Aprize Metrics: The Economic Rationale

Aprize’s Purpose

Aprize exists to multiply the local economic impact of our clients.

The Problem We are Addressing

In Indiana, there are 94 community foundations with nearly $4.1 billion in assets at the end of 2023.¹ On top of this, local governments in Indiana spent $34 billion in 2021.

This spending is intended to benefit the respective local communities. However, measuring the economic impact of these micro-level decisions, let alone creating incentive structures to maximize the growth impacts of this spending, is challenging and largely lacking throughout the local government, economic development, and grant-making communities.

Traditional economic growth metrics, such as Gross Domestic Product (GDP) or employment reports, operate at a regional or national level, failing to capture the granular, transaction-level impact of individual projects.

Existing industry solutions attempt to estimate economic impact using assumption-driven models or static multipliers, which often rely on outdated economic relationships rather than actual spending patterns. These models struggle to account for leakages in local spending, supply constraints, and counterfactual scenarios—leading to estimates that may overstate or misrepresent the true impact of local investment decisions.

Without accurate, real-time tracking of how money flows through a local economy, policymakers and organizations are left to make investment decisions in the dark—unable to differentiate between spending and incentives that genuinely stimulate local economic growth versus policies and programs with little impact that leak money out of the community.

Our Solution

At the core of our product offering is our ability to map the transaction-level financial data of our clients and their partners to a set of metrics that measures and incentivizes local economic growth.

To understand how our metrics are designed to accomplish these purposes, we first need to introduce some key economic concepts.

What is Local Economic Growth?

Economic growth is an increase in the size of the economy. One common metric economists use to measure the size of the national economy is based on the total production of final goods and services, also known as the Gross Domestic Product or GDP. Local economic growth is the increase in the production of goods and services within a specific geography.

The key drawback for using GDP to define local economic growth is that GDP does not account for net monetary flows into and out of the locality. In contrast, Gross National Income (GNI) adjusts for these income flows by measuring the total income earned by residents and businesses of the locality, regardless of where production occurs.

While Gross Domestic Product (GDP) measures all economic activity that takes place within a region, it does not distinguish between income that stays local and income that flows out to external businesses, workers, or investors. Gross National Income (GNI), on the other hand, focuses on the total earnings received by residents and businesses of the area, providing a clearer picture of how much economic value is retained locally.

If a large share of wages, profits, or rents generated within a region is paid to non-local entities—such as out-of-state corporations, remote workers, or external suppliers—then GNI will be lower than GDP. This gap between GDP and GNI signals that a significant portion of the locally produced wealth is leaking out rather than contributing to local job growth, business expansion, or reinvestment in the community. In contrast, when local businesses and residents receive and re-spend a greater share of the income generated, GNI more closely matches GDP, reflecting stronger economic benefits for the local economy.

Thus, at Aprize, when considering the economic benefit to a region for economic development purposes, we prefer a GNI-centric measure of economic growth as this better captures the flow of money and the holistic realized benefits for the residents of the local economy.

Illustrative Example of GNI vs GDP

To illustrate, consider a $10,000 grant issued to repaint a local government building in Johnson County. The government has two potential contractors:

Local Painting Company – Owned and operated by Johnson County residents, with workers and suppliers also located within the county.
National Behemoth Company – A large firm headquartered out of state, with workers and owners residing elsewhere.

Regardless of the contractor chosen, $10,000 of final goods and services is produced within Johnson County, meaning GDP remains consistent in either scenario. However, the distribution of income and spending power differs dramatically:

Wages ($6,000) – Kept within Johnson County if Local Painter Company is chosen but leave the county if National Behemoth Company is used.
Owner Profits ($2,000) – Retained in Johnson County only if the local company is chosen.
Supplies ($2,000) – Could stay in the county or leak out depending on sourcing.

If the National Behemoth Company is selected, 80% of the funds ($8,000) immediately leave Johnson County, significantly reducing the local economic benefit. Since wages and profits flow to out-of-county workers and owners, only the $2,000 spent on supplies has the potential to stay local. In contrast, choosing the Local Painter Company ensures that wages and profits remain within the community, circulating through the local economy and amplifying the long-term impact on local GNI.

