Gini Coefficient (Income Inequality)

Quality / Updated / COI

Quality

Reviewed

Updated

02/12/2026

Source

Citations & Trust

COI

none

TL;DR

The Gini coefficient helps assess income inequality by clarifying income distribution and the trade-offs between equity and incentives. It keeps scope and assumptions aligned.

Definition

The Gini coefficient summarizes income inequality on a scale from 0 (perfect equality) to 1 (perfect inequality). It specifies the unit of analysis and the assumptions behind income distribution measurement, including income definitions and household equivalence scales. The concept separates what is in scope (income distribution metrics) from what is out of scope (wealth inequality unless specified), so comparisons stay consistent. Applied well, it turns a vague debate into a measurable choice and makes the drivers of results explicit.

Decision impact

• Use the Gini Coefficient to decide inequality-focused policy responses, because it exposes income distribution and the trade-off with equity versus incentives.
• It changes budgeting and prioritization by making income definitions and equivalence scales explicit and reviewable.
• It informs adjustments when tax policy or labor market shifts occur, so the decision stays grounded in current conditions.

Key takeaways

• Define the unit and time horizon before comparing Gini values across options.
• Track the primary driver (Gini value) separately from secondary noise.
• Run sensitivity checks on top-income measurement and data coverage to avoid false precision.
• Document data sources and calculation steps so results are auditable.
• Revisit the metric when the business model or market context changes.

Misconceptions

• Gini does not show where inequality occurs; it is a summary measure.
• Different distributions can yield the same Gini value.
• Data quality and definitions strongly affect comparisons.

Worked example

A policy unit sees the Gini rise from 0.32 to 0.36 after tax changes. It decomposes the distribution to see whether the top 10% or bottom 40% drove the change and tests how a targeted credit would alter the index. The analysis shows a modest credit could reduce the Gini by 0.01 without large revenue loss. After implementation, they track the Gini alongside poverty rates to validate impact.

Citations & Trust

• CORE Econ (The Economy)