To assess and measure the progress of the three Northern Michigan regions in the knowledge economy, the Michigan State University Center for Community and Economic Development (CCED) project team and its three regional partners-the Eastern Upper Peninsula Regional Planning and Development Commission (EUPRPDC), Northeast Michigan Council of Governments (NEMCOG), and Northwest Michigan Council of Governments (NWMCOG)-developed a set of 27 knowledge economy Indicators.

In collaboration with the three partners, the project team examined the MSU CCED's 2006 Michigan Knowledge Economy Index: A County-Level Assessment of Michigan's Knowledge Economy (LaMore, Melcher, Supanich-Goldner, & Wilkes, 2006) to commence development of this set of regional knowledge economy indicators. The 2006 CCED county assessment was based on the national 2002 State New Economy Index published by Robert D. Atkinson (2002) of the Progressive Policy Institute (now with the Information Technology and Innovation Foundation [ITIF]). The project team also reviewed the current State New Economy Index, published in November, 2008 by the ITIF and Kauffman Foundation (Atkinson & Andes, 2008).

The principal objective of this collaborative process between MSU CCED and its three regional planning partners was to identify meaningful and useful knowledge economy strategies derived from reliable indicators for the three predominantly rural regions. The use of clear indicators can empower planners and stakeholders to understand current conditions, prioritize strategic actions, and track quantifiable changes associated with the transformation of economic development strategies from a traditional manufacturing-based model to one consistent with the dynamics of the global knowledge economy.

The 2006 CCED assessment consisted of 16 knowledge economy indicators in five categories-1) Knowledge Jobs, 2) Digital Economy, 3) Innovation Capacity, 4) Globalization, and 5) Economic Dynamism. The MSU CCED-regional partner team selected seven of those 16 indicators and added one new category-talent. The collaborative team also added five new measures to describe the regional context and 20 new indicators for a total of 32 measures.

This robust collaborative process between the MSU-based project team and the three regional partners was grounded in thorough research and analysis as well as feedback from state agency technical experts and business and economic leaders in the regions. The overarching goal is to provide a practical set of knowledge economy indicators that will assist regional planners and stakeholders in aligning their economic development strategies with the demands and requirements of the global knowledge economy.

The Current Lenses to the Emerging Knowledge Economy in Northern Michigan and the Easter UP

On balance, what the CCED-regional partner team has done is to create a new set of lenses through which economic development-related activity in the knowledge economy can be prioritized, tracked, measured, and assessed by the regional planning partners and their stakeholders.

This report consists of eight sections. In Section 1, the five measures that provide a demographic and overall economic context for the three regions are described. In Sections 2-7, the six knowledge economy categories and 27 indicators developed for this assessment are presented and discussed. Section 8 provides the conclusion of our regional assessment. The eight sections of the assessment, then, consist of the following:

1. Regional Context
2. Talent
3. Innovation Capacity
4. Knowledge Jobs
5. Digital Economy
6. Globalization
7. Economic Dynamism
8. Conclusion

In describing the 27 regional knowledge economy indicators, state, Great Lakes Midwest, and U.S. data provide a comparative context to illuminate what a particular measure means compared to state, regional (multi-state), or national performance levels. As most economic data is collected and aggregated at statewide or national levels, discerning any meaning from breakouts of regional (i.e., sub-state) data can be problematic. In many cases, generating primary data would have been preferable to using existing data sets, but such data collection was beyond the scope and resources of this project.

The primary intent underlying our data collection and analysis is to provide a topical and relevant set of lenses to understand where the regions currently stand in what we currently understand about the knowledge economy. In this respect, these knowledge economy indicators represent an initial economic development planning methodology that seeks to capture a very fast-moving target. The world of ubiquitous email, Google, Youtube, wireless communications, Blackberries, hybrid vehicles, and broadband access issues didn't even exist just 11 years ago. Google was founded September 7, 1998. Youtube started up in 2005. Over 21 million people now use BlackBerry smartphones on over 375 wireless networks in 140 countries around the world, according to Research in Motion (n.d.), the BlackBerry maker. Rapid change is the new routine. Innovation is the major economic imperative.

But we can't make progress in the knowledge economy if we don't know what indicators to use to assist in prioritizing strategies and measuring that progress. Put another way, we can't make progress if we don't know where we're going. The indicators identified in this report, then, represent an attempt to get our bearings in a stormy sea of economic changes. The sustainable recovery and reinvention of our regional economies must be informed by new learning and understanding of the transformation of the surging knowledge economy. Restarting our state economy depends on innovative regional economic development planning and strategies that will wisely allocate scarce resources and make smart investments in job-creating projects and sustainable economic growth.