Best Case Scenario for Johnson County
Local Metric	Definition	Local Painting Company	National Behemoth Company
GDP (Gross Domestic Product)	Total value of goods/services produced within Johnson County	$10,000	$10,000
GNI (Gross National Income)	Total income earned by Johnson County residents	$10,000	$2,000

Local Economic Multipliers

If the Local Painting Company is selected, the additional $8,000 in local wages and profits represents only the initial injection of economic activity. Local workers and business owners will then re-spend a portion of their earnings on goods and services, generating additional income for others in Johnson County. Because they live and operate within the community, they are more likely to make purchases locally, setting off a local economic multiplier effect as money continues to circulate.

The extent to which this process amplifies economic activity depends on how much of each additional dollar remains in the local economy versus how much leaks out. This relationship is captured by the marginal propensity to consume locally, which measures the fraction of gross income that is spent within Johnson County rather than saved or spent elsewhere. A higher marginal propensity to consume locally results in a stronger multiplier effect, increasing the total economic impact of the initial investment.

Mathematically, if c denotes the marginal propensity to consume locally, then the local economic multiplier m is:²

m = \frac{1}{1 - c} .

The higher the marginal propensity to consume locally, the higher the local economic multiplier. For example, if only 20% of income is spent locally ( $c = 0.2$ ), the local economic multiplier is:

m = \frac{1}{1 - 0.2} = 1.25 .

This means that every $1 spent locally generates a total economic impact of $1.25 after accounting for additional rounds of re-spending. If 40% of income is spent locally ( $c = 0.4$ ), then:

m = \frac{1}{1 - 0.4} = 1.67,

indicating a stronger multiplier effect due to increased local circulation of funds.

To get the total local economic impact of a grant or government spending g, we need to multiply it by both the share of spending initially spent locally and the local economic multiplier:

Total Local Impact = g \cdot c \cdot m = g \cdot \frac{c}{1 - c} .

Supply Constraints and Equilibrium Considerations

The local economic multiplier assumes that additional spending leads to increased local production and income. However, when supply constraints exist, this assumption weakens because businesses and workers may not be able to expand output in response to new demand. This introduces important short-run and long-run equilibrium considerations that affect the effectiveness of local investment decisions.

Short-Run Equilibrium: Fixed Supply and Crowding Out

In the short run, key inputs such as labor, production capacity, and business services may be fixed or inelastic, meaning that local firms cannot easily expand to meet new demand.

When this happens:

Hiring Constraints – If local businesses are already at full employment, new spending does not create additional jobs but instead may raise wages or shift workers between firms.
Capacity Constraints – If local suppliers and firms are at full production, they may increase prices rather than output, weakening the multiplier effect.
Crowding Out – If a local firm takes one contract, it may be unable to take another, meaning that the net gain to the local economy depends on what would have happened otherwise (the counterfactual).
1. Example: If Local Painting Company could have completed another project elsewhere, then choosing it for this grant does not increase total local employment or income—it just reallocates work. In this case, the total GNI effect is neutral.

Long-Run Equilibrium: Supply Elasticity and Business Growth

In the long run, the effect of local spending on economic growth depends on how responsive the local economy is to increased demand. The key factor is supply elasticity, which determines whether local businesses can scale up in response to new opportunities:

If supply is elastic (firms can expand production, and more workers can enter the labor force), then local investment leads to higher long-term GNI, reinforcing the local multiplier effect.
If supply is inelastic (businesses cannot expand, and new workers are unavailable), then increased demand mainly results in higher wages or prices, limiting economic gains.

Mathematically, we can adjust our local multiplier to account for the elasticity of supply $λ$ :

m = \frac{1}{1 - c \cdot λ} .

When $λ$ =1, we have perfectly elastic supply and the full local multiplier effect. When 0< $λ$ <1, we have inelastic supply and the local multiplier is dampened. When $λ$ =0, we have perfectly inelastic supply and there is no local multiplier effect.

And so, in the long run, we have the total economic impact of spending g on a project as:

g \cdot c \cdot m \cdot λ = g \cdot \frac{c}{1 - c} \cdot λ .

Bridging Economic Theory with What Is Observed

Traditional economic impact models often start with the grant amount (g) and then multiply it by a theoretical multiplier to estimate local benefits. In theory, the total economic impact of a local investment can be expressed as:

g \cdot (c_{1} + c_{1} c_{2} + c_{1} c_{2} c_{3} + \dots) \cdot λ

where:

$c_{1}$ is the fraction of the grant spent locally by the first recipient (for example, a contractor)
$c_{2}$ , etc. are the successive shares of spending retained by local entities, and
$λ$ is the supply elasticity factor, which adjusts for whether increased demand leads to new production rather than price increases or crowding out.

