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Chapter 8. State Indicators


State Data Tool

Chapter Overview

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 58 indicators for individual states, the District of Columbia, and Puerto Rico.

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 ranked 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.

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 which case, data are given only for the years in which comparisons are appropriate. Most indicators contain data for 2008–09; some contain data for 2010.

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.

Types of Indicators

Fifty-eight indicators are included in this chapter and grouped into the following areas:

  • Elementary and secondary education
  • Higher education
  • Workforce
  • Financial R&D inputs
  • Research and development outputs
  • S&T in the economy

The first two areas address state educational attainment. 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 the expected level of competence on the standardized test.

Comparable state-level performance data are not available for high school students. Although performance and proficiency data in science are available for students in grade 12 at the national level but not at the state level, data on performance and proficiency in mathematics is not available at either the federal or state 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 the top-performing high school students. Other indicators in education focus on state spending, teacher salaries, student costs, and undergraduate and graduate degrees in S&E. Three indicators measure the level of education in the populations of individual states.

Workforce indicators focus on the level of S&E training in the employed labor force. These indicators reflect the higher education level of the labor force and the degree of specialization in S&E disciplines and occupations.

Financial indicators address 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. This section enables readers to compare the extent to which R&D is conducted by industrial, academic, or state agency performers.

The final two sections provide measures of outputs. The first focuses on the work products of the academic community. It includes 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.

The last section of output indicators examines the robustness of a region's S&T-related economic activity. These indicators include venture capital activity, Small Business Innovation Research awards, and high-technology business activity. Although data that adequately address both the quantity and quality of R&D results are difficult to find, these indicators offer a reasonable information base.

This edition includes six new indicators. Consistent with other indicators in the chapter, they are normalized. The first covers AP Calculus AB exams and is presented as a percentage of high school students scoring 3 or higher on the exam. The second covers the number of bachelor's degrees in science and engineering that were conferred relative to the size of the population in the appropriate age range. The third provides an indication of the degree to which a state's educational infrastructure provides the highest level of training in science and engineering and is presented as the number of doctorate degrees conferred in science and engineering as a percentage of all science and engineering degrees conferred. The fourth indicator covers state funds for higher education and is presented as the percentage of state gross domestic product. The fifth addresses the amount of state funding for public research universities per enrolled student. Finally, the last new indicator focuses on the percentage of technical workers in a state's workforce.

Data Sources and Considerations

Raw data for each indicator are presented in the tables. Each table provides an average value for all states, labeled "United States." For most indicators, the state average was calculated by summing the values for the 50 states and the District of Columbia for both the numerator and the denominator and then dividing the two. Any alternate approach is indicated in the notes at the bottom of the table.

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 sparsely populated 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.

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.

Key Elements for Indicators

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 below shows the outline of each state. The state is identified by its postal code. In 1978, Congress initiated the Experimental Program to Stimulate Competitive Research (EPSCoR) at the National Science Foundation to build R&D capacity in states that have historically been less competitive in receiving federal R&D funding. Subsequently, several federal agencies established similar programs, the largest of which is the Institutional Development Award (IdeA) program at the National Institutes of Health. States shown with a gray background in figure 8-A are states in the EPSCoR group. The EPSCoR group of states are those eligible for EPSCoR-like programs in at least five federal agencies or departments. The 24 EPSCoR states are Alabama, Alaska, Arkansas, Delaware, Hawaii, Idaho, Kansas, Kentucky, Louisiana, Maine, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Mexico, North Dakota, Oklahoma, Rhode Island, South Carolina, South Dakota, Vermont, West Virginia, and Wyoming. The EPSCoR Program is discussed further in chapter 5, "Academic Research and Development," in the sidebar "EPSCoR: The Experimental Program to Stimulate Competitive Research." The remaining 26 states are considered states in the non-EPSCoR group.

The second element is a state distribution chart illustrating state values for the latest data year for that indicator (figure 8-B). States are listed alphabetically by postal code and are centered over the mid-point of the range for their indicator values. Indicator values are presented along the x-axis of the chart. States stacked together have indicator values in the same range but not necessarily identical values. The reader is referred to the table for values of the indicators. All of the indicators are broad measures, and several rely on sample estimates that have a margin of error. Small differences in state values generally carry little useful information.

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. 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.

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 8-39, is calculated by summing the R&D of all the EPSCoR states, summing the gross domestic product of these states, and dividing to compute an average. States with more R&D and a larger gross domestic product affect this average more than smaller ones do, just as data on California affect U.S. totals more than data on Wyoming do.

The first and second 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 24 EPSCoR states and dividing by 24. All state ratios count equally in this computation. Examples of this calculation are shown in tables 8-5 and 8-18.

High-Technology Industries

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 2002 North American Industry Classification System (NAICS). The 2002 NAICS codes contain a number of additions and changes from the previous 1997 NAICS codes that were used to classify business establishments in data sets covering the period 1998–2002, and therefore cannot be applied to data sets from earlier years.

The list of high-technology industries used in this chapter includes the 46 four-digit codes from the 2002 NAICS listing shown in table 8-A.

Appendix Tables

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. Additional data on the coefficient of variation for data sources in the chapter also are presented in the appendix tables when they are available.


Hecker D. 2005. High-technology employment: A NAICS-based update. Monthly Labor Review 128(7):57–72.