Measurement of Program Scale Metrics

Program scale metrics are meant to capture the cumulative effects of multiple restoration and improvement projects in a region (gulf-wide, state-level, estuary, watershed). For these metrics, data collection and analysis should be conducted at a regional scale to provide measures that can be used by programs and projects throughout the Gulf to understand and report on their impact.  These program scale metrics and analytics are designed to complement the project scale metrics. The data and analysis needed are not yet in place to operationalize this. Spatially explicit data on restoration projects needs to be compared with spatially explicit data on economic and social change to track and report on impacts over time.  We hope to collaborate with others interested in impacts from disasters (e.g., hurricanes, oil spills) to implement these regional scale efforts in the future.  There are three types of information that still need to be collected or analyzed for this regional scale effort to occur:

Restoration project data

For all program scale metrics, an underlying data set on restoration project types, characteristics, and location are needed. With these data, these projects’ attributes can then be correlated with economic and social data, to indicate which types of projects (oyster restoration vs. treatment wetland), where, are having what type of social/economic outcomes (e.g., jobs, lower exposure to HABs toxins). NOAA’s Restoration Atlas, ELI gulf of Mexico Restoration Projects Database, and the Deepwater Horizon Project Tracker have much of this data, but would need to be updated and enhanced with data from other funders and project types.

Economic Activity Analysis

The program scale economic activity metrics (e.g., jobs, labor income, gross state product, and total industry output) require the use of economic impact modeling (e.g. using a modeling platform like IMPLAN).  Spatially explicit economic outputs from this model would be connected with the spatially explicit restoration data described above to estimate the impact that a program of restoration projects is having on traditional economic activity.  For example, what is the overall economic impact (jobs, revenue, value added) of project spending in the last 3 years in the Gulf of Mexico? Which types of restoration contributed most to recreational fishing vs. those that contributed most to commercial fishing? How does location of a project or type of project influence its economic impact?

For some sectors there is sufficient data available to conduct economic impact modeling now; for others the data either needs to be disaggregated and shared in a different way or newly collected. For example:

  • Commercial fishing and shellfish harvest – Sufficient data are currently available.
  • Commercial aquaculture harvest – Data are collected by USDA at a county level but are not separated by species so it is difficult to separate out oyster aquaculture specifically.
  • Recreational fishing and shellfish harvest – Data are collected at the county scale by states, but need to be made available for analysis and disaggregated to track fish or shellfish of interest.
  • Recreation and tourism – These data are available at a county level, but would need to be disaggregated to get to sub-county impacts and differentiate types of recreation and tourism.

We recommend that economic impact modeling be conducted every 3-5 years to capture the change that a restoration effort may have on a community over time.

Knowledge and community well-being surveys

Program scale metrics associated with knowledge and well-being may require the use of large-scale regional surveys.  Spatially explicit survey results would be connected with the spatially explicit restoration data, described above, to assess the impact that a program of restoration projects are having on societal well-being.  Ideally a regional or program survey would be conducted regularly (every 3-5 years) to collect data relevant to all the listed outcomes.  
Topics to assess may include:

  • a person’s knowledge or perception of the connection between a particular habitat or project type and its effects on the environment and communities;  
  • assess instrumental or relational values toward a particular project;
  • a person’s well-being associated with their livelihood, health, and social interactions; and
  • personal information about location, age, race, income, first language, etc., to determine the distribution of impacts and benefits to different communities.