Understanding Economic Change in Your Community

Using employment data to better understand your local economy.
Understanding Economic Change in Your Community - Articles

Updated: October 4, 2017

In This Article
Understanding Economic Change in Your Community

Read First

The tools offered in this article will help you answer these questions about your economy:

  • What are the current employment conditions in our community?
  • What parts of the local economy have been growing?
  • Which industries have been declining?
  • How does the local economy compare to other nearby economies?
  • What are the factors leading to local employment growth?
  • What are the factors leading to local employment decline?

In this series, we offer tools to help you address the important questions above.

Overview

In order to craft effective economic development strategies, understanding the current state of the local economy--including its relative strengths and weaknesses--is essential. To learn more about your local economy, we encourage you to conduct a detailed study of its current and historical performance.

The tools will help you get started in examining important economic trends in your community. The methods are flexible and can be used on a variety of economic indicators, including employment and unemployment, income, poverty rates, and housing. The tools can use secondary data, readily available from sources such as the Census Bureau, the Bureau of Economic Analysis, and the Bureau of Labor Statistics. The analysis should help a variety of local efforts, including industry development, grant writing, and visioning.

A Few Things to Keep in Mind When Carrying Out the Analysis

In practice a number of indicators illuminate our understanding of the local economy, ranging from simple descriptive statistics to in-depth surveys and analysis. Regardless of the methods and measures you choose, following important guidelines can help you make the most of your efforts. We urge you to keep the following points in mind when using the tools described on the following pages.

  1. No "single number" represents the local economy. When thoroughly assessing the local economy, you need to use several measures, as reliance on a single measure provides only limited insight. For example, a high job growth rate may show the community is generating new employment opportunities; but you should be interested in the wages provided by new jobs as well, in order to get a better understanding of the local labor market's "true performance."
  2. Make comparisons among communities. No community exists in isolation. In order to better understand local economic performance, you should compare your community to "similar" communities, the state, and the nation.
  3. Examine changes over time. While a "snapshot" analysis provides a good indication of where the community is today, knowing long-term trends in the community is important. With respect to employment, industries that have traditionally been important may now be declining. Trend analysis can be particularly informative as it often provides good predictions about the near future.
  4. A "reality check" is essential. While using the methods here to analyze "hard data" is a starting point, tapping the insights of local citizens and policy makers is also important. If the data analysis and the insights are at odds, a more in-depth examination may be necessary.
  5. The analyst, not the data, should tell the story. Always remember, economic data is boring, and no reader wants to pore over reams of tables and charts. The information can be extremely helpful if used to complement a coherent, consistent, and truthful story about the economy. The information is most useful when summarized with themes, interpretations, and conclusions.

Getting Started

The first step is to define the local economy. While in reality the mobility of people and money means any economy is not really place-specific. Defining some region as the local economy is necessary. For practical reasons (such as data availability) the economy is often defined according to political jurisdictions, such as municipalities, counties, or states. While practitioners usually want to define the economy as locally as possible, keep in mind that better data becomes more available as we move up the political hierarchy. Generally, comprehensive data at the sub-county level is available only every ten years from the census. Conversely, state, national, and even some county data are often updated monthly.

Once you have defined your economy, you can use these tools to size up your economy--keeping in mind the principles outlined in the previous paragraphs.

Some Basic Local Economic Indicators

Unemployment Rate

The Bureau of Labor Statistics provides monthly estimates of unemployment rates for states, metropolitan areas, and counties. The unemployment rate is easy to calculate: divide the number of people who are jobless and available for work by the labor force. The basic concepts involved in identifying the employed and unemployed are quite simple:

  • People with jobs are employed.
  • People who are jobless, looking for jobs, and available for work are unemployed.
  • People who are neither employed nor unemployed are not in the labor force.

Earnings

Earnings of employees are the sum of wages and salaries, other labor income (for example, benefits), and proprietors' income. As with employment, data are from the Bureau of Economic Analysis. Also, like employment data, earnings data are by place of work, so that earnings of an employee who works in one county but resides in another are counted in the county where the job is. Earnings per worker are simply the total earnings in an industry divided by total number of employees.

Population

Historical population data is available from the U.S. Department of Commerce, Bureau of the Census. When analyzing local economies population growth trends are often used as a crude measure of relative economic performance. Typically areas with rapidly growing populations also have strong job growth.

Per Capita Personal Income

Personal income is the income people receive from all sources--that is, from working, transfer payments, and interest and investments. Per capita income is the total personal income of the residents of a given area divided by the resident population of the area. Per capita personal income is often used as an indicator of the quality of consumer markets and of the economic well-being of the residents of an area. The Census Bureau provides per capita income county data every 10 years for every municipality. Annual state- and county-level estimates are available from the Bureau of Economic Analysis.

Building Permits

These data summarize the number of new housing units authorized by building permits. The data relate to units intended for occupancy on a housekeeping basis. They exclude mobile home units. Building permit data is useful to understand the growth in residential development in a community.

Commuting to Work

These data detail the number of workers that commute to a county for work and the commuting patterns of workers with residences in a county.

Poverty Rate

The poverty rate is an estimate of the percentage of the county population that lives below the poverty threshold, as established by the federal government. In providing these estimates, the U.S. Census Bureau uses a set of money income thresholds that vary by family size and composition to detect who is poor. If a family's total income is less than that family's threshold, then that family--and every individual in it--is considered poor. As an example, the poverty threshold in 2014 for a family of four (two adults and two children) was $24,008.

The poverty thresholds do not vary geographically, but they are updated annually for inflation using the consumer price index. The official poverty definition counts money income before taxes and does not include capital gains and non-cash benefits (such as public housing, Medicaid, and food stamps).

How is this Information Used

Information gleaned from these tools can be helpful in several aspects of community development.

For economic development interests, this information can enhance understanding of employment trends, not only as specific to the community, but also in context of other regions, the state, or the nation. By identifying growing industries, as well as those that are declining, local development groups can concentrate their efforts on industries where the chances of success are greatest.

When applying for community development grants, documenting current and historical conditions is important. For non-government organizations, using the tools yields information that can strengthen grant-writing efforts.

Regardless of how you use the data, you must present it effectively! While information generated by the data analysis tools is an important aspect of understanding the local economy, the fact is that tables and charts are almost always boring. In order to be effective, you must use the data to tell a story--don't expect it to be the story.

For More Information

In addition to the links provided, one of the best Web sites for information on regional economic analysis is EconData.Net. On this site, you can find links to more than 1,000 online regional data collections, including the "Ten Best Sites." You will also find an excellent guide Socioeconomic Data for Understanding Your Regional Economy, which provides some simple analytical tools as well as an overview of many data providers and data sets.

Regional Rural Development Centers and data depositories include:

The Penn State Data Center provides extensive economic data. Their mission is to provide easy and efficient access to U.S. Census Bureau data and information through a wide network of lead, coordinating, and affiliate agencies in each state.

