operations, these types of projects effectively enable more transportation activity per worker — which represents enhanced labor productivity. By reducing the expense per passenger or per ton of freight, they may also enhance capital productivity. However, both those types of cost savings can already be captured by traditional user benefit analysis. So unless there are further reliability, accessibility or connectivity effects (which are not necessarily caused by these types of projects), there may not be any further factors driving productivity change beyond those already measurable by traditional methods. Examples of this second category of projects are shown in Table 1, part B.

The third category is projects with broader impacts on reliability, market access or intermodal connectivity that are not being captured by standard traveler benefit metrics. These is the projects for which this report will be most useful, as inclusion of their broader productivity effects (in addition to currently-measured benefits) could lead to a change in project ranking or funding decisions. What sets them apart is neither the project size nor the magnitude of travel time or cost changes, but rather, the existence of particularly notable impacts on: (a) enhancing reliability (i.e., reducing travel time variability) in congested areas, or (b) enhancing the breadth of inter-zonal access across a region, or (c) enhancing access to, or connecting services a,t a specific intermodal (air, marine or rail) terminal. Table 1, part C shows the types of projects that tend to fall into this category.

Table 1. Classification of Transportation Projects by Form of Productivity Impact

Classification of Transportation Projects by Form of Productivity Impact

Projects with productivity benefits driven by traditionally measured user benefits

C. Projects with wider business benefits not all captured by traditional benefit assessment

C2. Projects that Enhance Regional Accessibility (and Reduce Time Cost) for Business Travel

* Business travel includes freight deliveries and worker travel to deliver services or attend business meetings) – the cost of which is typically paid by businesses. Commuting travel may be included in productivity measurement when the affected trips are predominantly to/from an employment cluster or center where businesses are paying a wage premium to workers in compensation for the longer travel time, greater delay or higher expense associated with working there. That applies most typically for travel to large urban centers with congested access and high parking cost, or to locations at the fringe of a labor market area.

Implications for the practical use of this guide. This three-way categorization in Table 1 is a first step in distinguishing projects that can and should be evaluated in terms of productivity impacts. It enables the following observations.

Projects in categories A, B or C can be evaluated and ranked using either the multicriteria rating or benefit-cost methods. However, only categories B and C are likely to have productivity effects that can be distinguished.

Technically, the productivity calculation steps can be applied to projects in both categories B and C. However, results for category B will be similar to numbers generated by standard traveler benefit methods, so there is a screening step that recommends further analysis only for a subset of projects in category C.

Limitations of currently available research and analysis tools mean that productivity calculation can be fully carried out only for a subset of projects in category C. Currently available tools are applicable primarily for urban or regional road and rail projects, and ground access to intermodal terminals or ports. So while the general methodology can be applied to other types of projects (e.g., inter-city ground transportation as well as air and marine transportation projects), those applications will require more customized analysis.

Screening Process. The magnitude of project impacts on productivity, beyond that normally associated with standard traveler benefits, will depend on the magnitude of the Category C factors — reliability, accessibility and intermodal connectivity.

Incorporating Productivity Impacts into Project Evaluation

For those types of projects that can generate productivity impacts, it will also be important to be sure that the productivity effects are measured and portrayed in a form that is useful for decision analysis. Table 2 shows examples of how productivity impacts can be used in a variety of decision processes that involve some form of project rating or prioritization.

Table 2. Relevance of Productivity Impacts to Transportation Decisions

Table 2. Relevance of Productivity Impacts to Transportation Decisions

Available Rating Methods for Project Prioritization

Every US State DOT and MPO has some process for evaluating and prioritizing requests for enhancement of individual roads and rail transportation facilities. We can classify these processes into three primary methods, which are also commonly used in Europe and elsewhere overseas. Productivity impact measures can be used in all three of these methods. They are summarized below:

  • Multi-Criteria Analysis (MCA) involves scoring projects by measuring or assessing a set of qualitative and quantitative impact factors, which may include productivityrelated factors. Weights are applied to each factor to produce an overall total score for each project. A variant is goal-based rating (GBR), which avoids formal scoring, but still considers how each project rates relative to a set of identified goals.
  • Benefit Cost Analysis (BCA) involves calculating the money value of all traveler benefits and all other social benefits. A transportation system change may have positive or negative effects on both classes of benefit, including productivity changes. Outside the US, BCA is often referred to as Cost-Benefit Analysis (CBA).
  • Economic Impact Analysis (EIA) involves calculating the impact on growth of jobs and income in the economy. There may be direct effects on the productivity of firms or industries in a given areas that use or rely on the transportation system, which drive the calculation of broader impacts on the economy in EIA models, and ultimately lead to wider measures of macroeconomic productivity change.

Each of these project rating methods has a different set of issues and solutions regarding the use of productivity impact measures.

