To address the interest of the policy and research communities in the role of science and technology (S&T) in state and regional economic development, this chapter presents findings on state trends in S&T education, the employed workforce, finance, and research and development. This chapter includes 59 indicators for individual states, the District of Columbia, and Puerto Rico.
The indicators are designed to present information about various aspects of state S&T infrastructure. The data used to calculate the indicators were gathered from public and private sources. When possible, data covering a 10-year span are presented to assist in identifying trends. However, consistent data were not always available for the 10-year period; in these cases, data are given only for the years in which comparisons are appropriate. Most indicators contain data for 2010–11; some contain data for 2012.
Ready access to accurate and timely information is an important tool for formulating effective S&T policies at the state level. By studying the programs and performance of their peers, state policymakers may be able to better assess and enhance their own programs and performance. Corporations and other organizations considering investments at the state level may also benefit from this information. The tables are intended to provide quantitative data that may be relevant to technology-based economic development. More generally, the chapter aims to foster further consideration of the appropriate uses of state-level indicators.
The 59 indicators are divided into six categories.
Student achievement is expressed in terms of performance, which refers to the average state score on a standardized test, and proficiency, which is expressed as the percentage of students who have achieved at least an expected level of competence on the test.
State-level performance data are not available for high school students. Performance and proficiency data in mathematics are available for students in grade 12 at the national level but for students in fewer than one-quarter of the states at the state level. Performance and proficiency data in science are only available at the national level for students in grade 12. Instead, mastery of college-level material through performance on Advanced Placement Exams has been included as a measure of the skills being developed by top-performing high school students.
These indicators measure the higher education different states provide, the level of education in their populations, the cost of college attendance at the undergraduate level, and state expenditures to public universities.
Workforce indicators focus on the level of S&E training and occupations of the employed labor force. These indicators reflect the higher education level of the labor force and the extent of S&E employment.
Financial indicators present the sources and level of funding for R&D. They show how much R&D is being performed relative to the size of a state’s business base. The indicators also present the extent to which R&D is conducted by industrial and academic performers.
These indicators show the number of new doctorates conferred, the publication of academic articles, and patent activity from the academic community and from all sources in the state.
These indicators include venture capital activity, Small Business Innovation Research (SBIR) awards, and high-technology business activity.
Unlike other chapters in this volume, this chapter presents indicators individually. Indicators are normalized to enable comparisons among states of different sizes, but indicators are presented discretely rather than in a continuous text that describes the relationships among them. Because these indicators span a broad range of topics across the entire S&E landscape—inputs and outputs, people and dollars, businesses and universities, R&D and education—a validated model synthesizing interrelationships among these specific indicators does not exist. Moreover, states are both heterogeneous, with hubs of intense S&E activity alongside areas without substantial S&E infrastructure, and porous, with limited control over movements of people and funds across their borders. As a result, smaller regions, which form more tightly coupled economic systems, and nations, which create stronger barriers to movement, are often considered to be better units of analysis for studying geographic variation in S&E activity.
Nonetheless, state governments and other state-based actors have significant leverage and can affect S&T-related economic development in their states and regions. The data in this chapter offer ample opportunities for exploratory analysis of variations among states and the interplay of education, R&D, and economic activity. The online state data tool (http://www.nsf.gov/statistics/seind14/chapter-8/interactive)—which includes the state data in this chapter plus, when available, additional data on state S&T over the past 20 years—enables readers to examine the relationships among the different indicators in the chapter.
Some examples of possible issues that could be explored with the current set of indicators include the following:
The data in this chapter cannot be expected to provide definitive answers to any of these questions. Additional data, well-defined theoretical models, and more refined geographical comparisons will be required as social scientists grapple with these complex relationships. But exploring relationships in the existing data via the online state data tool can stimulate policymakers and other stakeholders to think more broadly and deeply about the possible implications of strategies used to address state-level S&E policy topics.
The tool offers users the following capabilities:
The tables present estimates for the components that make up each indicator. Each table provides an average value for all states, labeled “United States.” For census-based data, the national average is the sum of numerator values for the 50 states and the District of Columbia divided by the sum of the denominator values. For sample-based data, the national totals were estimated directly, and the national average is the ratio of the estimated totals.
The values for most indicators are expressed as ratios or percentages to facilitate comparison between states that differ substantially in size. For example, an indicator of higher education achievement is not defined as the absolute number of degrees conferred in a state because less populous states are unlikely to have or need as extensive a higher education system as states with larger populations. Instead, the indicator is defined as the number of degrees per number of residents in the college-age cohort, which measures the intensity of educational services relative to the size of the resident population.