PA Workstats provides extensive statistics on Pennsylvania's workforce.

These materials are an update of those originally produced by Martin Shields, Professor of Economics at Colorado State University.

A snapshot provides a basic overview of important economic indicators. Usually, these snapshots are presented as a series of tables and charts.

Overview: Describing the Current State of the Local Economy

At the local level, the first question to ask is "What is the state of our economy?" A variety of indicators can inform this question. Some common ones are industry employment, unemployment, wages, population, and housing starts. (We provided a more thorough discussion of these and other indicators--with appropriate Web links--in the "Some Basic Indicators" section of the introduction; this section emphasizes general methods.) For example, you might develop a current employment picture that shows the breakdown of local employment by industry. Alternatively, you might develop a social indicators picture that shows the local poverty rate, health care availability, and so on. Table 1 is an example.

Note several features of Table 1 that follow the principles laid out in the introduction. First, the table looks at two geographic areas: the county, which is the area of interest, and the state, which is a comparison area. Second, it looks at various measures of economic health, including employment, unemployment, poverty, and income. Finally, it makes a comparison over time (population growth).

Table 1. Key County Social and Economic Indicators for Cumberland County
VariableCountyPennsylvania
Population (2014)243,76212,787,209
Population growth
Total Change8,35484,325
Percent Change (April 2010 - July 2014)3.5%0.7%
Percent of population at least 65 years old (2013)16.6%16.4%
Per capita income (2013 dollars) (2009-2013)$31,791$28,502
Poverty rate (2009-2013)8.3%13.3%
Percent of population aged 25 or older with at least a high school degree (2013)91.1%88.7%
Percent of population aged 25 or older with at least a college degree (2013)32.5%27.5%
Unemployment rate (Sept 2014)4.7%5.8%
Total Employment (2012)158,9657,294,338
Agriculture, Forestry, and Related Services1,92396,718
Mining13352,066
Utilities9523,183
Construction6,618358,198
Manufacturing8,841593,957
Transportation and warehousing13,807266,329
Wholesale Trade4,540250,277
Retail Trade18,433773,601
Finance, Insurance, and Real Estate16,407655,089
Professional, scientific, and technical services10,199476,255
Federal Government (excluding Military)4,914100,423
State Government4,940192,667
Local Government7,644458,737

Sources:census.gov, bea.gov, and paworkstats.pa.gov

Regardless of your choice of indicator, this data can be entered and used in a spreadsheet and presented in either tabular form or as a pie chart. For example, tables are helpful way to provide actual values of employment. This helps enhance understanding about the relative size of the economy. Pie charts are useful for examining the relative levels of economic dependence on certain sectors within the economy. Local pie charts can also be compared to state pie charts to examine relative dependence across economies. Table 2 is an example.

Table 2. Cumberland County Employment by Industry (2012)
Industry% of Employment
Retail Trade23%
Finance, Insurance, and Real Estate20%
Transportation and Warehousing17%
Professional, Scientific, and Technical Services13%
Manufacturing11%
Construction8%
Wholesale Trade6%
Agriculture, Forestry, and Related Services2%
Mining0%
Utilities0%

How This Information Is Used in Economic and Community Development

After developing this snapshot, you should spend some time interpreting the data. Continuing with the employment example, you might want to consider several questions.

  1. What is the major industry in your community? How is this industry affected by change at the state or national level?
  2. Compared to other regions, does the community seem highly dependent on any particular industry? How might this dependence be problematic? Or, is this dependence a strength?
  3. Does this information support popular perceptions? For example, many rural communities are surprised to find out that agriculture is a relatively small sector when compared to the service industry.

With this basic understanding of current economic conditions, you are in a better position to identify not only the strengths and weaknesses of the local economy, but also the needs and potential opportunities for development.

For More Information

Basic demographic profile data for Pennsylvania counties is available on the Center For Economic and Community Development website.

The state's Center for Workforce Investment Analysis at the Department of Labor and Industry is Pennsylvania's designated provider of employment statistics. On their website you can find a variety of statistics related to employment, wages, and unemployment for the state, metropolitan areas, and counties.

Other potential data sources are outlined in the section on indicators in the introduction.

These materials are an update of those originally produced by Martin Shields, Professor of Economics at Colorado State University.

Tracking the performance of key economic indicators over time can help you identify growing and declining sectors. You can use trend analysis to identify new opportunities.

Overview: Tracking Economic Trends

While a snapshot is a good way to capture today's economy, understanding how the local economy has changed over time is important for several reasons. First, trend analysis allows you to examine long-term performance-- identifying indicators that have shown strength over time and those that are declining. Using trend analysis to identify important trends will help you get started in implementing actions that further develop areas of strength, or address new or long-standing problems.

Second, trend analysis helps communities identify "shocks" to important local indicators (such as a sudden upsurge in unemployment). If the data shows some quick, dramatic change, you'll probably want to know why this happened (for example, perhaps a local factory moved overseas). Recognizing the economic factors that influence local industries can strengthen the local ability to prevent shocks, or at least quickly respond to them.

Third, trend analysis can either raise or alleviate concerns. For example, a high local unemployment rate may be a historic local problem, suggesting that job creation is imperative. Alternatively, an increase in unemployment may just mirror national business cycle trends, and better times may be inevitable.

A final useful application of trend analysis is in identifying growth opportunities. Perhaps some economic sectors have recently increased in importance. Once again, you'll want to ask "why?" It may be that your community is especially well suited for some particular "new industry." In this case, local economic development efforts might focus on developing a niche.

Charts

One useful way to analyze data over time is to examine trends relative to some baseline. With a bar chart you can look at how an indicator has changed between two time periods (see Figure 1 for an example). These charts are especially useful for data available only periodically, such as census data.


Figure 1. Cumberland County Industry Growth (2003-2012)

The Index of Growth is another tool that provides a cumulative measure of change over time and is an especially useful way to investigate local economic behavior relative to other economies (such as the state or nation). Figure 2 is an example of an index of growth.


Figure 2. Employment Growth

Growth Index (1990=100)

The index is based on local economic performance relative to some base year (here 1990), and is computed according to the following formula (with subscripts identifying region (r) and year (t)):

Indexr,t = (Yr,t/Yr,1990) x 100

Y = Economic variable (employment, population, etc.)
r = Region
t =Year
1990 = Base Year (1990)

This index compares the level of a particular economic variable to its level at the beginning of the period. The index for the base year is always 100. For example, if total employment is 5,000 in 1990 and 6,000 in 2010, then the value of the index in 1990 is (6,000 / 5,000) x 100 = 120. In this example, employment for the region increased by 20 percent (120 - 100). You can calculate the index for any number of years and plot the resulting values in a graph (Figure 2.).

Using this measure of economic performance has three advantages. First, placing all regional data on an index basis allows a direct comparison between regions. Second, changes in the value of the index from one year to the next can be interpreted as a growth rate. Here fast growth and slow growth can be identified. Finally, by examining the index over a period of time, you can establish the relative stability of the local economy.