Incorporating Productivity Factors into Multi-Criteria Analysis (MCA)

Current State of MCA Practice. The most common processes used by State DOTs for prioritizing transportation projects are rating systems – either multi-criteria analysis (which scores proposed projects, based on a set of rating criteria and a set of weights applied to them), or goal-based ratings (which consider a set of goal criteria without calculating formal scores). The rating criteria typically include transportation system performance indicators, along with some aspect of equity, economic development impact, environmental sustainability, and compatibility with social/community goals.

Measures of change in productivity outcomes or productivity elements can be used directly in MCA as indicators of economic development impact. Table 3 shows examples of economic development indicators now in use as rating criteria for project prioritization in six State DOTs.

It shows that none of the six states currently include total statewide productivity impact as a rating factor. The practice that comes closest is Wisconsin DOT, which does have a qualitative rating factor called “support freight productivity.” Missouri and Oregon also have qualitative factors recognizing projects that enhance freight movement.

Yet all of the states have rating factors that capture efficiency effects, and all have some additional factors that represent drivers of productivity – measures that lead to reliability, market access and/or connectivity change. However, these rating factors generally pertain to all trips and do not distinguish between impacts on business-related travel (which drivers productivity) and impacts on non-business travel (which does not affect productivity).

Finally, it is notable that several states also have rating factors reflecting overall economic growth impacts –in terms of jobs or statewide GDP (gross domestic product) or GRP (gross regional product). These outcome factors are typically results of increased productivity.

Table 3. Use of Productivity-Related Effects in Multi-Criteria Rating (Six State Examples)

Table 3. Use of Productivity-Related Effects in Multi-Criteria Rating (Six State Examples)

Recommendation for Incorporating Productivity Measurement into MCA. To truly capture productivity impacts in project impact rating systems such as MCA, it is necessary to modify the set of rating factors so that they are shifted from transportation system efficiency metrics to productivity related factors. The productivity factors can be calculated and portrayed at any of three levels:

  1. Measure of overall labor productivity change – calculating direct project effects on labor productivity in the study area. This is the most straightforward way to incorporate productivity into project rating. Inclusion of this measure will give weight and priority to projects that add the most to regional income and job creation.
  2. Measure of input elements that lead to productivity change – using metrics which isolate effects on freight or all business trips and express impacts in terms of change in (1) reliability (reduction in non-recurring delay), (2) labor force access or truck delivery market access (depending on the type of project) and/or (3) connectivity to intermodal terminals. This is a less direct way to recognize productivity effects in decision-making, but it may be desired when there is policy interest in specific goals.
  3. Measure of outcomes generated because of productivity change – using overall economic growth measures generated by regional economic models that explicitly account for all three of the input elements listed in the prior bullet. Depending on the input data provided for the economic model, this approach could provide a more precise and complete calculation of productivity and income growth effects.

Incorporating productivity elements into BCA (Benefit Cost Analysis)

Current State of BCA Practice. Many states regularly conduct some form of user benefit analysis or BCA to assess impacts of proposed projects, as part of their prioritization processes. However, nearly all of those analyses stick to the AASHTO Red Book definitions which primarily calculate the value of travel time savings, vehicle operating cost savings and crash reduction impacts which basically represent the benefit to travelers and transportation system efficiency. Few states incorporate the other productivity elements discussed in this report – benefits associated with greater reliability, accessibility (agglomeration impact) and intermodal connectivity. The reason is not that there is any doubt about the existence of such benefits, but rather, there are questions about how to measure them. This guide brings information to help address those issues and enable broader benefit calculations.

Recommendation for Incorporating Productivity Factors into BCA. To capture productivity impacts in BCA calculations for individual transportation projects, it is necessary to add additional elements of benefit. Productivity cannot by itself be added to BCA because a portion of productivity gain associated with cost savings is already included in traditional BCA. So it is instead necessary to modify current BCA methods to include the heretofore unmeasured portion of business benefits associated improved reliability, accessibility (agglomeration impact) and intermodal connectivity. This involves three actions:

