# Shift Share Analysis

## INTRODUCTION

Shift Share Analysis is another tool economic development folks use to analyze changes in their regional economy. Basically, it looks at several of the factors that contribute to the growth or decline in a region, breaking the actual change into three parts (Blair, 1995). The good news is that IMPLAN has done the hard work for you. This article will walk you through what the pieces of Shift Share mean, how you can use them, and how they are calculated.

## DETAILS

Shift Share Analysis, also known as Shift & Share, or simply SSA, takes a retrospective look at how a regional economy changed in relation to the nation. In order to dig into a SSA, you need to have a time period, a comparison region, and an industry or occupation you want to analyze.

Using SSA with other tools in the economic development toolbox is a great way to dig deeper into your economy as it helps us understand what changes are happening. We know that our regional economies are not static and tend to have specializations in certain industries or occupations. SSA recognizes that some industries in a local economy are likely to be growing at a faster rate when compared to the reference region, while others are shrinking. It also helps clarify what is behind the net change in employment and which industries in the regional economy are competitive in comparison to the national economy.

SSA is often used with Location Quotients (LQ). Location Quotients compare the relative concentration in a specific area to the concentration in a larger region. They show the industry or occupational concentration as it exists in one year. SSA shows the change over a time period of the researchers choosing. It also breaks down the actual growth into three parts: National Growth, Industry Mix, and Competitive Share.

### National Growth

The National Growth component basically shows you the national trend. It explains the growth or decline in your region based on how much the nation overall grew or declined. If the US economy grew at a rate of 3%, the region would also grow at 3%, all else being equal. You might also see this referred to as National Share or simply Share.

Let's assume that in 2019 the local industry had 1,000 jobs and in 2020 it had 1,060 jobs. SSA can help us contextualize these additional 60 jobs. First let's look at the growth in the overall national economy. If the national economy grew at 3%, one would expect that the local industry would have added 1,000 X 3% = 30 jobs if it kept up with national trends. So, our National Growth Effect is 3% (or 30 jobs).

### Industry Mix

The Industry Mix shows the change in a specific industry above and beyond the national average. It is the portion of the regional growth that may be attributed to the given national industry after accounting for the national growth trend for all industries. It indicates how much that industry outperformed or underperformed the economy as a whole nationwide. You may see this referred to as Industrial Mix, Mix Component, or Proportionality Shift.

If the industry growth rate was 5% nationally, one would expect that the local industry would have added 1,000 X 5% = 50 jobs if it kept up with the industry trends nationally. However, we certainly recognize that the national growth rate applies to all industries, so to avoid double counting we can subtract the National Growth Effect from the above calculation to arrive at the Industry Mix Effect: 50 jobs - 30 jobs = 20 jobs. So, when we account for the National Growth Effect the Industry Mix Effect is 20 jobs (or 2%).

### Competitive Share

The third piece explains how local industries changed relative to the US and the national industry. It shows the share of the local growth attributable to the competitive advantage of the region. This represents how much the region over or under performed compared to national average and industry average. This measure is sometimes called Regional Shift, Competitive Component, Competitive Position, or Differential Shift.

If the Competitive Share is 1%, and the local industry had 1,000 jobs, then we would expect that the local industry would have added 1,000 X 1% = 10 jobs due to competitive advantage. These 10 jobs are in addition to the National Growth Effects and the Industry Mix Effect. Essentially this means that the local region was able to grow 1% beyond the expected rate indicated by the national and industry trends.

### Actual Growth

The Actual Growth can be calculated from the data by taking the difference between the two years of data for each industry. In our example, the local industry employed 1,000 jobs in 2019 and 1,060 jobs in 2020, so the growth rate is 6% (or 60 jobs). If we look at the components of SSA, The National Growth Effect was 3% (or 30 jobs), the Industry Mix Effect was 2% (or 20 jobs), and the Competitive Share was 1% (or 10 jobs). Note that if we sum up the three components of SSA we get 3% + 2% + 1% = 6% (or 60 jobs). This is why it’s called a “simple decomposition” technique as it’s breaking down the observed growth over a time period into three parts. Math - it just makes sense.