How This Information Is Used in Economic and Community Development

Once again, you should spend some time interpreting the data. When looking at employment trends, for example, consider the following questions:

  1. Which local industry has shown the greatest growth? How has this growth compared to that of the state? The nation? What do you think has caused this growth?
  2. Have there been any surprises, such as a sector that has grown or declined faster than anticipated? Why do you think this is so?
  3. Are there any aspects of local change that are similar to the state or the United States? Are there any that are different? Why do you think there are differences?
  4. Does this information support popular perceptions? For example, in many communities services have grown to be the largest share of local employment.

Looking at a variety of indicators is also useful. For example, after identifying a "fast-growing" industry, you might want to also look at wages in that industry--the jobs may not be very high paying, and the growth may not be as great of a local boon as it appeared.

In general, recent trends tend to maintain themselves, at least in the short term. By understanding historical growth patterns, you are in a better position to identify not only the strengths and weaknesses of the local economy, but also the needs and potential opportunities for development.

For More Information

Some basic profile data for Pennsylvania is available at the Center for Economic and Community Development Website.

The state's Center for Work-force Investment Analysis at the Department of Labor and Industry is Pennsylvania's designated provider of employment statistics. On their web site you can find a variety of statistics related to employment, wages, and unemployment for the state, metropolitan areas, and counties.

Other potential data sources are outlined in the section on indicators in the introduction.

These materials are an update of those originally produced by Martin Shields, Professor of Economics at Colorado State University

The location quotient helps you identify those local industries that are producing more than is needed for local use and selling outside the region (exporting) and those that are not meeting local needs and are a source of consumption leakage (importing).

Overview: Location Quotients Help Identify Exporting Industries

The first two tools discussed are useful for understanding the current local economic picture and historical economic trends. While this information is helpful in terms of knowing "where we are" and "how we got here," how this information can be used in a proactive way is not as clear. The location quotient is a simple tool that relies on much of the same employment data as the first two tools--snapshot and trend analysis--but it provides a different insight into understanding particular local economic strengths as well as identifying development prospects.

In a nutshell, the location quotient helps you identify exporting and importing industries. This is important in terms of understanding the extent to which community needs are being met (or not) by local businesses (importing). Often, the location quotient reinforces what you already know about your local economy; but just as often, it uncovers things you did not know or, at least, changes your perceptions. The real strength of the tool is that it is a simple, yet effective educational resource.

Calculating a location quotient is a straightforward process, and, in practice, most often uses employment data that is widely available. (Hint: This analysis is most informative when using as disaggregated employment data as you can find for your region [NAICS three- or four-digit]; for an overview of NAICS data, see the section "Read Me First.") The basic formula for the location quotient is:

LQ = % of Local Employment in Industry i / % of National Employment in Industry i

OR

LQ = (Local Employment in Industry i / Total Local Employment) / (National Employment in Industry i / Total National Employment)

Simply put, the location quotient identifies how local industries stack up with national averages.

In practice, location quotients are often used to identify importing and exporting industries. An exporting industry is one where the industry not only meets the local demand for its products, but also produces enough so it can sell outside of the region. An importing industry is one where local production levels are insufficient to meet local demand.

When interpreting the data, a location quotient greater than 1.0 indicates that the economy is self-sufficient, and may even be exporting the good or service of that particular industry. (As a rule of thumb, a location quotient greater than 1.25 almost certainly identifies exporting industries.) On the other hand, a location quotient less than 1.0 suggests that the region tends to import the good or service. (The applicable rule of thumb is that a location quotient less than 0.75 indicates an importing industry.)

Location quotients for Pennsylvania and Cumberland County are shown in Table 1 for 2013. The following example uses the above formula and data from Table 1 to illustrate how a location (LQ) quotient is calculated for agriculture and related services for Cumberland County (pertinent data is highlighted in bold).

LQ = (221/109,440) / (1,210,474/112,958,334) = 0.19

Using the rule of thumb for exporting industries, we see that agriculture and related services is an importing industry in Cumberland County. People familiar with the county may be surprised, as there are many farms in the county. However, other aspects of the analysis may be surprising. For example, the high location quotient for Finance and insurance suggests that the sector is an important source of local economic exports. Analysis of more disaggregated data (such as the three-digit or four-digit NAICS level) will help identify the specific industries that generate the export employment.

Table 1. 2013 Location Quotients
IndustryU.S. EmploymentPA EmploymentCumberland County EmploymentPA Location QuotientCumberland County Location Quotient
Total Employment112,958,3344,905,312109,4401.001.00
Agriculture and related services1,210,47423,3902210.440.19
Mining813,25835,184831.000.11
Utilities547,80721,992710.920.13
Construction5,819,950225,5624,0240.890.71
Manufacturing11,994,922563,5378,0591.080.69
Wholesale trade5,739,082225,5693,6530.910.66
Retail trade15,073,504632,34715,4560.971.06
Professional and technical services8,122,350324,5317,2890.920.93
Finance and insurance5,625,736253,2028,5771.041.57
Real estate and rental and leasing1,991,18658,9441,3590.680.70

How This Information Is Used in Economic and Community Development

Once again, you should spend some time interpreting the data. When looking at location quotients consider the following questions:

  1. What is the major exporting industry in your community?
  2. Compared to other regions, does the community seem highly dependent on any particular industry? How might this dependence be problematic? Or, is this dependence a strength?
  3. Are there any obvious relationships between industries with high location quotients and other sectors of the local economy? For example, an exporting industry might be highly dependent on other local businesses for important inputs.
  4. Does this information support popular perceptions? Or, does the analysis uncover surprising areas of economic strength?
  5. Does the analysis reveal any potential opportunities to substitute local production for imports?

After looking at location quotients and thinking about the questions above, you should be well positioned to identify local strengths and opportunities. For example, the presence of an exporting industry often indicates a local competitive advantage.

Using Location Quotients in Industry Cluster Analysis

Any exporting industry might be a strong candidate for further development and can serve as the core of an industry cluster. By definition, an industry cluster consists of a group of local industries that are closely linked by local supply networks, local customer networks, common labor markets, and access to technical expertise. Focusing on industry clusters in which your region enjoys a competitive advantage can help you understand the strengths and challenges of the local economy and better focus on factors that may foster continued growth for the region.

Used in conjunction with local expertise the location quotient can help identify industry clusters. After identifying important industries, focus groups and interviews with industry experts can provide a good complement to your analysis. Such qualitative approaches can help interpret aspects of the quantitative research, develop a better picture of the relationships among local industries, and identify similar workforce or infrastructure needs.

