Inequality Larry Temkin. Studied at UW Madison, Oxford, and Princeton Currently chair of Philosophy at Rutgers.

Slides:



Advertisements
Similar presentations
Fairness and Social Welfare Functions. Deriving the Utility Possibility Frontier (UPF) We begin with the Edgeworth Box that starts with individual 1,and.
Advertisements

NUMERICAL DESCRIPTIVE STATISTICS Measures of Variability.
Lecture 2 Describing Data II ©. Summarizing and Describing Data Frequency distribution and the shape of the distribution Frequency distribution and the.
Chapter 9: The Normal Distribution
Project: – Several options for bid: Bid our signal Develop several strategies Develop stable bidding strategy Simulating Normal Random Variables.
Calculating & Reporting Healthcare Statistics
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. by Lale Yurttas, Texas A&M University Chapter 171 CURVE.
Learning Objectives In this chapter you will learn about the importance of variation how to measure variation range variance standard deviation.
Chapter 5 – 1 Chapter 5: Measures of Variability The Importance of Measuring Variability The Range IQR (Inter-Quartile Range) Variance Standard Deviation.
Algebra Problems… Solutions
Measures of Central Tendency
THIRD AND FOURTH GRADE NUMBER AND OPERATIONS: FRACTIONS
Alliance Stat Class Understanding Measures of Center and Spread.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 7) Slideshow: weighted least squares and logarithmic regressions Original citation:
1 Measures of Variability Chapter 5 of Howell (except 5.3 and 5.4) People are all slightly different (that’s what makes it fun) Not everyone scores the.
Simple Linear Regression Models
BIOSTAT - 2 The final averages for the last 200 students who took this course are Are you worried?
Measures of Central Tendency & Spread
INCOME REDISTRIBUTION: CONCEPTUAL ISSUES
Measures of Variability In addition to knowing where the center of the distribution is, it is often helpful to know the degree to which individual values.
Why statisticians were created Measure of dispersion FETP India.
Tuesday August 27, 2013 Distributions: Measures of Central Tendency & Variability.
Measures of Central Tendency and Dispersion Preferred measures of central location & dispersion DispersionCentral locationType of Distribution SDMeanNormal.
Thinking About Psychology: The Science of Mind and Behavior 2e Charles T. Blair-Broeker Randal M. Ernst.
COURSE: JUST 3900 INTRODUCTORY STATISTICS FOR CRIMINAL JUSTICE Instructor: Dr. John J. Kerbs, Associate Professor Joint Ph.D. in Social Work and Sociology.
Chapter 2 Risk Measurement and Metrics. Measuring the Outcomes of Uncertainty and Risk Risk is a consequence of uncertainty. Although they are connected,
Psychology’s Statistics Module 03. Module Overview Frequency Distributions Measures of Central Tendency Measures of Variation Normal Distribution Comparative.
Decision making Under Risk & Uncertainty. PAWAN MADUSHANKA MADUSHAN WIJEMANNA.
An Introduction to Statistics. Two Branches of Statistical Methods Descriptive statistics Techniques for describing data in abbreviated, symbolic fashion.
Gile Sampling1 Sampling. Fundamental principles. Daniel Gile
Measurement of income distribution. Income distribution Income distribution refers to the way the nation’s “income cake” is divided or shared between.
