Chapter 8: Economic Impact Assessment

8.1 Introduction to Economic Impact Assessment

Imagine dropping a stone into a calm pond – the initial splash is just the beginning, as ripples spread outward, affecting a much larger area. This is precisely how economic policies and projects function in our interconnected world. When a big company moves into a small town or a government builds a new airport, the impacts extend far beyond the immediate changes, influencing various sectors, communities, and even future generations. Enter Economic Impact Assessment (EIA), a powerful lens that captures these far-reaching consequences. Unlike Cost-Benefit Analysis (CBA), which focuses on direct costs and benefits, EIA casts a wider net, systematically evaluating the full spectrum of economic changes triggered by an intervention. It’s not just about the splash; it’s about understanding every ripple, from direct effects to indirect and induced impacts, providing a comprehensive view of how economic decisions reshape our entire economic landscape.

While CBA asks, “Is this project worth its cost?”, EIA poses a broader question: “How will this project reshape our economic landscape?” EIA considers factors such as job creation, changes in income levels, shifts in business activity, and even alterations in the tax base. This comprehensive approach is crucial for policymakers and stakeholders to understand the full implications of their decisions, especially in an era where local actions can have global repercussions.

So, when do you pull out the EIA toolkit? Here are some prime scenarios:

1. Major infrastructure projects: Think highways, airports, or renewable energy installations.
2. Policy changes: Like tax reforms or new trade agreements.
3. Large-scale events: Hosting the Olympics or a World Expo, anyone?
4. Industry shifts: When a major employer enters or leaves a region.
5. Natural resource management: Decisions about mining, forestry, or fishing can have far-reaching impacts.

In each of these cases, EIA helps policymakers and stakeholders understand the ripple effects across different sectors and communities. It’s particularly crucial in our globalized world, where local actions can have surprising global consequences.

Consider, by contrast, a project where the scope of economic impact is not merely citywide but continental. Ethiopia’s Grand Ethiopian Renaissance Dam (GERD), constructed on the Blue Nile from 2011 onwards, is Africa’s largest hydroelectric facility, with a generating capacity of up to 6.45 gigawatts. For Ethiopia, the EIA questions were about domestic energy security, employment, and the multiplier effects of electrification across a largely unelectrified economy. For downstream neighbours Sudan and Egypt, they were about water flows, agricultural output, and food security.

A computable general equilibrium analysis by Kahsay et al. (2018) found that once fully operational, the dam generates substantial economic benefits across all three Eastern Nile nations, with Sudan’s cumulative GDP gains projected at between $27 billion and $29 billion over the period 2020 to 2060. Those projections come with important caveats: the distribution of benefits is uneven, the impounding phase creates transitional costs for Egypt, and the analysis is sensitive to climate and hydrological scenarios.

The GERD case also offers a cautionary lesson: Ethiopia began construction without publishing a comprehensive formal EIA, drawing significant international criticism. The three affected countries subsequently established a joint International Panel of Experts to conduct retrospective impact assessments — a process that illustrates both the political sensitivity of transboundary EIA and the costs of deferring it. When EIA is conducted after a major infrastructure decision has already been taken, it shifts from informing a choice to managing a controversy.

EIA’s story begins in the mid-20th century, emerging from regional science and input-output analysis. A big shoutout goes to Walter Isard in the 1960s – the guy’s practically the godfather of regional science! His work on spatial economics laid the groundwork for what we do today. The 1970s saw EIA gain popularity, especially in the U.S., as people started eyeing big projects more critically. Fast forward to the digital age, and EIA got a major upgrade. Powerful computers allowed for more complex models, like the fancy computable general equilibrium (CGE) models of the 80s and 90s. Today, EIA is still evolving. We’re talking big data, advanced statistics, and even a sprinkle of behavioural economics to give us an ever-clearer picture of how our economic decisions ripple out into the world.

Key principles and concepts

Economic Impact Assessment (EIA) stands apart from tools like Cost-Benefit Analysis (CBA) and Multi-Criteria Analysis (MCA) in its ambitious scope: to capture insights across the entire economy. To achieve this comprehensive view, EIA employs several key principles and concepts:

Sectoral Analysis. EIA divides the economy into distinct sectors, creating a framework to analyse the flow of impacts. These typically include: Households, Businesses (often subdivided by industry), Government, and Foreign sector (for international transactions).