Although data for Puerto Rico are reported whenever available, they frequently were collected by a different source, making it unclear whether the methodology used for data collection and analysis is comparable with that used for the states. For this reason, Puerto Rico was not listed with the states, not assigned a quartile value, and not displayed on the maps. Data for United States territories and protectorates—such as American Samoa, Guam, Northern Mariana Islands, and Virgin Islands—were available only on a sporadic basis and thus are not included.
Readers must exercise caution when evaluating the indicator values for the District of Columbia. Frequently, the indicator value for the District of Columbia is appreciably different from the indicator values for any of the states. The District of Columbia is unique because it is an urban region with a large federal presence and many universities. In addition, it has a large student population and provides employment for many individuals who live in neighboring states. Indicator values can be quite different depending on whether data attributed to the District of Columbia are based on where people live or where they work.
Six key elements are provided for each indicator. The first element is a map color coded to show in which quartile each state placed on that indicator for the latest year that data were available. This helps the reader quickly grasp geographic patterns. On the indicator maps, the darkest color indicates states that rank in the first or highest quartile, and white indicates states that rank in the fourth or lowest quartile. Cross-hatching indicates states for which no data are available.
The sample map (figure
The second element is a state distribution chart below the map, illustrating state values for the latest data year for that indicator (figure
The third element, at the bottom of the map box, is a short citation for the data source. The full citation appears under the table on the facing page.
The fourth element, in a shaded box on the lower left side of the page, is a summary of findings that includes the national average and comments on national and state trends and patterns for the particular indicator. Only statistically significant findings are presented; adjustments in the testing to account for multiple comparisons have been made, when appropriate. Although most of the findings are directly related to the data, some represent interpretations that are meant to stimulate further investigation and discussion.
The fifth element, on the lower right side of the page, is a description of the indicator and includes information pertaining to the underlying data.
The final element is the data table, which appears on the facing page. Up to 3 years of data and the calculated values of the indicator are presented for each state, the District of Columbia, and Puerto Rico. Puerto Rico is included in the data table only when data are available.
For selected indicators, the data table has been expanded to include the average data and indicator value for the 50 states and the District of Columbia, and the averages for the EPSCoR and non-EPSCoR states. These averages have been calculated in two ways. The first two lines, “EPSCoR states” and “Non-EPSCoR states,” treat each group as a single geographical unit, ignoring the division of that unit into separate states. The ratio for the group is calculated by totaling the numerator value of each of the states in the group and the denominator value of each of the states in the group and dividing to compute an average. For example, the EPSCoR states’ average of R&D by gross domestic product by state, shown in table
The third and fourth lines, “Average EPSCoR state value” and “Average non-EPSCoR state value,” represent the average of the individual state ratios for an indicator. The average EPSCoR state value for R&D by gross domestic product by state is calculated by summing the ratios for the 22 EPSCoR states and dividing by 22. All state ratios count equally in this computation. Examples of this calculation are shown in tables
To define high-technology industries, this chapter uses a modification of the approach employed by the Bureau of Labor Statistics (BLS) (Hecker 2005). BLS’s approach is based on the intensity of high-technology employment within an industry.
High-technology occupations include scientific, engineering, and technician occupations. These occupations employ workers who possess an in-depth knowledge of the theories and principles of science, engineering, and mathematics, which is generally acquired through postsecondary education in some field of technology. An industry is considered a high-technology industry if employment in technology-oriented occupations accounts for a proportion of that industry’s total employment that is at least twice the 4.9% average for all industries (i.e., 9.8% or higher).
In this chapter, the category “high-technology industries” refers only to private-sector businesses. In contrast, BLS includes the “Federal Government, excluding Postal Service” in its listing of high-technology industries.
Each industry is defined by a four-digit code that is based on the listings in the North American Industry Classification System (NAICS). The NAICS codes change over time, thereby affecting the trend data presented in the tables. For data years up through 2008, the 2002 NAICS codes were used to define business establishments. Subsequent data years reflect the use of the 2007 NAICS codes. The list of high-technology industries used in this chapter includes the four-digit codes from the 2002 and 2007 NAICS listings shown in table
Additional data tables pertaining to the indicators in this chapter have been included in the appendix. These tables provide supplemental information to assist the reader in evaluating the data used in an indicator. The appendix tables contain state-level data on the performance of students in different racial/ethnic and gender groups on the National Assessment of Educational Progress evaluations.
Hecker D. 2005. High-technology employment: A NAICS-based update. Monthly Labor Review 128(7):57–72.