A Few Caveats

While location quotients can help you better understand your local economy, you should not rely solely on them for decision-making purposes. Users should keep the following in mind:

  • The location quotient assumes that local productivity (output per worker) is the same as national productivity. One interpretation of a "high" location quotient might be that a particular industry is exporting. An alternative interpretation is that the industry requires more workers than average to produce a level of output necessary to meet local needs. In other words, the local industry or workforce is inefficient. If the latter is true, the industry may be relatively weak rather than relatively strong.
  • The level of data aggregation matters. Location quotients can vary significantly depending on the level of industry aggregation (see the bulletin discussing data, particularly NAICS). For example, consider manufacturing. A community may have a location quotient less than 1.0 for the sector as a whole. But, a particular business, such as a paper mill, may definitely be a local strength. Only by looking at two-, three-, or even four-digit levels of disaggregation, where paper mills are identified independently of other manufacturing categories, would this strength be revealed. (While the three- or four-digit level is preferred for calculating location quotients, it is not always feasible, as employment data for most communities is only available at the two-digit level.)

Source: Bureau of Labor Statistics

For More Information

A Location Quotient Calculator and the data needed to calculate location quotients is available from the Bureau of Labor Statistics at the Department of Commerce. For Pennsylvania, much of the data and analyses used in this series is available online via Penn State's Center for Economic and Community Development. In addition, this Web site also provides educational materials and analyses for better understanding trends in the state economy.

The state's Center for Workforce Investment Analysis at the Department of Labor and Industry is Pennsylvania's designated provider of employment statistics. On their Web site you can find a variety of statistics related to employment, wages, and unemployment for the state, metropolitan areas, and counties.

Other potential data sources are outlined in the section on indicators in the introduction.

These materials are an update of those originally produced by Martin Shields, Professor of Economics at Colorado State University.

Shift-share analysis is a useful tool for overcoming the challenge of separating the role of local and national effects on current regional employment trends.

Local economic growth has a number of causes. In many cases, local businesses enjoy a competitive advantage, and growth within that industry spurs growth in the entire economy. In other cases, local industry growth simply mirrors national trends. Separating the role of local and national effects on current regional employment trends has long bedeviled many economic and community development practitioners. Shift-share analysis is a useful tool for overcoming this challenge.

Shift-Share Analysis can help you uncover answers to the following questions:

  1. How many jobs would be lost or gained if the total employment in your economy had changed at the same rate as the overall national job growth?
  2. How does the national growth or decline of particular industries translate into local industry growth or decline?
  3. What unique local factors relate to industrial growth or decline in your economy?

Overview: Shift-Share Examines the Engines of Growth

Generally, local employment changes are more or less concentrated in certain industries than they are in the nation as a whole. Most often, this difference is rooted in the region's industrial structure. For example, an area with several rapidly growing industries might display a high rate of overall employment gain. Likewise, a region with several declining industries might experience significant job losses. In examining the regional labor market merely knowing that employment changes have occurred is not sufficient.

The ability to separate local growth factors from national growth factors is an important aspect of understanding your local economy. By identifying industries that your region is particularly competitive in, you are in a position to focus economic development efforts on areas most likely to be successful. Shift-share analysis is used to account for the competitiveness of a region's industries and to analyze the local economic base. The analysis is primarily used to decompose employment changes within an economy over a specific period of time into three contributing factors:

  1. Growth that is attributable to growth of the national economy.
  2. Growth that is attributable to the mix of faster or slower than average growing industries.
  3. Growth that is attributable to the competitive nature of the local industries.

The technique facilitates comparisons between the local economy of interest and the larger economy. Specifically, shift-share helps analyze whether a particular local economy has witnessed a faster or slower growth rate in employment than the larger (national or state) economy has observed. Shift-share also helps explain these differences to some extent. For example:

  • Are observed differences in growth rates due to differences in employment mix found at the local level relative to that observed in the larger economy?
  • Or are differences due to the competitive advantage or disadvantage that the specific local economy has relative to the larger economy?

Components of Regional Industry Employment Change

As stated above, the shift-share analysis decomposes local industry employment change into three components:

  • The national growth share refers to local job growth that is attributed to national economic growth. Specifically, if the nation is experiencing employment growth, it is reasonable to expect that this growth will positively influence your area. This component describes the change that would be expected due to the fact that the local area is part of a dynamic national economy. In the first part of a shift-share analysis we examine the national growth share, or the number of jobs lost or gained in a region if total employment in the region had changed at the same rate as overall national employment.
  • Some industries add jobs more rapidly than others; some lose jobs. The industrial mix share component reflects differences in industry "mix" between the local and national levels. The mix-factor examines how national growth or decline of a particular industry translates into local growth or decline of that industry. Thus, this component represents the effects that specific industry trends at the national level have had on the change in the number of jobs in the region.
  • Even during periods of prosperity, growth is uneven--some regions and some industries grow faster than others. This is usually attributed to some local comparative advantage such as natural resources, linked industries, or favorable local labor situations. The local share describes the extent to which unique local factors relate to regional industrial employment growth or decline. The local component aids in identifying a local area's economic strengths and represents how a region's competitive position can contribute to regional job growth.

The key question is: What can we learn about the performance of a local economy by understanding these three components?

Analytical Method

This section describes how to calculate each of the three growth components described above. The example using data from Table 1 below, will help clarify the concepts.

To conduct the three-step analysis you need the following data for at least two points in time:

  • Local industry employment data.
  • National industry employment data.

In general, the rule of thumb is to use data from the most recently available year and compare it to 5 years earlier. Note, however, that the results will change--sometimes dramatically-- based simply on the choice of years! Appropriate county-level data sources include the Census Bureau's County Business Patterns and BEA-REIS. These sources are described more fully in the introduction to the series.

National Growth Share

This component measures the number of jobs created locally due to national economic trends. To calculate this component, you simply multiply the base year employment (2008 in our example) for each industry by the national average employment growth rate over the time period (2008 to 2013 in our example). See Table 2 for example National Growth Share calculations. Adding these results up over each industry yields the national growth component for the entire local economy.

NGS = industry employment x national average growth rate of total employment

Note: To calculate the appropriate growth rate, use the following formula:

Growth = (employment in 2013 - employment in 2008) / employment in 2008

Interpretation: The overall national growth component shows that, if the local economy was identical to the national economy, then the number of jobs in the county should have grown by 2,374 between 2008 and 2013. However, the data from Table 1 shows that the county only added 1,792 jobs during this period. This suggests that the county is not performing as well as the national average. The other components of the shift-share analysis can help identify why this happened.

Looking a bit closer at the analysis, we do see that Farm employment, Educational services and Finance industries added more jobs than expected if they performed at the national average (for example, 372 actual jobs versus 242 predicted jobs for finance and real estate employment).

The Manufacturing and Retail trade industries added less jobs than expected if they had performed at the national averages.

Obviously, the changes (gains or losses) in employment that occur at the local level do not exactly follow the overall national trend. Why might this be the case? Two reasons are described below.

