- Campuses :
- Twin Cities
- Crookston
- Duluth
- Morris
- Rochester
- Other Locations
Here's an outline of the entire process of using quantitative indicators, follow it in order or click on the section that most interests you.
Analyzing employment data
Step 1: Share of local employment
Step 2: Change in employment
Step 3: Location quotients
Step 4: Change in location quotients
Step 5: Shift-share analysis
National share
Industry mix
Competitiveness
Step 6: Analysis of payroll data
Step 7: Analysis of earnings data
Step 8: Analysis of firm data
Input-output accounts and analysis
Definition of key terms
To complete the quantitative portion of Understanding Your Industries, you will need both local and national level data. The World Wide Web includes a variety of sites at which you can easily find and either copy or download data. Federal government data sources provide all of the data you might need if you are looking at a county, group of counties, metropolitan area, or state. All of these data are available using NAICS codes. For more detailed data, you might want to check your state data sources. The two-digit level data, however, is a very useful starting point.
State and county level data is available through the Bureau of Labor Statistics' Covered Employment & Wages Series. This can be accessed through the BLS public query system. Copy and paste the URL below into your browser to reach this site.
The steps to obtaining your data are straightforward. First, select the state of interest. Next, choose between data for the entire nation, a state or group of states, a specific county or set of counties. (To select more than one county, remember to hold down the CTRL button as you highlight your selections.) The next step will be to select the industry of interest. (To select more than one industry, remember to hold down the CTRL button as you highlight your selections.) The NAICS system is best thought of as a "nested hierarchy". For example, NAICS 11 represents "Agriculture, Forestry, Fishing & Hunting" whereas NAICS 111 designates "Crop Production" and NAICS 1111 signifies "Oilseed & Grain Farming." As the number of digits in the code increases, the level of industry detail increases. It's important to be aware of disclosure/confidentiality issues. The more detailed an industry you select, the more difficult it becomes to obtain data (particularly at the county level.) If appropriate, you can next select the type and size of establishment. Finally, you can select the data you wish to have represented: Number of Employees; Number of Establishments; Total Wages; Average Weekly Wage; Average Annual Pay.
The following is a list of all of the basic data you will need to complete the analysis:
Overview
A review of employment data is a very good first step in identifying and understanding your area's key industries. Quite simply, employment data will provide you with the number of people in your community whose incomes depend directly on a particular industry. Employment data can also be used to help determine: 1) which industries are growing and which are declining, 2) the importance of an industry to your economy relative to its importance nationally, and 3) how competitive regional industries are compared with their counterparts nationally. In cases in which averages by employee are not available, you can use employment data to determine, for example, the average payroll, earnings, and value-added per employee.
For each of your industries determine its share of all local employment and its share of local employment in its sector. It is particularly instructive to pick the sectors you would like to look at, for example manufacturing and services, and perform your analysis within an individual sector or groups of sectors. Imagine that fabricated metal products represented only 1.3 percent of total employment, but 12 percent of manufacturing employment. If we looked only at the share of total employment, the fabricated metal industry could get lost, even though it is a very important manufacturing sector.
How to calculate share of regional employment:
Data you need:
To determine each industry's share of employment within its sector, divide employment in the industry by total employment in the sector. Imagine that employment in food and kindred products is 600 and total employment within manufacturing is 11,500. Therefore, food and kindred products' share of employment is: 5.2 percent.
Equation:
Share of Employment = Employment in Industry/Total Employment in Sector
Although an industry may not currently register one of the highest shares of employment, it may be outstripping all other firms in terms of growth in employment. In this case, you would want to study the industry and try to understand what is driving its growth. The reasons for this industry's growth may provide useful information that can help your economic development efforts. For example, if your fabricated metal products industry is growing quickly, you may want to see if your primary metals industry, which could potentially supply inputs to the fabricated metal products industry, is growing fast as well. If it is not, you might want to look for ways to help these two industries work together. On the other hand, if your food and kindred products industry is declining, you may want to look into what that means to the future of your farming sector.