This sectoral approach allows EIA to trace the ripple effects of economic changes throughout the economy. For instance, a new manufacturing plant doesn’t just impact the business sector; it creates a cascade of effects: Job creation in households, Changes in government tax revenue, Potential export opportunities in the foreign sector

Temporal and Spatial Dimensions. EIA recognises that economic impacts unfold across both time and space:

Temporal Aspects:

• Short-term effects (e.g., immediate boost from construction jobs)
• Long-term impacts (e.g., ongoing changes in regional productivity)
• Time horizons ranging from a few years to several decades

Spatial Considerations:

• Local, regional, and national level impacts
• Spillover effects across administrative boundaries

Example: A new airport might bring immediate economic benefits to its locality, boost regional tourism, and ultimately affect national GDP through increased international trade.

Economic Multipliers. EIA utilizes multipliers to capture how initial economic changes trigger additional activity. These multipliers quantify the ripple effect throughout the economy.

Types of Economic Effects. EIA distinguishes between three types of effects:

• Direct effects: Immediate economic changes from a project or policy
• Indirect effects: Subsequent changes in inter-industry purchases
• Induced effects: Changes in household spending due to income fluctuations
• Leakages and Displacement. EIA accounts for economic activity that may: “Leak” out of the study area and or Displace existing economic activity

Understanding these concepts is crucial for conducting thorough and accurate economic impact assessments. As we explore EIA further, we’ll examine how these principles are applied in practice, the various techniques and models used, and how EIA complements other economic analysis tools.

By mastering EIA, policy analysts gain the ability to provide decision-makers with a comprehensive view of how policies and projects can reshape economic landscapes. This holistic understanding is invaluable in our interconnected world, where local actions can have far-reaching consequences across sectors and regions. In the following sections, we’ll delve deeper into EIA methodologies, address common criticisms and limitations, and explore real-world applications of this powerful analytical tool.

8.2 Guide to Applying EIA

In today’s dynamic economic landscape, policymakers and business leaders face the challenge of anticipating the far-reaching consequences of their decisions. Economic Impact Assessment (EIA) emerges as an essential compass, helping navigate the complex currents of economic change. Unlike CBA and MCA, there are multiple tools for how EIA is applied. The key tools include

1. Input-Output Models: These models form the backbone of many EIAs, mapping the interdependencies between different sectors of the economy. They allow analysts to trace how changes in one sector ripple through others.
2. Computable General Equilibrium (CGE) Models: More sophisticated than input-output models, CGE models incorporate price changes and resource constraints, providing a more dynamic view of economic impacts.
3. Econometric Models: These use statistical methods to analyse historical data and project future impacts, often employed for long-term or large-scale assessments.
4. Regional Economic Models: Specialized tools that focus on sub-national economic impacts, crucial for understanding localized effects of policies or projects.

But, for each of the tools a structured process is undertaken following the same 5 steps. Let’s embark on a journey through the EIA process, using the development of a new international airport as our guide. We’ll explore how EIA illuminates the cascading effects across various sectors, quantifies economic ripples, and ultimately shapes informed decision-making. Best Practices for Effective EIA

Step 1: Clearly define the scope and boundaries of the assessment

Defining the scope and boundaries of your Economic Impact Assessment is the crucial first step that sets the foundation for your entire analysis. This involves determining the geographic area, time frame, and economic sectors to be included in your study. It’s essential to be specific about what you’re measuring and what falls outside the purview of your assessment.

Practitioner’s Tip: When defining your scope, consider the ripple effects of your project. While it’s tempting to cast a wide net, remember that broader scope often means increased complexity and data requirements. Strike a balance between comprehensiveness and feasibility. Also, be explicit about any limitations or exclusions in your scope to ensure transparency in your analysis.

Case Study: The country of Aerovia is planning a new international airport to boost tourism and trade. For the EIA, they define the scope as the capital city and surrounding 100-mile radius, projecting impacts over 20 years across aviation, tourism, and related sectors. This focused approach ensures a comprehensive yet manageable analysis of the airport’s potential economic footprint.

Step 2: Use multiple models to cross-verify results

Employing multiple models in your Economic Impact Assessment is crucial for enhancing the reliability and robustness of your analysis. Different models can capture various aspects of economic impacts, and using them in combination helps to validate your findings and identify potential discrepancies. This approach provides a more comprehensive and nuanced understanding of the economic effects under study.

Practitioner’s Tip: When selecting models, consider their strengths and limitations in relation to your specific case. Commonly used models include Input-Output (I-O) models, Computable General Equilibrium (CGE) models, and econometric models. Each has its own assumptions and focus areas. By comparing results across models, you can identify areas of consensus and divergence, leading to a more informed interpretation of your findings.