Table 1.1. BEA-NAICS Employment Data for the United States: 2008 and 2013.
Industry20082013Change in JobsPercent Change
Total employment179,645,900182,278,2002,632,3001.47%
Farm employment2,634,0002,629,000-5,000-0.19%
Manufacturing13,980,30012,747,100-1,233,200-8.82%
Retail trade18,609,90018,371,300-238,600-1.28%
Finance and insurance9,115,2009,873,900758,7008.32%
Educational services3,844,3004,221,300377,0009.81%
All Other Employment131,462,200134,435,6002,973,4002.26%
Table 1.2. BEA-NAICS Employment Data for Cumberland County: 2008 and 2013.
Industry20082013Change in JobsPercent Change
Total employment158,280160,0721,7921.13%
Farm employment1,6771,714372.21%
Manufacturing10,4848,599-1,885-17.98%
Retail trade19,42918,369-1,060-5.46%
Finance and insurance11,43311,8804473.91%
Educational services4,6265,15853211.50%
All Other Employment110,631114,3523,7213.36%
Table 2. National Growth Share Calculations
Industry2008 County EmploymentNational Employment Growth RateNational Growth Share
Farm employment1,677x1.5%=25
Manufacturing10,484x1.5%=157
Retail trade19,429x1.5%=291
Finance and insurance11,433x1.5%=171
Educational services4,626x1.5%=69
All Other Employment110,631x1.5%=1,659
County National Growth Share2,372

Industry Mix Share

Some industries add jobs more rapidly than others and some lose jobs. The "mix" component helps you determine if the local industry is weighted toward industries that are growing faster or slower than the national average. To calculate this component, simply multiply the base year local employment in each industry (here 2008) by the difference between the sector's national growth rate and the national economy's overall growth rate (see Table 3). Adding these results up over each industry yields the industrial growth component for the entire local economy.

To calculate Industry Mix Share use the following formula:

IMS = local industry employment X (national industry growth rate - national average growth rate)

Industrial Mix Share calculations can be found in Table 3.

Table 3. Industrial Mix Share Calculations
Industry2008 County EmploymentIndustry's National Growth RateNational Employment Growth RateIndustry Mix Share
Farm employment1,677x(-0.19%-1.5%)=-28
Manufacturing10,484x(-8.82%-1.5%)=-1,082
Retail trade19,429x(-1.28%-1.5%)=-541
Finance and insurance11,433x(8.32%-1.5%)=780
Educational services4,626x(9.81%-1.5%)=384
All Other Employment110,631x(2.26%-1.5%)=843
County Industrial Mix Share356

Interpretation: The overall industrial growth component of 356 means that the region in this example has nearly 356 more jobs than it would have if its structure were identical to the nation. The Finance and insurance, Educational, are growing faster than the national average, while the Farm employment, Manufacturing and Retail trade sectors are growing slower. The positive industrial mix means that the local economy grew faster than the national average, independent of the national influence.

Local Share

This component helps you determine whether local industries are growing faster or slower than similar industries at the national level. Accordingly, the local share is often interpreted as indicating whether local businesses are more or less competitive than the national average. To calculate the local share, you simply need to multiply employment in the base year (here 1993) by the difference between the local and national industry growth rates (see Table 4). Adding these results up over each industry yields the competitive growth component for the entire local economy.

To calculate Local Share use the following formula:

LS = local industry employment x (local industry growth rate - national industry growth rate)

Table 4. Local Share Calculations
Industry2008 County EmploymentIndustry's County Growth RateNational Industry Growth RateLocal Share
Farm employment1,677x(2.21%--0.19%)=40
Manufacturing10,484x(-17.98%--8.82%)=-960
Retail trade19,429x(-5.46%--1.28%)=-812
Finance and insurance11,433x(3.91%-8.32%)=-505
Educational services4,626x(11.50%-9.81%)=78
All Other Employment110,631x(3.36%-2.26%)=1217
County Local Share-941

Interpretation: According to the local share component, -941 new jobs in Cumberland County are attributable to its relative competitive position--in a sense, the county itself lost a greater share of employment growth than the nation did on average. In addition to overall growth, the analysis can also be used to examine how individual industries have fared competitively. Here, we see that three industries had negative local shares.

It is important to keep in mind that this is a descriptive tool rather than a diagnostic one is important. The shift-share analysis does not tell us why some local industries are more competitive and why some are less competitive--differences may be due to technology, management, or worker productivity. A more in-depth analysis of local versus national industries is required to sort out the sources of these differences. Potential factors could include access to natural resources, local wage rates, workforce productivity, or regional transportation networks.

Adding It Up

After calculating the national growth, industrial mix, and local shares, you should make sure your math is right. To do so, simply add up the three shares; their total should equal the total local employment change over the period.

Total Employment Change = National Growth Share + Industry Mix Share + Local Share

1,792 = 2,372 + 356 + -939

The United States Regional Economic Analysis Project

provides a tool that generates Shift-Share Analysis for any county and metropolitan region in the country. The analysis generates a relatively detailed report and tables.

How This Information Is Used in Economic and Community Development

Once again, you should spend some time interpreting the data. When looking at shift-share analysis, consider the following questions:

  1. Compared to other regions, does the community seem highly competitive in any particular industry? What is the source of this competitiveness?
  2. Does this information support popular perceptions? Or, does the analysis uncover surprising areas of economic strength or weakness?
  3. Are observed differences in growth rates due to differences in employment mix found at the local level relative to that observed in the larger economy? Or are differences due to the competitive advantage or disadvantage that the specific local economy has relative to the larger economy?

A Few Caveats

Noting that shift-share is a simple analytical technique that does not account for many factors is important. Keep several things in mind when digging into the results:

  • The technique minimizes the impact of issues such as business cycles.
  • The method falls short in actually identifying comparative advantages.
  • A shift-share industrial analysis is a "snapshot" of two particular points in time, and the results are sensitive to the period of time chosen.
  • Finally, shift-share is sensitive to differences caused by levels of industrial detail. Shift-share analysis does, however, offer a simple, straightforward approach to separating out the national and industrial contributions from local or regional employment growth. This makes it a valuable addition to the practitioners' toolbox.

Conclusion

Shift-share analysis examines the sources of changes in local employment growth or decline. By using shift-share, you can identify local advantages, as well as pinpoint growth or potential growth industries. Like many other economic tools, the shift-share technique is a descriptive tool that should be used in combination with other analyses help better understand the region's key industries.

Shift-share, and the local share component in particular, can point to industries that enjoy local comparative advantage. It cannot, however, identify what the actual comparative advantage is. Identifying which factors have contributed to the local area outperforming national growth is important.

Identifying whether the large gainers or losers are typically exporters is also important. (Remember: You can use the location quotient tool to identify importing and exporting industries.) Exporting industries are important because they pull in dollars from outside of the local region, thus serving as a growth engine.