How to calculate change in an industry's share of employment:
To determine the growth or decline of employment in an industry over a specific period of time, divide the number of employees in the most recent year by the number of employees in the base year. Subtract 1 from the result and multiply by 100 to get the percentage.
Equation for determining the percentage change in employment:
Percentage Change = [(Employment in Industry/Total Employment in Sector)-1]*100
The location quotient is a measure of an industry's concentration in an area relative to the rest of the nation. It compares an industry's share of local employment with its share of national employment. Although location quotients require several assumptions, including uniform local consumption patterns and labor productivity across the country, they are a quick and useful tool in determining a region's key industries.
A location quotient is simply an industry's share of local employment over the industry's share of national employment. If the location quotient is 1 than the industry's share of local employees is the same as the industry's share nationally. A location quotient greater than 1 means the industry employs a greater share of the local workforce than it does nationally. A location quotient less than 1 implies that the industry's share of local employment is smaller than its share of national employment. A location quotient between .85 and 1.15 is close enough to 1 so that it is not considered particularly significant.
How to calculate a location quotient for each industry:
The data you need:
Equation:
Industry Location Quotient =(Employment in Industry/Total Employment in Base)/(National Employment in Industry/Total National Employment)
What does this mean?
A location quotient greater than one implies that the industry is producing more goods and services than are consumed locally. Thus, the industry is exporting the goods or services out of the area and, in the process, bringing new dollars into the area. Industries that bring dollars into the area help the local economy grow.
If a location quotient is less than one in a service or retail industry, it may mean that residents and businesses purchase services and retail goods they require from outside the area. In such a case, you may want to look at the reasons this is happening. For example, is there local demand for a good or service that is not being met? If so, is there an opportunity for growth of that industry in the area?
Caution!
Just because a location quotient is greater than 1, does not necessarily mean that an industry is competitive or growing. It may simply mean the industry is not as efficient and employs more people than the national average to produce the same level of output. Other measures, such as earnings or value-added per employee, shift-share analysis, and changes in employment, can help you determine whether the industry is actually high-growth and competitive.
As with other quantitative techniques, location quotients calculated for highly aggregated industries, such as the NAICS 2-digit industries, may mask interesting activity of individual industries. For example, an area might have a very high location quotient for a 3-digit industry. The importance of that embedded industry could be masked if a NAICS 2-digitindustry has a location quotient near or less than one.
Another way to use location quotients is to look at how they have changed over a period of time. This comparison will give you an idea of whether each industry is increasing or decreasing its concentration and importance in your area relative to other areas.
How to calculate changes in location quotients:
The data you need:
Note: Data should be obtained for the current year and a base year.
Equation:
First, calculate the location quotient as shown in Step 3.
Percentage Change in LQ = [(Most recent year LQ/Base Year LQ)-1]*100
What does this mean?
Determining whether the location quotients grew or declined will give you a better idea of how the industries' importance to the regional economy has changed over time. This information, coupled with the location quotients themselves, provides a useful analytical tool for understanding your region's industries. For example, your community might pursue one set of strategies for industries with high, but declining, location quotients, and another set of strategies for industries with low, but increasing location quotients.
The calculations you completed allow you to group your region's industries into four categories:
Each category might entail a different economic development approach. For example, if a region wanted to promote the expansion of manufacturing, it might focus its efforts on the industries that have large, but declining location quotients and on those that have small, but increasing location quotients. The industries with large location quotients are obviously quite important to the current economy, and to lose them might cause considerable hardship. Since some industries are experiencing decline, it may be wise to work with them to understand what is causing their demise and develop appropriate programs and policies to stop or slow their decline. Those industries that are heavily concentrated and growing, may be doing quite well and all you will want to do is try to understand why.
On the other extreme are those industries that have small, declining location quotients. These industries are not as important to the economy and apparently do not have much potential in the region. On the other hand, those industries with small but growing location quotients may be a source of considerable future growth for the economy and should warrant special attention.
Caution!