## THE MATH

Let’s dig into the math, because, why not? Here we will look at Wayne County, Michigan, home of Detroit, between 2010 and 2019. We have three industries here, but that’s just to save space as you can look across every industry to see where your region has an advantage. We also have the same data for the US and a percent change between the two years for each industry.

 Wayne County, MI US 2010 2019 % Change 2010 2019 % Change Cars 17,543 12,299 -30% 91,220 131,757 44% R&D 17,186 23,750 38% 2,134,786 2,927,206 37% Mgt of companies 18,933 25,509 35% 1,985,043 2,679,522 35% TOTAL 883,196 984,072 11% 173,180,700 203,141,000 17%

The National Growth is calculated as the year 1 employment in each industry in Detroit (17,543 for cars) multiplied by the total % change for the US (17%) (Blair, 1995).

Δei = the change in the local economy in industry i
ei1 = regional employment in industry i in time period 1
ei2 = regional employment in industry i in time period 2
US1 = total employment in time period 1
US2 = total employment in time period 2
USi1 = US employment in industry i in time period 1
USi2 = US employment in industry i in time period 2

National Growth Effect

= ei1 * [(US2 / US1) - 1]
= (17,543) * [( 203,141,000 / 173,180,700) - 1]
= 3,035

 National Growth Cars 3,035 R&D 2,973 Mgt of companies 3,275 TOTAL 9,284

The Industry Mix is calculated as the year 1 employment in each industry (17,543) multiplied by the percent change in that industry in the US (44%) less the percent change in all industries in the US (17%).

Industry Mix Effect

= ei1 * [(USi2 / USi1) - (US2 / US1)]
= (17,543) * [(131,757 / 91,220) - (203,141,000 / 173,180,700)]
= 4,761

 Industry Mix Cars 4,761 R&D 3,406 Mgt of companies 3,348 TOTAL 11,516

The Competitive Share is calculated as the year 1 employment in each industry (17,543) multiplied by the percent change locally in that industry less the percent change nationally in that industry.

Competitive Share Effect

= ei * [(ei2 / ei1) - (USi2 / USi1)]
= (17,543) * [(12,299 / 17,543) - (131,757 / 91,220)]
= -13,040

 Competitive Share Cars (13,040) R&D 185 Mgt of companies (48) TOTAL (12,903)

So, the Actual Growth in the car manufacturing industry is, sadly, negative.

= National Growth + Industry Mix + Competitive Share
= 3,035 + 4,761 + -13,040
= -5,244

 National Growth Industry Mix Competitive Share Actual Growth Cars 3,035 4,761 (13,040) (5,244) R&D 2,973 3,406 185 6,564 Mgt of companies 3,275 3,348 (48) 6,576 TOTAL 9,284 11,516 (12,903) 7,896

## FINDING IT IN IMPLAN DATA LIBRARY

You will also see the components of Shift Share throughout in the IMPLAN Data Library. In the Local Area > Regional Industry Compare you can see a comparison of North Carolina and South Carolina for Industry 1 - Oilseed farming. You can change any of the filters to examine your Region and Industry.

## CAUTIONS

There are a few things to keep in mind when using Shift Share Analysis. First, it is often criticized for lacking a theoretical basis. It is also based on past growth over a specific time period, which we know doesn’t necessarily predict the future, unless you know something we don’t. Finally, SSA doesn’t tell us why there is an advantage or disadvantage in certain industries or if further investment and attention would yield similar results.

## REFERENCES

Blair, J.P. (1995). Local Economic Development Analysis and Practice. Thousand Oaks, CA: SAGE Publications.

## RELATED ARTICLES

What can a Location Quotient do for you?

Written June 16, 2021

Updated June 23, 2021