For More Information

The data needed for calculating shift-share components is available from the Bureau of Labor Statistics or the Department of Commerce. For Pennsylvania, much of the data and analysis used in this series is available on-line via Penn State's Center for Economic and Community Development. In addition, this Web site also provides educational materials and analyses for better understanding trends in the state economy.

The state's Center for Workforce Investment Analysis at the Department of Labor and Industry is Pennsylvania's designated provider of employment statistics. On their Web site you can find a variety of statistics related to employment, wages, and unemployment for the state, metropolitan areas, and counties.

The Pennsylvania Regional Economic Analysis Project (REAP) provides a Shift-Share Calculator that quickly calculates Shift-Stare analysis for counties in the state.

These materials are an update of those originally produced by Martin Shields, Professor of Economics at Colorado State University.

The Center for Workforce Information and Analysis (CWIA) in the Pennsylvania Department of Labor and Industry provides employment projections at both the industry and the occupation levels.

Short-term and long-term online industry projections and demand occupations are provided for each of the state's 22 Workforce Investment Areas (WIAs), which include all 67 counties in the Commonwealth.

Overview: Employment and Occupation Projections Can Help Identify Workforce Needs

Other tools in this series have focused on answering questions such as "What jobs are the basis of our economy?" and "How has our economy changed over time?" While this information is important for understanding current local economic conditions, it provides somewhat limited insights into the question "What are the expected growth industries and occupations in our local economy?".

Employment and occupational projections for Pennsylvania, the 22 WIAs and 14 Metropolitan Statistical Areas are available from the Center for Workforce Information and Analysis (CWIA) in the Pennsylvania Department of Labor and Industry. Employment and occupation projections for the nation and other states are available from the federal Bureau of Labor Statistics.

Some Key Definitions

This tool introduces resources for analyzing employment growth at both the industry and occupation level.

  • An industry refers to a productive sector of the economy, such as manufacturing.
  • An occupation refers to a particular job title or skill.

Industry Employment Projections

Short-Term Industry Employment Projections

CWIA provides short-term industry forecasts (2 years) for each of the Commonwealth's 22 workforce investment areas and Metropolitan Statistical Areas. Short-term industry forecasts are produced by software designed by America's Labor Market Information System (ALMIS), which was tested and further refined through a consortium of states. Projections are produced at the two-digit North American Industry Classification System (NAICS) level for each WIA.

Short-term industry forecasts display the likely industry employment trends within a specific geographic area. The ALMIS models used to produce the industry forecasts rely on historical patterns, leading economic indicators, and the relationships among different industries. The model provides quarterly employment projections. Table 1 shows sample output.

Table 1. South Central Workforce Investment Area--Number of Jobs.
IndustryAverage EstablishmentsJulyAugustSeptemberAverage
Total, All Industries31,942627,768632,345632,774630,962
Accommodation and Food Services2,77850,91151,48649,71150,703
Agriculture, Forestry, Fishing & Hunting3855,0185,3435,8835,415
Finance and Insurance1,73427,58927,71327,52627,609
Real Estate and Rental and Leasing9155,5385,5345,3875,486
Retail Trade4,32270,47670,74769,81970,347
Transportation and Warehousing1,15042,47043,51044,98443,655

Data which might be identified with an individual employer are not published.
Note data aggregated by Workforce Investment Area (WIA)

Long-Term Industry Employment Projections

CWIA also provides long-term industry projections (10 years) for the state, the 22 WIAs and 14 Metropolitan Statistical Areas. Like the short-term forecasts, long-term industry projections are produced using the ALMIS software. Projections are produced at the four-digit North American Industry Classification System (NAICS) codes and titles level with the appropriate three-digit and Major Industry Division aggregations. The model provides 10-year employment projections. Table 2 shows sample output.

Table 2. South Central Workforce Investment Area--Industry Employment.
Industry TitleEmployment - Estimated 2012Employment - Projected 2022Change: NumberAnnual Average Percent ChangeTotal Percent Change
Total, All680,420734,87054,4500.77%8%
Business & Financial Operations33,30036,2602,9600.86%8.89%
Community & Social Services15,04016,3001,2600.81%8.38%
Education, Training & Library33,82035,2101,3900.40%4.11%
Farming, Fishing & Forestry8,2208,140-80-0.10%-0.97%
Legal4,9805,4404600.89%9.24%
Management32,12033,5501,4300.44%4.45%
Office & Administrative Support109,860113,6203,7600.34%3.42%
Sales & Related63,63066,3702,7400.42%4.31%
Transportation & Material Moving62,25069,0206,7701.04%10.88%

Totals may not add up due to rounding.
Pennsylvania Department of Labor and Industry, PA Workforce Statistics

The tool utilizes an Industry Projections Dashboard that allows the user to:

  • Produce Local Area to State Comparisons
  • Create Geographical or Industry Groupings of Interest
  • Export Excel Files for Further Review
  • Design Custom Reports and Charts

Long-Term Occupation Projections

Industry employment projections are useful in identifying areas of strength as well as new opportunities. However, an industry focus does not provide a complete picture of demand for workers in the local job market. The fact that industry-level data does not provide information on the expected growth in particular occupations or skills illustrates this. Thus, even though analysis may identify that an industry is growing, it does not necessarily provide information on the specific types of jobs that are growing. Occupation projections can help you understand what types of jobs will be in demand.

For example, suppose you are a workforce educator and, using the long-term industry projections, have identified that your local banking industry is expected to show tremendous growth. Hoping to capitalize on this growth, you have decided to develop an economic development strategy that nurtures this industry. One question you might ask is "What are the industry's workforce needs?"

While the banking industry projections show broad trends, they do not provide insight into what specific types of jobs are growing. It may be the case that the growth will result in an increase in the number of bank tellers; or, it could be that the number of mortgage personnel is expected to increase. Because these occupations require different worker skills and knowledge, both individuals and practitioners need to know which specific occupations are growing. With this understanding, proper workforce training and education strategies can be developed.

Occupation projections for Pennsylvania at the state level, Workforce Investment Areas, Metropolitan Statistical Areas and some counties are available from the Center for Workforce Information and Analysis (CWIA) in the Pennsylvania Department of Labor and Industry. Information is returned on a 10-year projected growth. Sample output for Pennsylvania is shown in Table 3.

Table 3. Select Occupation Projections for Pennsylvania - 2012-2022.
Title2012 Estimated Employment2022 Projected EmploymentTotal 2012-2022 Employment ChangeAnnual Avg. Percent ChangeTotal Percent Change
Total, All6,046,5606,514,500467,9400.75%7.74%
Architecture & Engineering96,290105,4309,1400.91%9.49%
Business & Financial Operations281,100309,43028,3300.96%10.08%
Healthcare Practitioners & Technical361,300420,79059,4901.54%16.47%
Office & Administrative Support973,6601,001,34027,6800.28%2.84%

Source: Center for Workforce Information and Analysis

Sample output for the South Central WIA for 2012 - 2022 are shown in Table 4.