Although an industry may fit into the small, declining location quotient category, it may include one or more small, but dynamic industries. Thus, it is important to look at the types of industries included in each SIC Industry classification and compare that list with information you have or will collect on the names and types of firms in the region.
Shift-share analysis is one way to measure the competitiveness of your region's industries. It provides a picture of how well the region's current mix of industries is performing and how well individual industries are doing. The analysis examines three components of regional growth: national growth, industry mix, and competitiveness.
Although the shift-share model can be applied to income, earnings, and other measures, it is used here to analyze employment. Shift-share analysis will provide the portion of total employment growth due to: growth of the national economy, a mix of faster or slower than average growing industries, and the competitive nature of the industries in the region. Like many analytical tools, shift-share analysis is only a descriptive technique that when combined with other analysis helps provide a picture of your region's key industries. The shift-share technique should be supplemented with qualitative research, such as interviews with representatives of the regional industries.
The work to complete the shift-share analysis is broken down into four separate sections: the national growth component, the industry mix component, the competitiveness component, and the total effect. The data you will need:
Note: Total employment is not needed since it should be equal to the total number of employees in the industries used in the calculations.
The national growth component is the share of local job growth that can be attributed to growth of the national economy. This assumes that if the national economy grew by, for example, 5 percent, then we can expect the local economy to grow by 5 percent.
How to calculate the national growth component:
The calculation for the national growth component is quite simple. Multiply base year employment for each industry by the average national employment growth rate in the sector or sectors (e.g., manufacturing, services, trade) you are using. Add up the result for each industry to get the national growth component.
Equations:
National Average Growth Rate =
[(Recent Total National Employment)/(Base Year Total Employment)-1]
Industry National Growth Component =
(Local Industry Base Year Employment)*(National Average Growth Rate)
Example:
Using simple and fictitious data in the table below, multiply the local base year employment of 350 by 11.1 percent to get 389 jobs in the durable goods industry. For nondurable goods, we would multiply 450 by 11.1 to get 500. We add these and get 889 as the national growth component or share of the region's total employment growth.
National growth rate: (30,000 / 27,000) - 1 = 11.1 percent.
What does this mean?
The national growth component of each industry tells us how many jobs in the industry can be attributed to the growth of the national economy. We might also say that if the industries we are considering grew at the same rate as the same group of industries nationally, then the number of jobs created in our example would be 889. However, since we know that 950 jobs were created, we need to examine what might account for the additional 61 jobs. To do this, we will move to the industry mix and competitiveness components.
Unlike our example, you may find that the number of jobs created locally is actually less than the number of jobs that would have been created if the industries grew at the national rate. In our example, if the total national growth component was 1000, we would need to account for the missing 50 jobs. To explain this difference we would also look at the industry mix and competitiveness components.
Once you account for national growth, the next step is to look at the number of jobs that may be attributed to the region's mix of industries. This part of the analysis calculates the number of jobs created or not created in each industry due to the difference in that industry's national growth rate and the average national growth rate. It also provides the number of jobs created or not created as a result of the region's overall industry mix.
Each industry's growth nationally may have been favorable, neutral, or unfavorable. If an industry experienced favorable growth it means that its employment growth rate outstripped the average national growth rate for all industries. If the growth is neutral, the industry grew at the average national rate. If the growth is unfavorable, the industry grew less quickly than the national growth rate.
Once these net rates are applied to the region's base year employment, you will be able to tell how much of the region's employment growth was affected by its concentration of high-growth and low-growth industries. You will also know how many jobs were created - or not created - in each industry because of its favorable or unfavorable growth rate relative to the rest of the nation.
How to calculate the industry mix component:
As a first step in calculating the industry mix component, determine how each industry's national growth rate differs from the average national growth rate for all of the industries. To do this, subtract the average national growth rate from each of the industry's national growth rates. Second, multiply this difference (which may be positive or negative) by the base year employment for each industry. This will provide the number jobs created, or not created, due to the industry's favorable or unfavorable growth rate. As a final step, add the job numbers you calculated to get a total industry mix component. If this is positive, then the economy can be said to have a favorable industry mix. If it is negative, then the region has an unfavorable industry mix.