Case Study: In assessing Aerovia’s airport project, analysts employ both an I-O model and a CGE model. The I-O model highlights immediate sector linkages, while the CGE model captures longer-term economic adjustments. This dual approach provides a more comprehensive view of the airport’s potential impacts, from initial construction boom to long-term structural changes in the regional economy.

Step 3: Conduct sensitivity analyses to understand the impact of key assumptions

Sensitivity analysis is a critical component of Economic Impact Assessment that helps evaluate how changes in key assumptions or input variables affect the overall results. This step involves systematically varying important parameters and observing how these changes impact the outcomes of your assessment. By doing so, you can identify which assumptions are most crucial to your results and understand the range of potential economic impacts under different scenarios.

Practitioner’s Tip: Focus your sensitivity analysis on the most influential and uncertain variables in your model. These might include factors like projected growth rates, multiplier effects, or external economic conditions. Present your results as a range rather than a single point estimate, and be transparent about the assumptions that drive the largest variations in outcomes. This approach not only enhances the credibility of your analysis but also provides decision-makers with a clearer picture of potential risks and opportunities.

Case Study: For Aerovia’s airport project, analysts conduct sensitivity analyses on key variables such as tourist arrival projections and fuel prices. By adjusting these factors within plausible ranges, they demonstrate how the airport’s economic impact could vary under different scenarios. This analysis reveals that while the project remains economically beneficial across most scenarios, its impact is particularly sensitive to changes in international tourism trends.

Step 4: Engage stakeholders throughout the process

Stakeholder engagement is a vital component of Economic Impact Assessment that enhances the relevance, accuracy, and credibility of your analysis. This step involves identifying and involving key stakeholders – such as local businesses, community leaders, government officials, and industry experts – throughout the assessment process. Their input can provide valuable insights, data, and perspectives that might otherwise be overlooked, leading to a more comprehensive and nuanced understanding of potential economic impacts.

Practitioner’s Tip: Develop a stakeholder engagement plan at the beginning of your EIA process. Use a mix of engagement approaches; informing, consulting and collaborating through surveys, interviews, focus groups, and public meetings and social media, to gather and share diverse perspectives. Be sure to engage stakeholders not just in data collection, but also in reviewing preliminary findings and discussing implications. This iterative approach can help validate your assumptions, identify potential issues or opportunities, and build buy-in for your assessment results.

Case Study: In Aerovia’s airport project, the EIA team conducts regular roundtable discussions with local business associations, tourism boards, and community representatives. These engagements reveal concerns about potential displacement of small businesses near the proposed airport site, leading to the inclusion of local economic transition strategies in the impact assessment. This stakeholder input ultimately results in a more balanced and locally relevant analysis of the airport’s potential economic effects.

Step 5: Make a decision and transparently communicate results and limitations

The final step in the Economic Impact Assessment process involves synthesizing your findings, drawing conclusions, and clearly communicating both the results and the limitations of your analysis. This step is crucial for ensuring that decision-makers and stakeholders can make informed choices based on a comprehensive understanding of the potential economic impacts, including areas of uncertainty or limitation in the assessment.

Practitioner’s Tip: When presenting your results, strive for clarity and transparency. Use visual aids like graphs and charts to illustrate key findings, but also provide clear explanations of what these visualizations represent. Be explicit about the assumptions underlying your analysis and any limitations in your data or methodology. Consider presenting your findings in layers of detail – from executive summaries to full technical reports – to cater to different audience needs. Remember, acknowledging limitations doesn’t weaken your analysis; rather, it enhances its credibility and usefulness for decision-making.

Case Study: In concluding the EIA for Aerovia’s airport project, the assessment team presents a comprehensive report to government officials and the public. They clearly outline the projected economic benefits, including job creation and GDP growth, alongside potential challenges such as short-term disruptions to local businesses. The team also transparently communicates the limitations of their long-term projections, particularly regarding global tourism trends. This balanced presentation allows decision-makers to proceed with the airport project with a clear understanding of both its potential benefits and risks, leading to the development of targeted strategies to maximize positive impacts and mitigate potential negative effects.

8.3 Navigating the Complexities: Limitations, Criticisms, and Effective Communication in EIA

Throughout our exploration of EIA, you might have pondered about the accuracy of economic multipliers or the potential for overestimating benefits. These are common concerns that analysts and policymakers grapple with when employing EIA.

