Shift share is a standard regional analysis method that attempts to determine how much of regional job growth can be attributed to national trends and how much is due to unique regional factors. Shift share helps to answer the question “Why is employment growing or declining in this regional industry, cluster, or occupation?”
Shift share analysis looks at national and regional trends over a certain timeframe and asks, “If the region had just followed national trends (such as percent job growth) during this time, what would it have looked like at the end?” It then compares this picture of “expected” change to the region’s actual change during that time. The difference between the two is one measure of regional performance.
Shift share analysis is practical because it provides context for regional job growth. Just knowing that the health care industry is growing in your area does not tell you how your area stacks up to the national average in health care industry growth. Conversely, just knowing that a certain manufacturing industry has declining employment in your area would not tell you whether it is declining more quickly or slowly than national trends.
Shift Share Components
To help make the following explanation clearer and more concrete, we’ll assume the following facts as the basis of a shift share scenario:
- The national economy grew by 4% (total employment) in the given timeframe.
- The Employment Services industry grew by 15% nationally, and by 350 jobs regionally. It had 1000 total jobs regionally at the beginning of the given timeframe.
- The Apparel Manufacturing industry declined by 5% nationally and by 80 jobs regionally. It had 200 total regional jobs at the beginning of the given timeframe.
The National Growth Effect
The national growth effect explains how much of the regional industry’s growth is explained by the overall health of the national economy: if the nation’s whole economy is growing, you would generally expect to see some positive change in each industry in your local region (the proverbial “a rising tide lifts all boats” analogy).
So if the entire national economy grew at a rate of 4%, we might have expected the regional Employment Services industry would also grow by 4%, or 0.04 * 1000 = 40 jobs. These 40 jobs are the national growth effect for Employment Services. For Apparel Manufacturing, the national growth effect is 0.04 * 200 = 8 jobs, meaning that we might have expected it to grow by 8 jobs over the time period simply because of general economic growth.
The Industrial Mix Effect
The industrial mix effect represents the share of regional growth explained by that industry’s growth at the national level. To arrive at this number, the national growth rate of the total economy is subtracted from the national growth rate of the specific industry, and this growth percentage is applied to the regional jobs in that industry.
In our example, Employment Services grew by 15% nationally, but we subtract the 4% growth of the national economy to arrive at a national industry-specific 11% growth rate for Employment Services (the industry’s national growth that exceeded overall trends). Applied to the regional industry, we would thus have expected Employment Services to grow by (0.11 * 1000) = 110 jobs due to industry-specific trends at the national level. Similarly, we get a national industry-specific relative growth rate of (-5% – 4%) = -9% for Apparel Manufacturing (i.e., the industry not only declined 5% nationally but failed to grow 4% with the rest of the nation), meaning we would have expected a regional loss of (0.09 * 200) = 18 jobs due to national industry-specific trends.
The Regional Competitiveness Effect
The regional competitiveness effect is the most important of the three indicators, as it explains how much of the change in a given industry is due to some unique competitive advantage that the region possesses, because the growth cannot be explained by national trends in that industry or the economy as whole. This effect is calculated by taking the total regional growth and subtracting the national growth and industrial mix effects. Note that this effect can be higher than actual job growth if national and/or industry mix effects are negative while regional growth is positive. This is because the regional competitiveness effect accounts for jobs “saved” from declining national trends as well as new jobs created.
So in our example, Employment Services grew by 350 jobs regionally, but 40 of those jobs might have been expected due to national trends in the economy as a whole, while 110 jobs might have been expected due to national trends in Employment Services specifically. This makes a total of 150 jobs expected from national trends. Since the actual growth was 350 jobs, (350 – 150) = 200 jobs cannot be explained by national trends, and so they must be attributed to unique conditions and advantages that the region possesses which contribute to the growth of this specific industry.
For Apparel Manufacturing, we might have expected a net change of (8 + (-18)) = -10 jobs regionally, while in fact there was a regional change of -80 jobs. The regional competitiveness effect is thus (-80 – 10) = -90 jobs, indicating that it fell short of the expected change by 90 jobs due to some specific conditions in the region, such as the closing of a factory.
Using Shift Share Analysis
Shift share is similar to location quotient in that it highlights the uniqueness of a regional economy, but it does so in terms of job growth rather than total jobs in an industry. Industries with high regional competitiveness effects highlight the region’s competitive advantages or disadvantages. Shift share does not indicate why these industries are competitive—that is the job of analysts who have knowledge of local conditions. Shift share merely shows the sectors in which the region is out-competing or under-competing the nation. Shift share is thus useful in identifying investment targets so that local stakeholders can help high-performing regional industries either continue to outperform national trends or else “catch up” with national trends so that the regional economy is not left behind in those sectors.
The basic use of shift share is to prevent a hasty and inaccurate interpretation of raw job growth or decline numbers:
- An industry may be booming in a region, but shift share reveals that the industry is actually growing even faster at the national level, showing that regional factors probably have little influence on the regional boom. Or, shift share may reveal a national decline in that industry, showing a unique regional advantage in that industry that ought to be identified and fostered.
- An industry may be declining in a region, but shift share reveals that it is declining even faster at the national level—and thus the regional industry is actually outperforming the nation by stemming job loss. Or, the industry may be growing nationally, indicating that the region faces some disadvantage that is causing localized job loss in a nationally growing industry. If it is significant, this disadvantage should be investigated further.
Analyst primarily uses shift share as a way of estimating “regional competitiveness” in terms of an industry or occupation. If the region’s industry X is outperforming national trends in all industries and industry X in particular, this indicates some competitive advantage in the region that is helping industry X to flourish.
One practical application of shift share is in the context of a workforce training grant application. Suppose a community’s health care sector has a high regional competitiveness effect, meaning that it is experiencing an above-average growth rate. They might use this as an argument for additional state or federal funding for health care workforce training/education programs.
Another example: in economic development, shift share can help business recruiters identify and strengthen the region’s competitive advantages. For example, a shift share analysis might reveal that the aerospace parts manufacturing industry in the region, though small, has a high competitive effect. This is a clue that the region is friendly to this type of industry. After further research into the reasons for this competitive advantage, researchers would be equipped to cultivate this advantage and create focused campaigns to attract more businesses of that type.
A final example: a negative competitive effect can indicate that a business is a flight risk for the region. Because shift share describes growth or decline using the context of national performance, it’s likely that the business with low competitiveness in your region could do better elsewhere. Identifying those businesses and addressing their problems early on can aid in retention efforts.
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