Equations:
Industry National Growth Rate =
[(Industry's Recent National Employment)/(Industry's Base Year Employment) ' 1]
Difference in national average growth rate and industry's national growth rate:
Industry Mix Differential =
(Industry's National Growth Rate) ' (National Growth Rate)=
(The national growth rate was calculated earlier for the national growth component.)
Each industry's employment change due to industry mix differential:
Industry Mix Employment Change =
(Differential) * (Local Industry's Base Year Employment)
Total Industry Mix Component =
Sum of Industry Mix Employment Changes for each Industry
Example:
Using the data and calculations in the table below, we can examine how each industry's growth rate differs from national growth. For durable goods, subtract the national growth rate of 11.1 from the industry growth rate of 7.7 to get -3.4 percent. For nondurable goods, subtract 11.1 from 14.3 to get 3.2 percent. By multiplying these percentages by the base year employment data, you will find that 14 additional jobs were created in nondurable goods because the region has a favorable industry mix. You will also find that because durable goods were not growing as quickly as the national average, 12 jobs that would have been created if the regional economy matched the national economy were not generated. Since more employees work in nondurable goods than in durable goods, when the industry mix components for each industry are added together the result is positive. This means that because the region's economy has a more favorable mix of industries than the nation as a whole, two additional jobs were created.
National growth rate: (30,000 / 27,000) - 1 = 11.1 percent.
Industry growth rate:
Durable Goods: (14,000 / 13,000) - 1 = 7.7 percent
Nondurable Goods: (16,000 / 14,000) - 1 = 14.3 percent
What does this mean?
Those industries that are growing faster than the national average may be more dynamic and have bright futures. However, these industries might also have increasing appetites for labor, space, and inputs that may outstrip the local supply if adjustments are not made to anticipate future demands of these industries.
There may be a variety of reasons some industries experienced negative employment changes. For example, the industry's importance may be declining nationally, it may be going through a cyclical change that does not coincide with the rest of the economy's business cycle, it may have altered its production so that it is producing the same, but not employing as many people, or it may have ineffective management and processes that hurt its ability to expand. Top
After calculating the national growth and industry mix components, the next step is to look at the number of jobs created, or not created, as a result of the region's competitiveness. The assumption is that once you account for national growth and the mix of industries, any additional job growth must be due to the region's competitive advantage. The shift-share competitiveness component measures the ability of the regional economy to capture
How to calculate the competitiveness component:
As a first step in calculating the competitiveness component, determine how each industry's local growth rate differs from the industry's national growth rate. To do this, subtract each industry's national growth rate from its regional growth rate. As a second step, multiply this difference (which may be positive or negative) by the base year employment for each industry. This will tell you how many jobs were created, or not created, due to the region's competitiveness in the industry. As a final step, add the job numbers you calculated to get a total competitiveness component. If this is positive, the region is considered more competitive than the nation in the sectors examined. If it is negative, the region is considered less competitive than the nation.
Equations:
Industry's Local Growth Rate =
[(Industry's Recent Local Employment)/(Industry's Base Year Local Employment) ' 1]
Difference in industry's local growth rate and the industry's national growth rate:
Competitiveness Differential =
(Industry's Local Growth Rate)/(Industry's National Growth Rate)
(The industry's national growth rate was calculated earlier for the industry mix component.)
Each industry's employment change due to competitiveness differential:
Competitiveness Employment Change =
Differential * Local Industry's Base Year Employment
Total Competitiveness Component =
Sum of Competitiveness Employment Change for each Industry
Example:
Using the data and calculations in the table below, we can examine how competitive each regional industry is compared with its counterparts nationally. For durable goods, subtract the industry's national growth rate of 7.7 from it regional growth rate of 14.3 to get a net change of 6.6 percent. For nondurable goods, subtract 14.3 from 22.2 to get a net change of 7.9 percent. By multiplying these percentages by the base year employment data, you find that 23 additional jobs were created in the durable goods industries and 36 jobs were created in the nondurable goods industries. When you add the two you will find that 59 jobs were created in the region because of its competitive position relative to the rest of the nation.