Let’s consider a cautionary tale from infrastructure development. In the early 2000s, many cities used EIA to justify large-scale stadium projects, often predicting significant economic boosts to local economies. While these analyses highlighted potential job creation and increased tourism, they sometimes overestimated the long-term economic benefits and underestimated the opportunity costs. It’s akin to forecasting economic growth based solely on the construction phase of a project – the immediate impacts may be clear, but the long-term economic sustainability can be far more complex.

This example underscores one of the key challenges in EIA: the difficulty in accurately predicting long-term economic impacts, especially when dealing with complex, dynamic economic systems. It’s like trying to forecast how a diverse urban economy will evolve over decades – the interactions and feedback loops can be as intricate as the economy itself.

As we delve deeper into the world of EIA, we encounter a landscape fraught with ethical dilemmas and distributional challenges. Imagine an EIA predicting a significant economic boost from a new manufacturing plant. On the surface, the numbers look promising – increased GDP, more jobs, higher tax revenues. But dig a little deeper, and you might find a more complex story. Perhaps the plant’s economic benefits are concentrated among a small group of skilled workers and investors, while the environmental costs are borne by nearby low-income communities. This scenario illustrates the ethical tightrope that EIA practitioners must walk. They must grapple with questions of whose economic interests are being prioritised and how to account for impacts that can’t be easily quantified in dollars and cents.

Moreover, the distributional effects of economic impacts often remain hidden in aggregate figures. An EIA might show a net positive impact for a region, but this could mask stark disparities between different socioeconomic groups or geographic areas. It’s like looking at an average temperature for a country – it tells you something useful, but it doesn’t reveal whether some areas are freezing while others are sweltering. As we use EIA, we must strive to peel back these layers, to understand not just the total impact, but how it’s distributed across society.

Critics of EIA often paint a picture of a tool that promises more than it can deliver, highlighting several key challenges and limitations. They argue that EIA can be like a funhouse mirror, distorting economic realities by overemphasizing benefits and understating costs. The optimistic assumptions baked into many EIAs can lead to a kind of economic mirage – projects that look like oases of prosperity on paper but turn out to be illusions in reality. This potential for overestimation of impacts, if not carefully calibrated, is a significant concern. Critics point to numerous examples of infrastructure projects or policy initiatives that failed to deliver the economic bonanza promised by their initial impact assessments. The accuracy of EIA is further compromised by data limitations, which can affect the reliability of the assessments. Moreover, the assumptions underlying EIA models may not always hold in complex, real-world situations, leading to discrepancies between projected and actual outcomes. Another significant challenge lies in the difficulty of capturing all indirect and induced effects accurately, particularly in interconnected and dynamic economic systems. These limitations underscore the need for careful application and interpretation of EIA results, as well as the importance of complementing EIA with other analytical tools to provide a more comprehensive understanding of potential economic impacts.

Another challenge is the bias that can occur when these tools are developed by special interest groups. It is critically important to understand how the tools were developed, and who has paid for them, as this can create bias that is hard to unpack.

EIA often struggles to capture the nuances of informal economic activities, leading to potential misrepresentations of true economic impact. A classic example is the “housewife paradox”: if a woman marries her housekeeper, GDP technically decreases because the formerly paid service is now part of unpaid household work, despite no real change in economic activity. This illustrates how EIA, tied closely to formal economic measures like GDP, can overlook or misinterpret significant portions of economic value, particularly in economies with large informal sectors or substantial unpaid household labour. In economic terms, this means EIA may overlook informal economic activities, fail to account for opportunity costs, or struggle to quantify long-term and intergenerational impacts. These blind spots can lead to incomplete or misleading assessments, potentially guiding decision-makers down problematic paths.

Tips for Implementation:

1. Embrace Spatial Analysis: Integrate GIS tools into your EIA to map economic impacts across different geographic areas. This can help visualize and address distributional effects, ensuring that your assessment doesn’t just focus on aggregate impacts but also on how they vary spatially.
2. Incorporate Equity Metrics: Develop and include specific indicators to measure how economic impacts are distributed across different socioeconomic groups. This could involve creating an ‘equity impact score’ alongside traditional economic measures.
3. Engage in Participatory Scenario Planning: Instead of relying solely on expert-driven models, involve diverse stakeholders in creating and assessing different economic scenarios. This can bring in perspectives that might be missed in traditional EIA approaches and help address ethical concerns.
4. Utilize Dynamic Modelling: Employ system dynamics or agent-based modelling techniques to capture complex interactions and feedback loops in the economy. This can help address criticisms about EIA’s ability to capture economic complexity.
5. Conduct “Reverse EIAs”: Occasionally, start with desired economic outcomes and work backwards to identify what conditions or policies would be necessary to achieve them. This approach can provide valuable insights and challenge assumptions in traditional EIA methodologies.