Copyright © 2014 by Nelson Education Limited. 3-1 Chapter 3 Measures of Central Tendency and Dispersion.
Descriptive Statistics: Presenting and Describing Data.
Chapter 5: Measures of Variability  The Importance of Measuring Variability  IQV (Index of Qualitative Variation)  The Range  IQR (Inter-Quartile Range)
The Statistical Analysis of Data. Outline I. Types of Data A. Qualitative B. Quantitative C. Independent vs Dependent variables II. Descriptive Statistics.
FREQUANCY DISTRIBUTION 8, 24, 18, 5, 6, 12, 4, 3, 3, 2, 3, 23, 9, 18, 16, 1, 2, 3, 5, 11, 13, 15, 9, 11, 11, 7, 10, 6, 5, 16, 20, 4, 3, 3, 3, 10, 3, 2,
Sociology 5811: Lecture 3: Measures of Central Tendency and Dispersion Copyright © 2005 by Evan Schofer Do not copy or distribute without permission.
Measures of Variability: “The crowd was scattered all across the park, but a fairly large group was huddled together around the statue in the middle.”
1.  In the words of Bowley “Dispersion is the measure of the variation of the items” According to Conar “Dispersion is a measure of the extent to which.
Introduction to statistics I Sophia King Rm. P24 HWB
Variability Introduction to Statistics Chapter 4 Jan 22, 2009 Class #4.
Variability. What Do We Mean by Variability?  Variability provides a quantitative measure of the degree to which scores in a distribution are spread.
1 HETEROSCEDASTICITY: WEIGHTED AND LOGARITHMIC REGRESSIONS This sequence presents two methods for dealing with the problem of heteroscedasticity. We will.
Organizing and Analyzing Data. Types of statistical analysis DESCRIPTIVE STATISTICS: Organizes data measures of central tendency mean, median, mode measures.
Negotiating Socially Optimal Allocations of Resources U. Endriss, N. Maudet, F. Sadri, and F. Toni Presented by: Marcus Shea.
1 Measuring Poverty: Inequality Measures Charting Inequality Share of Expenditure of Poor Dispersion Ratios Lorenz Curve Gini Coefficient Theil Index Comparisons.
Regression Analysis: A statistical procedure used to find relations among a set of variables B. Klinkenberg G
Psychology’s Statistics Appendix. Statistics Are a means to make data more meaningful Provide a method of organizing information so that it can be understood.
The Law of Averages. What does the law of average say? We know that, from the definition of probability, in the long run the frequency of some event will.
© aSup-2007 VARIABILITY   1 MEASURES OF VARIABILITY.
(Unit 6) Formulas and Definitions:. Association. A connection between data values.
Virtual University of Pakistan Lecture No. 11 Statistics and Probability by Miss Saleha Naghmi Habibullah.
Theme 5. Association 1. Introduction. 2. Bivariate tables and graphs.
Absolute and relative poverty
Part 5 - Chapter
Part 5 - Chapter 17.
INCOME REDISTRIBUTION
Multi-Step Equations How to Identify Multistep Equations |Combining Terms| How to Solve Multistep Equations | Consecutive Integers.
Describing Distributions
Dispersion.
The Experimental Method
Descriptive Statistics: Presenting and Describing Data
MEASURES OF CENTRAL TENDENCY
Part 5 - Chapter 17.
EVSC 1300—Spring, 2017 Exam 3 40 total points >35 A 29–34 B 23–28 C
An examination of the purpose and techniques of inequality measurement
Mean Absolute Deviation
Mean Absolute Deviation
Mean Absolute Deviation
DRQ #10 AGEC pts October 17, 2013 (1 pt) 1. Calculate the median of the following sample of observations for a variable labeled.
Presentation transcript:

