5.1 Measuring Income Inequality

Income inequality is a crucial concept in public economics, measuring how unevenly income is distributed across a population. It affects economic growth, social mobility, and overall well-being, shaping public policies on taxation and welfare programs.

Measuring income inequality involves various metrics like the Gini coefficient, Lorenz curve, and percentile ratios. These tools help economists and policymakers analyze income distribution, compare countries, and assess the impact of economic policies on different income groups.

Income inequality in public economics

Defining income inequality and its importance

Top images from around the web for Defining income inequality and its importance

• 

  La courbe de "Gatsby le Magnifique" : inégalités et élasticité intergénérationnelle des revenus ... View original

  Is this image relevant?

• 

  Gini coefficient - Wikipedia View original

  Is this image relevant?

• 

  Sampling distribution of Gini coefficient View original

  Is this image relevant?

• 

  La courbe de "Gatsby le Magnifique" : inégalités et élasticité intergénérationnelle des revenus ... View original

  Is this image relevant?

• 

  Gini coefficient - Wikipedia View original

  Is this image relevant?

1 of 3

Top images from around the web for Defining income inequality and its importance

• 

  La courbe de "Gatsby le Magnifique" : inégalités et élasticité intergénérationnelle des revenus ... View original

  Is this image relevant?

• 

  Gini coefficient - Wikipedia View original

  Is this image relevant?

• 

  Sampling distribution of Gini coefficient View original

  Is this image relevant?

• 

  La courbe de "Gatsby le Magnifique" : inégalités et élasticité intergénérationnelle des revenus ... View original

  Is this image relevant?

• 

  Gini coefficient - Wikipedia View original

  Is this image relevant?

1 of 3

• Income inequality describes uneven distribution of income among individuals or households within a society or economy
• Plays crucial role in public economics by affecting economic growth, social mobility, and overall societal well-being
• Measured in absolute terms (total income differences) or relative terms (ratios or percentages of income distribution)
• Public policies shape income inequality levels through taxation and social welfare programs
• High levels of income inequality may lead to:
  • Social and political instability
  • Reduced economic growth
  • Decreased overall societal welfare
• Often linked to wealth inequality, although the two concepts are distinct and may have different policy implications
• Examples of countries with high income inequality:
  • United States (Gini coefficient around 0.41)
  • Brazil (Gini coefficient around 0.53)

Impact of income inequality on society

• Influences social cohesion and trust within communities
• Affects access to education and healthcare, potentially perpetuating inequality across generations
• Can lead to political polarization and populist movements
• May impact consumer spending patterns and overall economic demand
• Influences labor market dynamics and wage negotiations
• Examples of potential consequences:
  • Increased crime rates in areas with high inequality
  • Lower social mobility in countries with higher income gaps

Measuring income inequality

Common metrics for quantifying inequality

• Gini coefficient ranges from 0 (perfect equality) to 1 (perfect inequality)
  • Widely used measure allowing for easy cross-country comparisons
  • Example: A country with a Gini coefficient of 0.3 has lower inequality than one with 0.5
• Lorenz curve graphically represents cumulative distribution of income across population
  • Diagonal line represents perfect equality
  • Greater curve distance from diagonal indicates higher inequality
• 20:20 ratio compares income share of top 20% of population to bottom 20%
  • Provides insight into extremes of income distribution
  • Example: A 20:20 ratio of 10 means the top 20% earn 10 times more than the bottom 20%
• Palma ratio measures ratio of income share between top 10% and bottom 40% of population
  • Focuses on extremes while considering a larger portion of lower incomes
  • Example: A Palma ratio of 2 indicates the top 10% earn twice as much as the bottom 40%

Advanced inequality measures

• Percentile ratios compare incomes at different points in distribution to assess inequality
  • 90/10 ratio contrasts incomes at 90th and 10th percentiles
  • 80/20 ratio examines gap between upper-middle and lower-middle incomes
• Theil index uses entropy-based measure allowing for decomposition of inequality
  • Enables analysis of inequality within and between different subgroups (regions, industries)
  • More complex to interpret than Gini coefficient
• Atkinson index incorporates parameter reflecting society's aversion to inequality
  • Allows for different weightings of income disparities based on societal preferences
  • Higher Atkinson index indicates greater social welfare loss due to inequality

Interpreting income distribution data

Analyzing income segments and trends

• Examine income quintiles or deciles to understand distribution across population segments
  • Example: In the US, the top quintile often earns more than 50% of total income
• Compare pre-tax and post-tax income distributions to reveal impact of tax policies
  • Progressive tax systems typically reduce post-tax income inequality
• Analyze income shares held by specific percentiles (top 1%, bottom 50%)
  • Helps identify concentration of wealth and income at extremes
  • Example: In some countries, the top 1% may hold over 20% of total income
• Conduct trend analysis of inequality measures over time
  • Indicates whether income disparities are increasing, decreasing, or stable
  • Example: Rising Gini coefficient over decades suggests growing inequality

Cross-country and demographic comparisons

• Compare income inequality metrics among different economies
  • Reveals relative inequality levels and potential policy effects
  • Example: Nordic countries often have lower Gini coefficients than the US
• Disaggregate income data by demographic factors (age, gender, education)
  • Uncovers patterns of inequality within specific subgroups
  • Example: Gender pay gap analysis across different industries or age groups
• Interpret income mobility data alongside distribution data
  • Provides comprehensive understanding of economic opportunities and long-term inequality
  • Example: Countries with high income inequality but high mobility may have different policy needs than those with low mobility

Strengths vs limitations of inequality measures

Advantages of common inequality metrics

• Gini coefficient offers single, easily comparable measure across countries and time periods
• Lorenz curves provide visual representation of inequality, aiding in intuitive understanding
• Percentile ratios are straightforward to communicate to non-technical audiences
  • Example: "The top 10% earn 5 times more than the bottom 10%" is easily grasped
• Theil index allows for subgroup decomposition, enabling detailed analysis of inequality sources
  • Useful for identifying regional or sectoral contributions to overall inequality

Limitations and challenges in measuring inequality

• Gini coefficient may obscure details about shape of income distribution
  • Two very different distributions can have the same Gini coefficient
• Lorenz curves can be difficult to compare precisely across different distributions
• Percentile ratios may miss important information about middle of distribution
  • Focus on extremes can overlook changes in middle-class incomes
• All income-based measures may fail to capture total economic well-being
  • Exclude non-monetary benefits (healthcare, education) and wealth accumulation
  • Example: Countries with strong social services may have better economic well-being than income measures suggest
• Some metrics are more sensitive to changes in different parts of distribution
  • Gini coefficient is more sensitive to changes in the middle of the distribution
  • Palma ratio is more sensitive to changes at the top and bottom
• Data quality and availability significantly impact accuracy and reliability of measurements
  • Particularly challenging in developing countries with large informal economies
  • Underreporting of top incomes can lead to underestimation of inequality