National industry growth rate:
Durable Goods: (14,000 ÷ 13,000) - 1 = 7.7 percent
Nondurable Goods: (16,000 ÷ 14,000) - 1 = 14.3 percent
Local industry growth rate:
Durable Goods: (400 ÷ 350) - 1 = 14.3 percent
Nondurable Goods: (550 ÷ 450) - 1 = 22.2 percent
What does this mean?
If the total competitive component is positive, the region gained additional jobs over those that can be attributed to national growth and the region's industrial structure. If the total competitive component is negative than the region was less competitive than the national average.
Caution!
The competitiveness component does not tell us why various industries grew or declined in the number of people they employ. Rather, it offers a way to look at industries relative to their counterparts around the country. In addition, shift-share analysis examines employment changes not changes in income, earnings, or value-added, which are alternative measures of an industry's size. For example, although healthy firms usually expand and hire more employees, in some cases, firms may actually reduce their number of employees and over time increase their competitiveness, earnings, and profits.
Payroll data provide another useful tool to help identify key industries in your region and serve as a complement to employment data. Although an industry may employ a high percentage of workers, it may not offer those workers high wages or it may only hire employees on a seasonal, part-time, or temporary basis.
Data generally do not distinguish among industries that pay low wages, those that require seasonal or temporary work, and those that do both. However, you can conclude that those industries with large numbers of employees, large payrolls, and, thus, high pay per employee are more important to the region than industries with neither large numbers of employees nor large payrolls.
Another way to look at how well various industries pay their employees is to examine wage data by industry, which is not as widely available through federal data sources as payroll data. However, many state economic development offices will have this data. For access to and information on the various types of wage data available, see our Data Resources by Subject or the State Economic Development Data Sources sections of this site.
By looking at the total payroll and the payroll per employee, you can get a better idea of the quality of jobs that various industries in the region offer. Although determining job quality is a subjective exercise, most would agree that high-wage jobs are important and regional industries are very valuable.
In this Web site, four ways of looking at payroll are provided: total payroll in the region, payroll per employee, change in payroll per employee, and a national comparison. The national comparison is simply the region's average payroll per employee divided by the national average payroll per employee. This comparison offers insight into both specific industries and the region's economy.
Total payroll by industry
This part is easy. Copy or download payroll data for each industry in your region.
What does this mean?
It is interesting to note and appreciate in most cases the sheer size of each industry's payroll. It is also a useful exercise to examine with income multipliers how that money is recirculated into the regional economy. For more information on multipliers, go to Input-Output Analysis.
Payroll per employee offers a measure for comparing average wages and salaries of a region's industries. Although there are a variety of limitations of such analysis, payroll per employee by industry provides another useful lens through which to view and compare industries.
Although Understanding Your Industries uses payroll and employment data to calculate per employee estimates, a variety of other wage and salary data are available, including weekly and hourly earnings by industry and occupation, including production workers. These data are also available from the Bureau of Labor Statistics and many state employment security agencies. To access data that may be available for your state go to: Data Sources by State/National Data sources.
How to calculate payroll per employee for each industry:
The data you need:
Equation:
Payroll per Employee = (Industry's Regional Payroll)/(Industry's Regional Employment)
What does this mean?
An industry that employs a large number of workers, yet has a relatively low average payroll per employee, likely plays a different role in the economy than an industry with fewer employees but a high average payroll per employee. The former may pay low wages, employ a large number of part-time workers, or both. Conversely, the latter may pay high wages, employ a large number of full-time employees, or both.
Comparing payroll per employee with the industry growth analysis completed in Step 2, provides another dimension for analyzing the data. High-growth industries with high average payrolls per employee will likely command more attention in an economic development strategy than low-growth, low average payroll per employee industries.
Caution!