This equation captures the idea that each round of local spending generates additional economic activity. However, traditional models rely on assumptions to fill in these factors, which can obscure true local dynamics.

At Aprize, we replace assumptions with direct observation. Rather than estimating multipliers, we track actual transaction data across multiple tiers of spending. This approach yields two core metrics:

Observed Local Multiplier (OLM): Calculated as the sum of observed rounds of local spending $\sum_{i = 1}^{n} c_{1} c_{2} \dots c_{i}$ , this metric measures how far each dollar circulates in the community. If a round of spending isn’t tracked, it is treated as zero rather than filled in with an assumption.
Local Spend Share (LSS): This is simply the first layer (c₁)—the share of the initial grant that is spent locally. It provides an immediate snapshot of spending behavior and sets the foundation for understanding the broader multiplier effect.

For our core metrics, we exclude g to standardize across grant levels, and we exclude λ to ensure our analysis is based solely on observable, verifiable economic activity.

Our Tiering System

Due to privacy and other factors, we do not publicly report OLM and LSS. Instead, we report each client’s rating on our tiered system: Diamond, Platinum, Gold, Silver, Bronze, and Unrated.

Our tiers take into consideration both the OLM and the LSS to identify and incentivize clients who:

Spend a significant share locally
Give a significant share locally
Spend and give to organizations that spend and give locally (as exemplified by their Aprize rating)

We do this via our Composite Local Impact Score (CLIS). The CLIS is computed by normalizing and weighting the two metrics on a scale of 0 to 100:

CLIS = weight × (Normalized OLM) + (1 − weight) × (Normalized LSS).

Normalization is based on current industry benchmarks and regional data, and the weighting factor, which is the same for all parties, is selected to ensure that both metrics contribute meaningfully to the overall score.

We map CLIS scores into ratings according to the following cutoff points:

Diamond (CLIS > 90): Exceptional performance, reflecting a strong multiplier effect and outstanding local spend behavior.
Platinum (CLIS 80–90): Robust local impact, with high levels of both observed spending circulation and local spend share.
Gold (CLIS 70–80): Solid performance characterized by a well-balanced contribution from both metrics.
Silver (CLIS 60–70): Moderate impact with clear opportunities for improvement in either spending retention or multiplier effect.
Bronze (CLIS < 60): Basic local economic activity that signals a need for significant enhancements.
Unrated (Insufficient Data): Either performance falls below the baseline for reliable assessment or the data is incomplete.

Our tiering system is designed to actively adapt to the most comprehensive and accurate data. Ratings are periodically reassessed to reflect real-time changes and improvements. As we collect more localized data, our benchmarks refine even further, ensuring that our ratings remain both accurate and relevant to the localities we serve.

Wrapping Up

Aprize’s methodology transforms local economic impact measurement by grounding it in real, transaction-level data rather than theoretical estimates. Our approach begins by capturing the Observed Local Multiplier and Local Spend Share—two metrics that directly track how funds circulate within a community. These metrics reveal not only the extent of local spending but also the multiplier effect generated when dollars repeatedly circulate among local businesses.

To distill these insights into an actionable and competitive framework, we developed the Composite Local Impact Score (CLIS), which harmonizes our core metrics into a single, standardized score. This score underpins our dynamic tiering system, categorizing organizations into Diamond, Platinum, Gold, Silver, Bronze, or Unrated based on their demonstrated local economic performance. By incentivizing not only local spending but also engagement with other Aprize-rated businesses, our tiering system drives organizations toward continuously improving their local integration.

Ultimately, our model empowers economic development corporations (EDCs), governments, grant-makers like private and community foundations, chambers of commerce, redevelopment commissions, non-profits, and businesses to make more informed investment decisions that strengthen their communities. With periodic reassessments and data-driven benchmarks, our transparent and verifiable metrics provide a clear roadmap for enhancing local economic resilience and growth—ensuring that every dollar spent locally multiplies economic growth and community impact.

The Lilly Endowment has contributed $900m to these foundations since 1990. ↩︎
The formula is derived from the fact that 1+c + c^2 + ... = 1/(1-c). ↩︎

Aprize Metrics: The economic rationale