  • Change the Definition of User. While the standard practice has been to measure costs for those driving or riding vehicles, this view is not appropriate for freight since it is the shippers and consignees (receivers) who pay for freight transportation service and hence are the actual users of the transportation system, rather than the truck drivers or freight carriers. Adopting this view allows for the inclusion of logistics costs relating to added labor and overtime cost incurred when loading dock workers have to wait for delayed shipments. It allows for inclusion of added costs of freight transfers between vehicles, time cost of freight inventory in vehicles, and added costs for relief drivers that become necessary when there are road/bridge use restrictions or long delays.
  • Incorporate Reliability as a Benefit Category. Change in average (mean) travel time and the associated valuation of it is part of standard BCA procedures. However, accounting for the value of reliability enhancement (variation around the mean) is not a part of standard BCA procedures, though there is a strong theoretical case building for its inclusion in BCA calculations. The core issue is that variation in travel time caused by traffic incidents tends to grow exponentially in both likelihood and severity as congestion worsens. Unpredictability can also occur in the timing of road closings when trains pass through at-grade rail crossings, and when major road reconstruction projects lead to intermittent and sporadic road closings and detours. Businesses that depend on worker travel and freight deliveries commonly pad their schedules (i.e., build in extra “buffer time”) to enable them to be on-time even in the event of such occurrences. Of course, that can bring costs in terms of added labor time, added inventory requirements and reduced number of deliveries scheduled per vehicle per day. Transportation projects that improve reliability can thus reap the benefits of avoiding those added labor and capital costs. At a certain point, reliability improvements can also enable new supply chain technologies.
  • Include Market Access and Connectivity Effects. Changes in speeds, distances and travel times can also increase the effective density, range and size of a firm’s labor market and customer delivery market. That can enable product and service firms to gain “economies of scale” (changes in business efficiency and unit cost) as well as other agglomeration economies (associated with urbanization and localization benefits) that can also affect business organization and operations. These economic gains associated with broader market access are all in addition to benefits associated with induced trips. While induced trips can include some accessrelated trips (for instance, travelers seeking job opportunities in new markets that were previously considered inaccessible), business scale economies and other agglomeration benefits are above-and-beyond the benefits to induced travelers.

Measuring productivity in EIA (Economic Impact Analysis)

Current State of EIA Practice. Regional economic impact forecasting models are available in the US, Canada and other nations to estimate the macroeconomic impacts of programs or projects that affect productivity. These models forecast how changes in business costs and accessibility patterns lead to broader effects on regional employment, income and business output. The “region” scale of these models may vary from a city to a nation.

To incorporate productivity effects in EIA, it is necessary to use a dynamic economic impact forecasting model that can estimate impacts of business cost and access changes on business investment and trade, as well as changes in the labor supply and demand that can also affect relative wage rates. Static input-output models do not forecast impacts of cost or access changes, and thus cannot be used by themselves to generate transportation project impacts. A spatial economic model embedded within a land use-transportation modeling system may forecast changes in spatial patterns of economic activity within a region, but such models typically assume a fixed forecast of regional economic growth.

A growing number of State DOTs and MPOs are now regularly conducting EIA to assess impacts of proposed transportation projects on economic growth, using a dynamic economic impact forecasting model. Some do so as part of a formal prioritization process, and include job or GDP impacts as a factor in project ratings (e.g., Wisconsin DOT, North Carolina DOT, Kansas DOT and Ohio DOT). Many others apply EIA for major projects, to further support a business case assessment or environmental impact assessment process.

Treatment of Productivity Impacts in EIA. It is notable that regional macroeconomic impact models treat business and household cost savings differently. They treat savings in transportation costs for business-related travel as a reduction in business operating cost (which generates productivity gain), while changes in transportation costs for households are treated as shifts in the pattern of consumer spending (which can affect the mix of industry sales, but will not affect productivity). EIA models thus recognize productivity improvement as the primary basis for real income growth. This is different from BCA, which equally counts both business cost savings and household cost savings as benefits.

Regional economic impact forecasting models also estimate indirect effects on economic growth within a region. That includes effects of relative cost changes that lead to spatial and business sector shifts in trade flows, investment flows and business locations. These further impacts are not counted in BCA, though they may affect multi-factor productivity. This is most likely to be the case if a transportation project is significant enough to cause shifts in trade patterns or in relative prices for labor or capital goods. In those situations, productivity may change as there are changes in the $ valuation of the mix of labor, capital and transportation services used to produce goods.

Recommendation for Incorporating Productivity Factors into EIA. Regional economic impact forecasting models can directly build upon the productivity impact instructions described in Chapter 3, and they may be used to generate estimates of broader economic impacts for designated regions. The types of productivity-related factors that may be input into regional economic impact models are shown in Table 4. Major regional impact modeling systems such as REMITranSight and TREDIS have inputs for exogenous changes in business cost savings and effective market access (though they differ in the form and detail of those inputs). Those changes can be calculated through use of the Chapter 3 instructions. Depending on the economic model, further changes in reliability and connectivity may also be input as either cost and output changes, or directly calculated via procedures that are built into the model system.

Table 4. Productivity Related Inputs to Regional Economic Impact Models

Table 4. Productivity Related Inputs to Regional Economic Impact Models

Regional economic models can substitute for, and improve upon, some of the available tools and calculation processes described in Chapter 3. This can be desirable because regional economic models typically have information regarding how a project would affect specific industries within the study area economy. These models are able to allocate time, cost and access benefits among local industries by building upon datasets that contain employment breakdowns by industry and occupation, and freight shipment breakdowns by industry and commodity.

The results of a regional economic model may be used to help inform project prioritization processes, and may be included in project rating systems. However, economic impact models focus just on business impacts, and hence cannot fully substitute for BCA or MCA rating methods that also incorporate information on broader societal benefits and nonbusiness travel benefits.

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