Table 4. Projections for Selected Occupations in South Central WIA Pennsylvania for the 2012 - 22
Occupational TitleEstimated Employment 2012Projected Employment 2022Percent ChangeOpenings: Due to Growth1Openings: Due to replacement2Openings: total3
Total, All Occupations680,420734,8708.0%5,89716,09621,993
Management Occupations32,12033,5504.5%200632832
Top Executives9,2709,9207.0%66180246
Chief Executives2,1002,1502.4%54550
General & Operations Managers6,9107,5108.7%59129188
Legislators2502708.0%167
Advertising, Marketing, Promotions, Public Relations & Sales Managers1,8502,0008.1%154055
Advertising & Promotions Managers1001000.0%033
Marketing Managers52059013.5%71118
Sales Managers9501,0005.3%52025
Public Relations & Fundraising Managers2803007.1%268

1 Labor force growth openings, except for cases of negative growth where growth openings are expressed as zero.
2 Labor force net replacements due to death, retirement, disability, or withdrawal for personal reasons.
3 Total openings equal replacements plus annual growth.

High Priority Occupations

A list of High-Priority Occupations for the state, Workforce Investment Areas and Metropolitan Statistical Areas are available from the Center for Workforce Information and Analysis (CWIA) in the Pennsylvania Department of Labor and Industry. Information provided includes educational attainment, average annual wage and annual openings for occupations that are in demand by employers, have higher skill needs and are most likely to provide family sustaining wages. This information is intended to align workforce training and education investments with high priority occupations. Table 5 shows sample output.

Table 5. 2014 High-Priority Occupations for South Central Workforce Investment Area
SOC CodeOccupationOccupational GroupEduc. Attain.Annual Average WageAnnual Openings

13-2011

Accountants & AuditorsBusiness/Financial

BD

$65,230174

49-3021

Automotive Body & Related Repairers

Maintenance/Repair

MT OJT

$43,59025

49-3023

Automotive Service Technicians & Mechanics

Maintenance/Repair

LT OJT

$36,580163

43-3011

Bill & Account Collectors

Office & Administrative Support

MT OJT

$34,36031

43-3021

Billing & Posting Clerks

Office & Administrative Support

ST OJT

$32,58057

19-4021

Biological Technicians

Life/Physical/Social Science

BD

$42,8405

43-3031

Bookkeeping, Accounting & Auditing Clerks

Office & Administrative Support

MT OJT

$35,510139

47-2021

Brick masons & Block masons

Construction/Extraction

LT OJT

$44,98025

49-3031

Bus & Truck Mechanics & Diesel Engine Specialists

Maintenance/Repair

LT OJT

$42,74066

51-3021

Butchers & Meat Cutters

Production

LT OJT

$30,60041

29-2031

Cardiovascular Technologists & Technicians

Healthcare Professionals

AD

$54,36011

25-2032

Career/Technical Education Teachers, Secondary School

Education/Library

BD+

$64,01015

43-5011

Cargo & Freight Agents

Office & Administrative Support

ST OJT

$42,44012

Source: Center for Workforce Information & Analysis, Occupational Employment Statistics Survey (2013) and Employment

Methods

Industry Employment Projections

CWIA employment projections by industry forecast the anticipated changes within an industry over time. The local forecasts are based on past regional employment trends within each industry (indeed, the model is basically a fancy version of the charting method described in Tool 2). The effects of state-level economic trends are also considered. Preliminary employment forecasts are initially produced using a family of statistical models. Analysts then review preliminary industry employment forecasts and make adjustments based on local and state developments that may occur over the forecast period.

All Occupation Projections (State-Level)

The state-level occupation projections build on the industry employment projections. Specifically, industry-staffing patterns are used to convert industry employment to occupational employment via a two-step process. First, staffing pattern data is used to specify the percentage of a particular occupation within an industry; for example 65 percent of employees in the banking industry work as tellers. The percentages for each occupation are multiplied by industry employment to produce occupation employment for that industry.

Occupation projection for industry (i) = employment projection for industry (i) x occupation's share of total employment in industry (i)

(example: 650 tellers = 1,000 projected jobs in the banking industry x 65% of bank employees are tellers)

In the second step, total occupation projections are determined simply by adding occupation projections across industries.

Total projected occupation employment = sum of occupation employment for all industries.

Demand Occupation Projections (WIA-Level)

CWIA demand occupations identification process begins by identifying the top 25 growing industries by employment for each WIA. The CWIA supplements this data by evaluating hiring trends for the area. From this data, the top 75 occupations are identified. When available for the area, employment projections are used to add those occupations that show a minimum of 20 projected openings per year. The list is also expanded by listing occupations recognized as demand occupations based on the knowledge of regional analysts. This list is not meant to be all inclusive, and local experts can also provide occupations that may be added to the list.

How This Information Is Used in Economic and Community Development

Knowing future industry and occupation trends helps community and economic development practitioners understand their local economy, foster compatible growth, and promote local strengths. People looking for work and those doing training, counseling, and/or job placement may use the industry projections to learn about employment opportunities in various industries. Still, when interpreting this information, keep the following points in mind:

  1. The level of aggregation matters. If broad projections indicate declining employment, it may be that only one or two industry sectors are experiencing the decline and other sectors may still be expected to grow. For example, retail trade has several components, including building materials and garden supplies; general merchandise stores; food stores, automotive dealers and service stations; and apparel and accessory stores. Thus, investigating all sectors of the industry is best.
  2. Turnover creates opportunities. Although an industry may be stable and is not expected to grow, it does not mean that there are no opportunities for employment. Individuals change or leave their jobs permanently for varying reasons. High turnover, especially in industries that require lower skill levels, means that there are frequent openings even though there is little or no growth.
  3. Industries have varying levels of growth. Not all industry sectors, such as those in retail trade or in services industries, grow at the same rate; nor are all sectors located in every county.
  4. Location matters! If an individual is considering relocating within the state, the size of the industry and its expected growth level needs to be kept in mind. Often, small rural counties will not have the same opportunities that are available in larger metropolitan areas.

A Few Caveats

  • Remember that these data are estimates. The projections are developed assuming that historical trends will continue into the future. However, unpredictable events may occur over the course of the projection period, adversely affecting projection accuracy. For example, an unexpected major business closure or opening or a natural disaster can have a substantial impact on employment levels.
  • Long-term employment projections data are annual averages. These averages may not accurately portray seasonal occupations or industries such as those found in agriculture, retail sales, and recreation.
  • Projected employment levels reflect only those workers who are covered by the unemployment insurance program. As a result, industries made up largely of individuals who are self-employed will be understated. Examples include industries such as real estate, hair salons, and bookkeeping.
  • Do not use these projections as your sole source of information. Supplement the projections data with other, more recent sources of local economic data. Useful information may be found in other documents such as those published by local chambers of commerce or local economic development agencies.