As we conclude our exploration of Economic Impact Assessment, it’s crucial to recognise its place within the larger arsenal of economic policy analysis tools. EIA stands as a powerful instrument in our playbook, complementing other approaches like Cost-Benefit Analysis, and Multi-Criteria Analysis. Each tool offers unique insights, and the art of effective policy analysis lies in knowing when and how to deploy them in concert. Looking ahead, the evolution of EIA promises exciting possibilities. As data analytics and machine learning advance, we may see EIA models that can more accurately capture complex economic interactions and long-term effects. The integration of real-time data and improved spatial analysis techniques could enhance our ability to understand distributional impacts. Moreover, as global challenges like climate change and technological disruption reshape economies, EIA methodologies will likely adapt to better account for these systemic shifts. The future of EIA, much like the economies it seeks to understand, is dynamic and full of potential. As policy analysts, our task is to continue refining and expanding this tool, ensuring it remains relevant and robust in an ever-changing economic landscape.

Tying to Economic Policy Analysis

Drop a stone in a pond. The initial splash is immediate and visible ‘; but the ripples spread much further. That’s what EIA is designed to track: the full cascade of economic effects triggered by a policy or project, not just the direct impact. Used well, it’s an indispensable tool for understanding what you’re actually deciding when you approve a major investment. Used carelessly, it produces impressive-looking numbers that significantly overstate the real economic gain. When working with EIA:

• Define your assessment boundary clearly and justify it. Different boundaries produce materially different results ‘; and a generous geographic scope is one of the most common sources of EIA optimism bias.
• Account for displacement and leakage. New jobs in a project area are not automatically net new jobs for the economy as a whole.
• Use multiple models where possible. Where they converge, you have higher confidence; where they diverge, that divergence is itself informative.
• Commission EIA before major decisions are taken, not after. As the GERD case showed, deferring impact assessment shifts the tool’s function from informing a choice to managing a controversy.

Chapter 8: Further Reading & References (Economic Impact Assessment)

Further Reading

Economic Impact Assessment Methodology

Regional Economic Analysis

Isard, W., Azis, I. J., Drennan, M. P., Miller, R. E., Saltzman, S., & Thorbecke, E. (1998). Methods of Interregional and Regional Analysis. Ashgate.

Stimson, R. J., Stough, R. R., & Roberts, B. H. (2006). Regional Economic Development: Analysis and Planning Strategy. Springer.

Advanced Economic Modeling

Dixon, P. B., & Jorgenson, D. W. (Eds.). (2013). Handbook of Computable General Equilibrium Modeling. North Holland.

Rey, S. J., & Anselin, L. (2014). Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press.

References

Ambargis, Z. O., & Mead, C. I. (2012). RIMS II: An essential tool for regional developers and planners. Bureau of Economic Analysis.

Archer, B. H. (1982). The value of multipliers and their policy implications. Tourism Management, 3(4), 236-241.

Burfisher, M. E. (2017). Introduction to Computable General Equilibrium Models. Cambridge University Press.

Crompton, J. L. (1995). Economic impact analysis of sports facilities and events: Eleven sources of misapplication. Journal of Sport Management, 9(1), 14-35.

Destination Canada. (2019). Tourism’s Economic Impact in Canada.

Dwyer, L., Forsyth, P., & Spurr, R. (2006). Assessing the economic impacts of events: A computable general equilibrium approach. Journal of Travel Research, 45(1), 59-66.

Isard, W. (1960). Methods of Regional Analysis: An Introduction to Regional Science. MIT Press.

Leontief, W. W. (1986). Input-Output Economics (2nd ed.). Oxford University Press.

Oosterhaven, J. (1996). Leontief versus Ghoshian price and quantity models. Southern Economic Journal, 62(3), 750-759.

Rose, A., & Liao, S. Y. (2005). Modeling regional economic resilience to disasters: A computable general equilibrium analysis of water service disruptions. Journal of Regional Science, 45(1), 75-112.

SVA Consulting. (2016). Consolidated report on Indigenous Protected Areas following Social Return on Investment analyses.

Walras, L. (1874). Elements of Pure Economics. Routledge.