Shift share analysis

What Is Shift Share Analysis?

Shift share analysis is a descriptive tool in regional economics that helps explain changes in a local economy's performance, typically in terms of employment data or output data, by breaking down the changes into components related to broader economic growth trends. This economic indicator is primarily used to understand how much of a region's change is due to overall national economic trends, the specific mix of its industry sectors, and its unique competitive advantage or disadvantage. Shift share analysis helps regional analysts and policymakers identify the underlying drivers of economic change within a specific geographical area, such as a city, county, or state.

History and Origin

Shift share analysis, also known as shift-share, traces its roots back to the early 1940s, with initial development attributed to Daniel Creamer.²² The technique was later formalized by Edgar S. Dunn in 1960.²¹ Dunn's work helped establish shift share analysis as a systematic method for examining changes in economic variables over time. Since its formal inception, it has become a popular and relatively simple technique used by regional scientists, urban planners, and economists to analyze employment growth and other economic changes within regions compared to the broader national economy.¹⁹, ²⁰

Key Takeaways

• Shift share analysis decomposes regional economic change into three primary effects: national growth, industrial mix, and local share.
• It helps identify whether a region's economic performance is due to national trends, its specific industry composition, or its unique local factors.¹⁸
• The analysis is widely used in economic development and regional planning to highlight areas of strength or weakness.¹⁷
• Shift share analysis is a descriptive tool that explains what has happened, rather than why it happened, or providing a forecast of future changes.¹⁵, ¹⁶

Formula and Calculation

The regional change in an economic variable for a specific industry (e.g., employment in a particular industry in a region) can be broken down into three main components in a traditional shift share analysis:

1. National Growth Effect (NG): This component measures the change in a regional industry that would have occurred if it had grown at the same rate as the overall national growth rate across all industries.

   $$NG_i = E_{i,t} \times \left(\frac{E_{N,t+n} - E_{N,t}}{E_{N,t}}\right)$$

   Where:

   • (NG_i) = National Growth Effect for industry (i)
   • (E_{i,t}) = Employment in industry (i) in the region at time (t)
   • (E_{N,t}) = Total national employment at time (t)
   • (E_{N,t+n}) = Total national employment at time (t+n)

2. Industrial Mix Effect (IM): This component accounts for the portion of regional change attributed to whether the region has a concentration of industries that are nationally fast-growing or slow-growing.

   $$IM_i = E_{i,t} \times \left(\frac{E_{N,i,t+n} - E_{N,i,t}}{E_{N,i,t}} - \frac{E_{N,t+n} - E_{N,t}}{E_{N,t}}\right)$$

   Where:

   • (IM_i) = Industrial Mix Effect for industry (i)
   • (E_{i,t}) = Employment in industry (i) in the region at time (t)
   • (E_{N,i,t}) = National employment in industry (i) at time (t)
   • (E_{N,i,t+n}) = National employment in industry (i) at time (t+n)
   • (E_{N,t}) = Total national employment at time (t)
   • (E_{N,t+n}) = Total national employment at time (t+n)

3. Local Share Effect (LS) / Regional Competitive Effect (RC): This component reflects the change in a regional industry due to unique local factors, such as local comparative advantage, policy, or business environment, that cause it to grow faster or slower than the same industry nationwide.

   $$LS_i = E_{i,t} \times \left(\frac{E_{R,i,t+n} - E_{R,i,t}}{E_{R,i,t}} - \frac{E_{N,i,t+n} - E_{N,i,t}}{E_{N,i,t}}\right)$$

   Where:

   • (LS_i) = Local Share Effect for industry (i)
   • (E_{R,i,t}) = Regional employment in industry (i) at time (t)
   • (E_{R,i,t+n}) = Regional employment in industry (i) at time (t+n)
   • (E_{N,i,t}) = National employment in industry (i) at time (t)
   • (E_{N,i,t+n}) = National employment in industry (i) at time (t+n)

The total change in regional employment for an industry ((\Delta E_{R,i})) is the sum of these three components:

Interpreting Shift Share Analysis

Interpreting the results of a shift share analysis involves examining the signs and magnitudes of each component to understand the forces driving a region's economic change.