From World War II until the final decades of the last century, Michigan and other industrial states in the U.S. enjoyed virtually unchallenged global leadership in its unparalleled manufacturing capacity and corresponding high income levels and quality of life that were the envy of the world. That position has been eroding. Michigan must now deeply commit itself to re-establishing its economic leadership based once again on ingenuity, innovation, and resilience that typified historic Michigan economic success stories like Ford, Olds, Chrysler, Dodge, Dow, and many others. These are the same qualities that made the state the Arsenal of Democracy in World War II. As that Arsenal, we equipped the Allied forces to defeat the threat of global totalitarianism.

This time we must equip ourselves to defeat the cancerous threat of a mind-set clinging to replicating economic models that clearly no longer produce the desired results. We must reject a catastrophic retreat to mind-sets that don't work. We must act boldly and pioneer innovative planning and economic development tools and methods to pursue advanced manufacturing in green technology and other knowledge-based sectors to create a truly sustainable and equitable economy. These indicators are intended to assist navigating those innovative paths.

Data Collection Methodology and Constraints

The collaborative team tackled significant methodological issues to identify useful and meaningful knowledge economy indicators: What are the characteristics of the knowledge economy in predominantly rural Northern Michigan? What are useful and accurate indicators in this context? What are meaningful measures in this context? What data sets are available to provide measurements of identified indicators? Are data sets readily accessible? Will those data sets be available in the future? What proxy measures are reasonable and acceptable when the desired primary data is inaccessible or absent? These are the major questions we addressed in deciding on which data sets to select as indicator measures.

A thorough Web-based literature review was conducted by the project team to identify knowledge economy indicators and measures that could be applied to Northern Michigan and Eastern Upper Peninsula regions. The project team also met with Kenneth Darga, the State Demographer, and Mark Reffitt, a state Department of Energy, Labor, and Economic Growth (DELEG) labor market data analyst, to identify and evaluate available data sets that could serve as useful and meaningful measures.

Five teleconference meetings were held with the regional planner partners to review and discuss the results of the team's literature review, data collection, and discussions with external experts. NAICS (North American Industrial Classification System) code categories were thoroughly examined by the project team and partners to identify those codes that provide accurate indicators of knowledge jobs. Regional planners also consulted with local business leaders and experts to identify NAICS industry sectors that were relevant to their regions.

Constraints and limitations on regional data sets were encountered that constrict regional planners' efforts to develop their knowledge economies. These constraints can interfere with successful planning because they make it difficult, if not impossible, to quantify the results of economic development strategies.

For some potential indicators, no accurate data has been collected by any agency, or the project team lacked the resources (or authority) to collect or generate the data. For other indicators, data was available at the aggregate state level but not at regional or county levels. On the other hand, in some cases where local area datasets were generated, specific data was either suppressed by the data collection agency to protect privacy or riddled with unacceptably high margins of error. Data suppression and high error rates both result from thin population densities. For example, publicly available data on the number of full-time engineers in the NWMCOG region has a 29% margin of error for males (328 +/- 96) and a 77% margin of error for females (75 +/- 58).

Practically speaking, selection of accurate measures was restricted to the availability of data sets as the project lacked the resources to generate significant new primary data. The project team determined that some data sources previously used for knowledge economy indicators no longer existed. For example, the cyber-state.org Web site that had supplied data for measuring digital government had vanished. As data sets on "workforce education" and "management and professional jobs" are no longer available, these indicators were eliminated. These data sets were previously generated by U.S. Census Bureau "Long Forms" that have been replaced by the American Community Survey (ACS). The ACS sampling technique precludes the generation of meaningful data from rural areas for the most part because the number of data points is far too small to rely on for accurate data.

We welcome your feedback

The project team welcomes feedback and suggestions for improving the indicators and measures described below. The rapid ongoing development and frequent disruptiveness of the knowledge economy creates a fluid and often difficult environment in which numerous questions can be raised about the meaningfulness of selected indicators and the accuracy of available measures. The project team could not answer all of these questions definitively and certainly cannot anticipate all of the future questions that will come up. We look forward to any feedback that can help create a better set of regional knowledge economy indicators to support the development of innovative regional economic strategies that help communities compete and succeed in the knowledge economy. Readers may visit our Web site (KnowledgePlanning.org) to provide direct feedback to our project team.