Appendix B: Cost/Benefit Analysis
Ideally, cost/benefit analysis should capture the entire range of costs and benefits for each alternative considered. For instance, the costs and benefits may include increased farebox revenue, decreased tax revenue, and decreased costs of state or local assistance programs. The analysis should also consider the costs and benefits from multiple perspectives; one stakeholder (local transit agency or state department of revenue), may incur while benefits accrue to more than one stakeholder (such as the state welfare agency, transit riders, and local employers).
Issues that planners can address through cost/benefit analysis are discussed below. The discussion in this appendix is intended to shed light on processes and issues in evaluating costs and benefits. Different cost/benefit methodologies may prove to be more helpful in other situations.
Benefits of Transit to Welfare Reform Efforts and Other Assistance Programs
The following steps are used to measure the benefits of access-to-jobs programs:
- Establish what is to be measured. The benefits of access-to-jobs programs can be measured by assistance program expenditures, net revenues to the state, job creation or other economic growth measure, or net benefit to the target population (for instance, moving households out of poverty). Clearly identifying the benefit to be measured allows the independent variables to be modeled.
- Establish a baseline. If an access-to-jobs program is expected to meet specific goals, from what point will the progress of the program be measured? Baselines can be projected by assistance program expenditures in a business-as-usual future (with existing levels of transportation access) or by historic household incomes for the target population.
- Identify data sources. Census data, U.S. Department of Labor wage surveys, economic development agencies, and social service agency data can all provide information on household incomes of the target population. State and Federal wage data provides information on how wages may change given changes in type of job or industry of employment. Private Industry Councils (PICs), Chambers of Commerce, and non-profit organizations serving particular constituencies are also good data sources.
- Model the relationships between the dependent and independent variables. Using either causal or statistical models, identify how a change in the independent variable (for instance, welfare-to-work household income) affects the dependent variable (for instance, welfare-to-work program expenditures).
What is to be measured
To measure the benefit of access-to-jobs programs, a consideration must be made as to how an increase in transportation access to geographic areas of job growth affects the labor market and the local economy. Access-to-jobs programs can help meet goals for welfare reform, and improve economic conditions for low-income households in a number of ways, including the following:
- Allow jobs to be filled that are currently not being filled.
- Increase the wages and benefits of inner city residents by increasing access to higher-paying jobs located outside existing transit networks. The multiplier effect of greater disposable income translates into increasing inner city investment and distributes the affluence of suburban economic growth over a wider area.
The first two benefits are based on the concept of bringing a piece of the expanding economic pie to those not currently benefiting from the expansion. The third scenario examines the redistributive elements of creating access to jobs. Programs will increase the ability of inner city residents to compete for jobs with those who already have access to jobs. The third scenario is dynamic, and generally beyond the scope of access-to-jobs planning efforts.
Establishing a Baseline
Estimating the effects of transit investment requires establishment of a baseline. An example is job placement projections for the target population (welfare-to-work participants, low-income households). Such projections may not consider adequate transportation as a condition of meeting projections. Filling gaps in transportation access will thus allow the projections to be met.
Identify Data Sources
Access-to-jobs programs modify household income by moving people into higher wage jobs or jobs with upward mobility. Census data, Department of Labor wage surveys, economic development agencies, and social service agency data provide information on household incomes. State and Federal wage data provides information on how wages may change given changes in type of job or industry of employment.
Access-to-jobs programs increase the rate of economic growth in geographic areas or industries suffering labor shortages. Private Industry Councils (PICs), Chambers of Commerce, and state and local economic development agencies maintain information on labor shortages and rates of growth in particular industries. Anecdotal information, such as qualitative surveys, can shape the data to allow modeling of causal relationships.
Model Relationships between Variables
A statistical or econometric model measures statistical relationships in historic data, then forecasts changes based on estimated changes. Historic changes in household income could be statistically linked to levels of transit dependence and distance of job centers from low-income neighborhoods. A causal model sets an equation that defines the dependent variable, then forecasts changes in the variable as the inputs in the formula change. A balance sheet, for instance, shows how as one set of inputs change, the resulting balance changes. Transit expenditures by the state affects the amount of tax revenue generated by transit-dependent households and amount of assistance payments made to transit-dependent households.
Limitations of the Quantitative Analysis
The benefits of welfare reform are described in terms of matching participants with job openings, not in terms of expanding the economic pie (although the two are not mutually exclusive). In order to be consistent with the assumptions underlying the welfare reform, this study examines primarily the latter scenario. Before proceeding with the analysis, however, several potential limitations on this analysis must be noted. First, a number of "job gap" studies cast doubt on the proposition that entry-level jobs are not being filled. Second, questions have been raised regarding the ability of welfare reform measures to access the available jobs and link job seekers with employers. Third, the net effect of welfare reform on local wages and benefit levels is uncertain. Finally, the projections do not explicitly consider the transit limitations on linking jobs with job seekers.
This study focuses on the fourth of these limitations. The first three limitations address the underlying premises of welfare reform, and cannot be mitigated through transit measures. This study examines the changes in job placement and caseload costs if the transit system is not improved. In order to meet the job placement projections, the existing transit system must be supplemented, expanded, or extended. If the system is not improved, the number of subsidized placements will increase over projected levels, with commensurate increases in cost to the welfare system.