• A positive National Growth Effect indicates that the region benefited from overall national economic expansion. This suggests that even if the region's industrial mix or local conditions were average, it would have still experienced growth due to the rising tide of the national economy.
• The Industrial Mix Effect reveals whether the region specializes in fast-growing or slow-growing national industries. A positive industrial mix suggests the region has a favorable sectoral composition, while a negative one indicates a concentration in declining or slow-growing sectors nationally.
• The Local Share Effect, often considered the most insightful, indicates the region's performance metrics relative to national trends within specific industries. A positive local share points to unique regional strengths, such as a strong labor force, innovation, or supportive local policies, which allow its industries to outperform their national counterparts. Conversely, a negative local share implies local disadvantages that cause industries to lag.¹⁴

By analyzing these components, stakeholders can gain a nuanced perspective on a region's economic performance.

Hypothetical Example

Consider a hypothetical region, "Techville," and its software development industry sectors over a five-year period.

• Initial Employment (Techville Software, Year 1): 10,000 employees
• Final Employment (Techville Software, Year 5): 12,000 employees
• Total Change in Techville Software Employment: +2,000 employees

Now, compare this to national trends:

• Overall National Employment Growth Rate (all industries): +5%
• National Software Development Industry Growth Rate: +15%

Let's calculate the components of the shift share analysis for Techville's software development industry:

1. National Growth Effect (NG): This shows how many jobs Techville's software sector would have gained if it just kept pace with the overall national economy.

   • (NG = 10,000 \times 0.05 = 500 \text{ jobs})
   • Techville's software industry would have gained 500 jobs if it had simply grown at the overall national rate.

2. Industrial Mix Effect (IM): This quantifies the effect of Techville having a concentration in software development, which is a nationally fast-growing industry.

   • National Software Industry Growth Rate (15%) - Overall National Growth Rate (5%) = 10%
   • (IM = 10,000 \times 0.10 = 1,000 \text{ jobs})
   • Techville gained an additional 1,000 jobs because its software industry is part of a nationally fast-growing sector.

3. Local Share Effect (LS): This captures Techville's unique regional competitiveness in software development.

   • Techville Software Growth Rate: ((12,000 - 10,000) / 10,000 = 20%)
   • Local Software Growth Rate (20%) - National Software Industry Growth Rate (15%) = 5%
   • (LS = 10,000 \times 0.05 = 500 \text{ jobs})
   • Techville gained an extra 500 jobs due to its specific local strengths in the software sector, beyond what national and industrial mix trends would suggest.

Summary:

• Total observed growth: +2,000 jobs
• Sum of effects: (500 \text{ (NG)} + 1,000 \text{ (IM)} + 500 \text{ (LS)} = 2,000 \text{ jobs})

This analysis indicates that Techville's software industry growth was influenced positively by all three factors: overall national growth, the favorable national trend of the software sector (its industrial mix), and its own strong local competitive environment.

Practical Applications

Shift share analysis is a versatile tool widely used in various fields of economic analysis and planning:

• Regional Economic Development: Government agencies and economic development organizations use shift share analysis to understand the dynamics of local economies, identify industries with a competitive advantage, and formulate targeted strategies for growth or revitalization. For instance, universities and extension services often publish guides on its application for community economic assessment.¹³
• Industry Targeting: It helps pinpoint which industry sectors in a region are outperforming national trends, indicating local strengths that could be further supported or attracted.
• Policy Evaluation: Policymakers can employ shift share analysis to assess the effectiveness of past initiatives by observing how regional industries responded to policy changes relative to broader economic forces.
• Labor Market Analysis: By applying shift share analysis to employment data, analysts can uncover the drivers of job creation or loss within specific industries and regions, informing workforce development programs.
• Strategic Planning: Businesses and investors can use shift share analysis to evaluate the health and potential of regional markets, influencing location decisions or investment strategies. Data for such analyses is often readily available from government sources like the Federal Reserve Economic Data (FRED) database, maintained by the Federal Reserve Bank of St. Louis.¹²

