PROJECTQuantitative Data Analysis

• Project Home
• Inference
• Impact Analysis
• Bias
• Experiments
• Paired Testing
• Quasi-experimental Methods
• Difference-in-Difference and Panel Methods
• Instrumental Variables
• Propensity Score Matching
• Regression Discontinuity
• Regression Techniques
• Generalized Linear Model
• Linear Regression
• Logit and Probit Regression
• Segregation Measures
• Inequality Measures
• Decomposition Methods
• Descriptive Data Analysis
• Microsimulation
• The Dynamic Simulation of Income Model DYNASIM
• The Health Insurance Policy Simulation Model HIPSM
• The Model of Income in the Near Term (MINT)
• The Transfer Income Model TRIM
• The Tax Policy Center Microsimulation Model
• Performance Measurement and Management

• Inequality Measures

The measurement of inequality usually focuses on measuring inequality in outcomes (income or wealth or health or some other measure of well-being), variously using differences between the highest and lowest outcomes or variation nearer the middle or some other part of the distribution. Measures such as the 90th percentile divided by the 10th percentile characterize the gap between very high outcomes and very low outcomes—for example, the wealth or income of the rich and the poor—but there are many other measures of that gap.

Various indexes have been developed that summarize different aspects of the dispersion in the distribution of outcomes. For example, the Gini coefficient is more sensitive to variation around the middle of the distribution than at the top or bottom of the distribution. Indexes of inequality in the generalized entropy (GE) family are more sensitive to differences in income shares among the poor or among the rich depending on a parameter that defines the index (Jenkins and van Kerm 2008).

Similarly, Atkinson indexes are defined by a parameter that determines sensitivity to differences in different parts of the distribution. A larger GE parameter value makes the index more sensitive to differences at the top of the distribution; a smaller parameter value makes it more sensitive to differences at the bottom of the distribution. GE(0) is the mean logarithmic deviation, GE(1) is known as the Theil index, and GE(2) is half the squared coefficient of variation. The larger its parameter value, the more sensitive an Atkinson index is to income differences at the bottom of the distribution.

Research Methods Data analysis Quantitative data analysis Research methods and data analytics