Shift-Share Analysis
Introduction
Shift-Share Analysis (SSA) is a quantitative analytical method used in regional economics to dissect the components of regional economic growth. This technique helps to understand how much of the regional economic change can be attributed to national trends, industry mix, and regional competitive factors. SSA is particularly useful for policymakers and economists to identify the strengths and weaknesses of a region's economy and to formulate strategies for economic development.
Shift-Share Analysis decomposes regional economic growth into three main components: the national growth effect, the industry mix effect, and the regional competitive effect. Each of these components provides insights into different aspects of economic performance.
National Growth Effect
The national growth effect measures the portion of regional economic change that can be attributed to the overall growth of the national economy. This component assumes that if a region's industries were to grow at the same rate as the national economy, the regional growth would reflect this national trend. It is calculated by applying the national growth rate to the region's initial economic size.
Industry Mix Effect
The industry mix effect isolates the impact of the region's industrial structure on its economic growth. This component evaluates whether the region's mix of industries is more or less favorable compared to the national average. It is determined by comparing the growth rates of specific industries within the region to the national growth rates of those same industries. A positive industry mix effect indicates that the region has a concentration of industries that are growing faster than the national average, while a negative effect suggests the opposite.
Regional Competitive Effect
The regional competitive effect, also known as the local share effect, captures the unique competitive advantages or disadvantages of the region. This component measures the extent to which regional factors, such as local policies, resources, and business environment, contribute to the economic performance of the region. It is calculated by comparing the actual growth rate of each industry in the region to the national growth rate of that industry, after accounting for the national growth effect and industry mix effect.
Methodology
The methodology of Shift-Share Analysis involves several steps to decompose regional economic changes into the three components mentioned above. The following outlines the process:
Data Collection
The first step in SSA is to collect relevant economic data for the region and the nation. This typically includes employment, output, or income data for various industries over a specific time period.
Calculation of National Growth Effect
The national growth effect is calculated by applying the national growth rate to the initial economic size of the region. This provides an estimate of how much the region's economy would have grown if it had followed the national trend.
Calculation of Industry Mix Effect
The industry mix effect is determined by comparing the growth rates of specific industries within the region to the national growth rates of those industries. This involves calculating the difference between the national growth rate of each industry and the overall national growth rate, then applying this difference to the initial economic size of each industry in the region.
Calculation of Regional Competitive Effect
The regional competitive effect is calculated by comparing the actual growth rate of each industry in the region to the national growth rate of that industry, after accounting for the national growth effect and industry mix effect. This involves subtracting the national growth effect and industry mix effect from the actual growth of each industry in the region.
Summation and Interpretation
The final step is to sum the national growth effect, industry mix effect, and regional competitive effect to obtain the total regional economic change. The results are then interpreted to identify the key drivers of regional economic performance and to inform policy decisions.
Shift-Share Analysis has a wide range of applications in regional economic planning and policy-making. Some of the key applications include:
Identifying Growth Sectors
SSA helps to identify which industries are driving regional economic growth and which are lagging behind. This information is crucial for policymakers to target specific sectors for development and support.
Evaluating Regional Competitiveness
By isolating the regional competitive effect, SSA provides insights into the unique advantages or disadvantages of a region. This can inform strategies to enhance regional competitiveness, such as improving infrastructure, workforce development, and business environment.
Informing Economic Development Strategies
SSA can guide the formulation of economic development strategies by highlighting the strengths and weaknesses of the regional economy. Policymakers can use this information to design targeted interventions to stimulate growth and address challenges.
Monitoring Economic Performance
SSA is a valuable tool for monitoring the economic performance of a region over time. By regularly conducting SSA, policymakers can track changes in the regional economy and adjust policies accordingly.
While Shift-Share Analysis is a powerful tool, it has several limitations that should be considered:
Static Analysis
SSA is a static analysis that compares economic changes over a specific time period. It does not account for dynamic factors such as technological advancements, changes in consumer preferences, or shifts in global markets.
Aggregation Bias
SSA relies on aggregated data, which can obscure important variations within industries or regions. This can lead to misleading conclusions if the data does not accurately reflect the underlying economic dynamics.
Assumption of Homogeneity
SSA assumes that industries within a region are homogeneous and grow at the same rate as their national counterparts. This assumption may not hold true in practice, as regional industries can have unique characteristics and growth patterns.
Lack of Causality
SSA identifies correlations between regional economic changes and national trends, industry mix, and regional competitiveness, but it does not establish causality. Other factors, such as government policies, natural resources, and external shocks, can also influence regional economic performance.
To address some of the limitations of traditional SSA, several advanced techniques have been developed:
Dynamic Shift-Share Analysis extends the traditional SSA by incorporating time-varying factors and trends. This approach provides a more comprehensive understanding of regional economic dynamics and can capture the effects of technological changes, policy interventions, and other temporal factors.
Spatial Shift-Share Analysis considers the spatial relationships between regions and industries. This technique accounts for spillover effects, regional interdependencies, and spatial autocorrelation, providing a more nuanced analysis of regional economic performance.
Decomposition of Regional Competitive Effect
Advanced SSA techniques decompose the regional competitive effect into finer components, such as productivity changes, labor market dynamics, and innovation. This allows for a more detailed examination of the factors driving regional competitiveness.
Case Studies
Several case studies illustrate the practical applications and insights gained from Shift-Share Analysis:
Case Study 1: The Rust Belt
Shift-Share Analysis has been used to analyze the economic decline of the Rust Belt region in the United States. The analysis revealed that the negative industry mix effect, due to the concentration of declining manufacturing industries, and the adverse regional competitive effect, driven by outdated infrastructure and labor market challenges, were key factors in the region's economic downturn.
Case Study 2: Silicon Valley
In contrast, SSA of Silicon Valley highlighted the positive industry mix effect, with a high concentration of rapidly growing technology industries, and a strong regional competitive effect, supported by innovation, skilled workforce, and venture capital. These factors contributed to the region's robust economic growth.
Case Study 3: European Union Regional Policy
SSA has been employed to evaluate the impact of European Union regional policies on economic convergence among member states. The analysis showed that regions receiving targeted investments and support experienced positive regional competitive effects, leading to improved economic performance and reduced disparities.
Conclusion
Shift-Share Analysis is a valuable tool for understanding the components of regional economic growth and informing policy decisions. By decomposing economic changes into national growth, industry mix, and regional competitive effects, SSA provides insights into the drivers of regional performance and helps to identify opportunities and challenges. While it has limitations, advanced techniques and case studies demonstrate the continued relevance and utility of SSA in regional economic analysis.