Keep in mind that projections are just one planning tool and that the estimates are based on information available at the time the forecast was made.

For More Information

Additional information on industry employment and occupation projections is available from a number of sources. At the state level, these sources include:

  • Center for Workforce Information and Analysis, Pennsylvania Department of Labor and Industry. CWIA is Pennsylvania's designated provider of employment statistics. Their goal is to provide users with the most current data available to help with decision making and to assist in meeting local planning needs.
  • U.S. Bureau of Labor Statistics (BLS). BLS produces employment and wage estimates for over 700 occupations. These are estimates of the number of people employed in certain occupations and estimates of the wages paid to them. Self-employed persons are not included in the estimates. These estimates are available for the nation as a whole, individual states, and metropolitan areas; national occupational estimates for specific industries are also available.
  • Occupational Outlook Handbook, a nationally recognized source of career information, designed to provide valuable assistance to individuals making decisions about their future work lives. Revised every two years, the Handbook describes what workers do on the job, working conditions, the training and education needed, earnings, and expected job prospects in a wide range of occupations.
  • Projections Central: State Occupation Projections Projections Central provides Long-Term and Short-Term Projections for all states and the nation as a whole. Projected employment growth for an occupation can be compared among states. Projected occupation employment growth among occupations can be compared within one state.

Prepared by Martin Shields, assistant professor of agricultural and regional economics.

1 In 1998, Pennsylvania implemented federal legislation known as the Workforce Investment Act of 1998. This legislation was adopted to coordinate and improve employment, training, and education systems. The legislation also mandated the creation of local workforce investment boards as the vehicle to develop and ensure the implementation of a unified and effective strategy for addressing workforce development issues and meeting service delivery needs.

Qualitative analysis is useful in helping identify local key industries that are deemed “important” for reasons other than the number of jobs or amount of payroll they create.

A local business may not be large in terms of employment levels, but nonetheless is viewed as important because it has attributes that local citizens, government officials, and development practitioners find desirable. Qualitative analysis allows researchers to both find out why certain trends in data have occurred and complete the story quantitative analysis provides.

Overview: Qualitative Analysis Complements Other Tools

Most of the material in this series focuses on techniques for analyzing secondary data that is readily available and accessible. However, secondary data analysis cannot provide all of the answers when trying to understand the local economy. The shortcomings in secondary data analysis may arise from a number of factors, including data limitations such as unavailability at more detailed levels of analysis (perhaps for confidentiality reasons). Or, the data may not provide sufficient insight into the linkages between various sectors of the local economy.

Qualitative analysis refers to identifying and assessing factors that may not be easily quantified. Qualitative analysis is especially useful in helping identify local key industries that are deemed "important" for reasons other than the number of jobs or amount of payroll they create. For example, a local business may not be large in terms of employment levels, but nonetheless is viewed as important because it has attributes that local citizens, government officials, and development practitioners find desirable. In short, qualitative analysis allows researchers to both find out why certain trends in data have occurred and complete the story quantitative analysis provides.

Though not easily quantified, the following list shows some attributes used by states, regions, cities, and other areas when assessing industries in their economic development analysis.

The industry

  • is environmentally clean,
  • has manageable infrastructure needs,
  • has low energy needs,
  • supports desirable workforce skills,
  • provides training and skill enhancement,
  • offers economic diversity,
  • may attract other businesses to the area,
  • utilizes high-tech processes,
  • has manageable transportation needs,
  • is "family friendly,"
  • contributes positively to the local quality of life, and
  • is a business headquarters or a hub.

By assessing local businesses and industries according to these or similar criteria, your community can gain additional insight into the structure of the local economy. In addition, these attributes can be used to establish parameters for new local economic development initiatives.

How This Information Is Used in Economic and Community Development

Establishing local priorities and economic development objectives is helpful when formulating the key qualitative and quantitative criteria that you will use to evaluate local industries. The strategic planning process offers one way for your community or organization to determine these objectives. As part of the planning process, qualitative approaches such as key informant interviews, focus groups, surveys, and case studies can be used to identify key industries, values, skills, or resources that your community would like to strengthen or promote. After drawing up a list of desirable characteristics, you can see not only how local industries reflect these attributes, but you can also use this list to identify and evaluate future development prospects.

Qualitative approaches have been very useful to states and regions looking at developing industry clusters. Many states use interviews and focus groups with business leaders, for example, to categorize important forward and backward linkages among local industries. These techniques can also be used to discover common and complementary labor, infrastructure, and input needs among businesses. Finally, some states identify industry clusters through interviews with firms about their suppliers. Regardless of whether such analysis is done using formal or informal survey methods, such meetings can provide information on the linkages among local industries, as well as their anticipated workforce changes and other needs.

Qualitative methods can also help you rethink your overall approach to economic analysis. The recent State and Local Policy Program (SLLPP) survey conducted by the University of Minnesota's Humphrey Institute reported some states group industries by "function" rather than "product." The study also indicates that some states are beginning to group industries based on labor, energy, and transportation requirements as well as common technologies and workforce skills.

As an example of alternative approaches, the SLLPP cites a region that has defined a key cluster of industries it wants to promote according to a major input requirement--namely, warm water. This cluster was identified after noting that the region's energy plants generate large amounts of warm water that could be recycled. By finding a use for this input, the region promoted the local development of a cluster of greenhouses and related businesses.

A Few Caveats

You can devise a number of possible qualitative indicators to measure factors such as "family friendly," "environmentally sound," and "good corporate citizen." Unfortunately, there is usually no one agreed upon measure for them. As a result, certain qualitative indicators may be contentious.

While quantitative measures used to define key industries--employment, wages, earnings--are often common and well understood across states, regions, and communities, qualitative criteria can vary widely from place to place. Ideally, qualitative criteria should reflect an area's unique character, priorities, and reality.

For More Information

Focus groups and key informant interviews are two techniques commonly used in qualitative analyses. While a detailed discussion of these techniques is beyond the scope of this bulletin, the Internet provides several useful sets of material, including:

  • "Designing and Conducting Focus Group Interviews" by Richard Krueger. Richard Krueger is known for his efforts in using focus group interviewing within the public environment.
  • "Guide to Conducting Focus Groups for Community-based Research and Evaluation" While this guide was developed by First Work, an organization that supports and advocates for a sustainable youth employment delivery network in Ontario, its information is applicable to a broad array of topics.
  • "Conducting Key Informant Interviews". This site, USAID, provides an overview of this important survey technique. Covered topics include what are key informant interviews, when are key informant interviews appropriate, advantages and limitations and steps in conducting interviews.
  • "Key Informant Interviews" This resource from the University of Illinois Extension provides detailed overviews on, using key informant interviews, tips for managing a key informant interview survey, conducting a key informant interview, writing the introduction for key informant interviews, asking open-ended questions and probing the answers and recording and summarizing the results.

These materials are an update of those originally produced by Martin Shields, Professor of Economics at Colorado State University.