Limitations and Criticisms

Despite its widespread use, shift share analysis has several limitations and has faced criticism:

• Accounting Identity, Not Causal Model: A primary criticism is that shift share analysis is an accounting identity, meaning it decomposes observed change but does not explain the underlying causal relationships. It tells "what" happened, not "why" or "how."¹⁰, ¹¹ For example, a positive local share effect might indicate regional competitiveness, but it does not identify the specific factors (e.g., skilled labor, tax incentives, infrastructure) that created that advantage.⁹
• Lack of Theoretical Basis: Some critics argue that the method lacks a robust theoretical foundation in economic theory, making it primarily a descriptive tool rather than an analytical one that can predict future trends.⁷, ⁸ It is based on past growth and cannot inherently predict future changes.⁶
• Sensitivity to Base Year and Aggregation: The results can be sensitive to the choice of the initial and final years of the study period and the level of industry aggregation used. Different periods or more detailed industry breakdowns can yield different conclusions about regional performance.⁴, ⁵
• Ignores Inter-Industry Linkages: The traditional model does not explicitly account for complex inter-industry relationships, supply chains, or multiplier effects within a region. Changes in one industry can have ripple effects on others that are not captured by the independent components.³
• Implicit Assumptions: The model implicitly assumes that regional industries operate in isolation and that regional technology, labor productivity, and demand patterns are similar to national averages, which may not hold true in reality.²

Due to these limitations, shift share analysis is often best used in conjunction with other structural analysis techniques and qualitative insights into local conditions.

Shift Share Analysis vs. Input-Output Analysis

Shift share analysis and input-output analysis are both tools used in economic analysis, but they serve different purposes and operate on different principles.

Shift share analysis is a decompositional technique that disaggregates historical economic change within a region into national, industrial mix, and local competitive components. Its primary aim is to show how a region's growth compares to a larger reference economy and what factors (broader trends, industry composition, local competitiveness) contributed to that observed change over a specific period. It is backward-looking and descriptive, focusing on employment or output changes.

In contrast, input-output analysis is an interdependency modeling technique that quantifies the economic relationships between various industry sectors within an economy. It uses a matrix to show how the output of one industry serves as an input for others. This method is used to estimate the total economic impact (direct, indirect, and induced) of a change in demand for a particular sector's output. It is primarily forward-looking and used for forecasting or impact assessments, focusing on the flow of goods and services and multiplier effects throughout an economy. While shift share explains components of historical change, input-output analysis explains the potential ripple effects of new activity or shocks.

FAQs

What kind of data is needed for shift share analysis?

Shift share analysis primarily requires historical employment data or output data for specific industry sectors within a defined region, as well as the corresponding data for a larger reference economy (e.g., the nation) over the same time period.

Can shift share analysis predict future economic growth?

No, shift share analysis is a descriptive tool based on past trends and is not designed for forecasting economic growth. It explains why historical growth occurred, but it does not account for future events, policy changes, or shifts in business cycles that could influence future performance.¹

How does shift share analysis help with regional planning?

Shift share analysis helps regional planners understand the strengths and weaknesses of their local economy. By identifying which industries are growing due to local competitive advantage versus national trends, planners can tailor strategies to foster local strengths, address competitive disadvantages, and promote sustainable economic development.

What is the competitive effect in shift share analysis?

The competitive effect, also known as the local share effect or regional shift, measures the portion of an industry's growth or decline in a region that is attributable to factors unique to that region. If positive, it suggests local advantages (e.g., skilled labor, efficient infrastructure); if negative, it indicates local disadvantages relative to national trends for that industry.