Although it would be preferable to base payroll analysis on full-time equivalent (FTE) employees, that type of data is not readily available for every region. Instead, the data used in this calculation lumps full- and part-time workers together and determines an average payroll for them. The result is that the average wage for industries with high numbers of part-time people may be lower than those with a higher percentage of full-time employees. The reality may be, however, that the full-time employees in the industries that employ large numbers of part-time workers actually make considerably more than their counterparts in the other industries.
Another useful measure is to examine how average payroll per employee has changed over time. As is the case with the employee data, a review of changes in payroll may help lead to a better understanding of how an industry's presence in the region and its internal structure may be changing.
How to calculate changes in payroll per employee:
The data you need:
Equations:
Calculate the payroll per employee for each industry using the base year data.
(Use calculation from Step 5B)
Percent Change in Industry Payroll per Employee =
[(Most Recent Year Payroll per Employee)/(Base Year Payroll per Employee)-1]
What does this mean?
Although the results of this analysis may raise more questions than they answer, these questions may lead to the acquisition of key insights into specific industries. For example, if an important industry's average payroll per employee dropped significantly, you may want to explore why this occurred. Was it due to changing employment patterns (e.g., a switch to more part-time workers)? Within a larger industry classification (e.g., transportation equipment), did an industry or industries (e.g., boat and motorcycle manufacturers) that may have once paid higher wages close or contract?
A final way to analyze the payroll data is to compare the average regional payroll per employee with the national average for each industry. The calculation provides a starting point for further analysis of both individual industries and the regional economy as a whole. With respect to specific industries, the comparison will identify those industries that pay more or less than similar industry groupings nationally. The analysis also provides a broader picture of how average regional pay generally compares with the national average.
The comparison is simply the region's percentage of national average payroll per employee for each industry. To calculate the percentage for each industry, the industry's regional payroll per employee is divided by the industry's national payroll per employee. The result is multiplied by 100 to provide a percentage.
How to calculate the region's percentage of national average payroll per employee for each industry:
The data you need:
Equations:
Calculate the national average payroll per employee for each industry.
(Use the equation included in Step 5B)
Region's Percentage of National Avg. Payroll per Employee =
[(Regional Payroll per Employee)/(National Payroll per Employee)-1]
Example
In the example below, when the regional payroll per employee for industry A of $12,000 is divided by the national payroll per employee of $15,000, the result is 0.8. Multiply by 100 to get 92 percent. Thus, the average regional payroll per employee is only 80 percent of the national average for that industry. In industry B, the regional payroll per employee is 106 percent that of the national average for the industry.
What does this mean?
The results provide a very general picture of how average pay in the region compares with that of the nation. If most of the percentages you calculate are less than or more than the national average, you might conclude that wages in the region generally trail or exceed those of the nation as a whole. However, those industries that buck the trend are worth exploring further to understand what it is about the regional economy that causes this discrepancy. For example, is it the mix of industries within the industry classification? Or, is it that the regional industry employs more full- or part-time employees than the national average? Is the higher or lower than average pay a selling point for the region?
Caution!
The payroll data and comparisons offer a very rough picture of what is happening in terms of wages in the region. Industries that pay very well may be masked if highly aggregated data are used. The comparison also implies a certain level of consistency among industries within each industry grouping and that the regional industry has the same structure as its national counterpart. As long as you are mindful of the many limitations the payroll data entail, then using them in conjunction with other measures and as a first step to a better understanding of regional industries makes sense.
Earnings data are another useful source of information on a region's businesses. Earnings by place of work data are a measure of economic output generated within a region regardless of where employees actually live and serve as a proxy for gross regional product or output. Earnings data also serve as a proxy for gross regional product or output. Thus, earnings are a useful measure of the total size of the regional economy as well as a source of information on the size of specific industries. Earnings data are also a good complement to employment data.
Change in earnings by place of work
As with other data, it is useful to examine how earnings by place of work have changed over time. This analysis will be particularly useful when compared with employment and payroll data for the same time period.
How to calculate change in earnings by place of work:
The data you need:
Equation:
Percentage Change in Earnings by Place of Work for each Industry =
[(Most Recent Year Earnings by Place of Work) / (Base Year Earnings by Place of Work)-1]*100
Note: If you are looking at your region's manufacturing industries, use only national manufacturing data to determine the growth rate. Do not use the growth rate for employment in all sectors. If you are looking at a combination of sectors (e.g., manufacturing, services and retail), use the total employment for those combined sectors to determine the growth rate. To double check your work, your individual industry data should equal the total you use to determine the growth rate.
The number of firms in an industry provides an idea of whether economic activity in a given industry is concentrated in one, two, or several firms. If a key industry in your area is concentrated in one or two large firms, the region is obviously quite dependent on those firms and has a strong interest in ensuring their success and keeping them in the area. Complementary industries might be a focus of recruitment efforts. Likewise, if the industry appears to be based in a large number of small firms, then local policy might focus on creating a positive environment for small businesses.
Employment and payroll data can help reveal general trends and patterns within your economy's industries. In many cases, these tools provide a level of detail that is more than adequate for the analyst's needs. However, in situations where additional detail is needed, particularly about the transactions between individual industries in an economy, input-output accounts can provide this information. Input-output analysis is a very data and time intensive process, so analysts use it only when they need a very detailed picture of inter-industry linkages in an economy. Many states and regions use input-output analysis to try to understand the economic impact of various programs, projects, and industry gains or losses. A region might use input-output analysis to examine the economic impact of any number of projects or changes, such as the impact of efforts to boost tourism, the addition of a new manufacturing plant to the area, the development of a new airport or freeway, the loss of a professional sports franchise, or massive flooding.
Input-output analysis utilizes multipliers that take into account inter-industry relationships. These relationships consist of each industry's distribution of inputs purchased from and sold to other industries, including government. Input-output multiplier tables allow a user to examine how important economic measures such as output, employment, and earnings will be affected by increases or decreases in the final demand of an industry or series of industries.
Two sources of input-output multipliers and analysis are the Bureau of Economic Analysis (BEA) and IMPLAN, both of which are summarized below.
RIMS II (Regional Input-Output Modeling System)
Bureau of Economic Analysis
Department of Commerce
Special tabulations are prepared from the Regional Input-Output Modeling System (RIMS II) of regional economic multipliers for any combination of counties on a reimbursable basis. These tabulations can be used in analyzing the economic effects of events, such as the conversion of military bases and the expansion of airports. Long-term and mid-term projections of personal income and gross state product, employment, and earnings by industry for states, as well as long-term projections of employment and earnings for metropolitan areas and BEA economic areas are prepared for use by planners and marketing analysts.
For more information on BEA's input-output multipliers or RIMS II, visit the Regional Economic Accounts website.
IMPLAN
The Minnesota IMPLAN Group (MIG) provides software and data sets for use in economic modeling and marketing analysis. They have data for every state and county in the United States and can create ZIP code level data on request. IMPLAN's input-output multipliers can be used in such applications as:
Further information on this data source can be obtained at IMPLAN's Web site or by contacting:
Minnesota IMPLAN Group
1940 South Greeley Street, Suite 101
Stillwater, MN 55082-6059
Voice: 612/439-4421
Fax: 612/439-4813
Organizing and Interpreting Your Results
Each of the analytical tools discussed in Understanding Your Industries provides insight into a different facet of your local industry mix. In order to gain the fullest picture of your local industries, the results from each of the different measures should be examined and compared. A spreadsheet program helps to organize your analysis. Using data for Stearns County, Minnesota, we have prepared a spreadsheet and analytical discussion as examples of how you might interpret the data and results you generated and develop a picture of the county's key industries. The discussion section can also be viewed online.
Do not forget, however, that no measure is perfect. The caution statements in each section of "Understanding Your Industries" give some of the common pitfalls in each type of analysis. In addition, it is also important to bear in mind that the data used in this sample do not correspond exactly with a business cycle, so some of the trends may reflect cyclical changes in the economy.
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