Inequality Larry Temkin

Studied at UW Madison, Oxford, and Princeton Currently chair of Philosophy at Rutgers

Central Question: When is one situation worse than another with respect to inequality?

Complaints Temkin introduces the idea of a “complaint with respect to inequality” If one situation is worse than another with respect to inequality, it will be worse for some person or persons in that situation. That person or those persons are then said to have a complaint.

Complaint 1: Any who are worse off than average have a complaint. – If society has n total welfare, then any who have less than one nth of the total through no fault of their own have a complaint. – One who is below the average then has less than their fair share, and a complaint seems warranted in a way that it does not from anyone with their fair share or more.

Complaint 2: All except the most well-off have a complaint. – Consider the following diagram:

q has a complaint in each case, because q has less than p through no fault of q.

The presence of r and s only seem to multiply the complaints.

The situation for q even seems worse in C than in A or B, even though q is 10 units closer to average in C than in A.

Size of Complaints There are 3 plausible ways of judging the size of complaints: 1.How far below average is the one with the complaint

Size of Complaints There are 3 plausible ways of judging the size of complaints: 1.How far below average is the one with the complaint 2.How far below the most well off is the one with the complaint

Size of Complaints There are 3 plausible ways of judging the size of complaints: 1.How far below average is the one with the complaint 2.How far below the most well off is the one with the complaint 3.How those less well off compare with everyone who is better off than they

Size of Complaints The first two parallel our thinking concerning who has a complaint. 1.How far below average is the one with the complaint 2.How far below the most well off is the one with the complaint 3.How those less well off compare with everyone who is better off than they

Size of Complaints The first two parallel our thinking concerning who has a complaint. The third requires some explanation… 1.How far below average is the one with the complaint 2.How far below the most well off is the one with the complaint 3.How those less well off compare with everyone who is better off than they

Size of Complaints The third requires some explanation… If it is bad to be worse off than someone else through no fault of your own, it is worse to be worse off than more than one such person. 1.How far below average is the one with the complaint 2.How far below the most well off is the one with the complaint 3.How those less well off compare with everyone who is better off than they

Principles of Equality The maximin principle of equality seeks to first, maximize the position of the least- well-off group, and then minimize the number of people in that group. 1.Maximin Principle

Principles of Equality The additive principle simply adds together the sizes of each complaint, and worlds with a greater total complaint are worse than worlds with less total complaint. 1.Maximin Principle 2.Additive Principle

THE SEQUENCE Temkin’s main tool to think about inequality is a series of simple situations referred to as “The Sequence”.

THE SEQUENCE Temkin’s main tool to think about inequality is a series of simple situations referred to as “The Sequence”. The Sequence represents 999 distinct situations to be analyzed with respect to inequality

Notice that both the total and average utility get worse and worse as the sequence progresses.

For our purposes, this is an irrelevant feature. We simply want to know what inequality does over the course of the sequence.

View 1: The sequence gets BETTER AND BETTER with respect to inequality.

– The distance between those who have less and the average decreases as the sequence progresses

View 1: The sequence gets BETTER AND BETTER with respect to inequality. – The number of people better off than those who are less well off decreases as the sequence continues.

View 1: The sequence gets BETTER AND BETTER with respect to inequality. – It looks like someone is unjustly punished at the beginning and someone is unjustly rewarded at the end, and unjust punishments are more objectionable than unjust rewards.

View 1: The sequence gets BETTER AND BETTER with respect to inequality. – The maximin principle of equality prefers the complaints to be more evenly distributed, instead of heaped upon a few or one.

View 1: The sequence gets BETTER AND BETTER with respect to inequality. – The costs to the well-off to fix the inequality increase as the sequence progresses, and the gain for the worse-off decreases as the sequence progresses, so the inequality is more egregious at the beginning and less so at the end.

View 2: The sequence gets WORSE AND WORSE with respect to inequality.

– The additive principle states that the more people with a complaint, the worse a situation is.

View 2: The sequence gets WORSE AND WORSE with respect to inequality. – At the beginning, the worse-off has a complaint against the best-off. As the sequence progresses, more people have the same complaint.

View 3: The sequence gets WORSE THEN BETTER with respect to inequality. – The endpoints are closer to absolute equality than the middle, so as the sequence progresses, we move further from absolute equality, and then closer past the midpoint.

View 3: The sequence gets WORSE THEN BETTER with respect to inequality. – If you look at complaints against all those who are better off and use the additive principle, then the middle, where a large number have a large complaint will be worse than the beginning, where a small number have a large complaint, and the end where a large number have a small complaint.

View 3: The sequence gets WORSE THEN BETTER with respect to inequality. – If you look at complaints of all those who are below average and use the additive principle, then the middle, where a large number have a large complaint will be worse than the beginning, where a small number have a large complaint, and the end where a large number have a small complaint.

View 4: The sequence gets BETTER THEN WORSE with respect to inequality.

– “By now it may seem that there are bound to be several plausible positions supporting the judgment that the Sequence first gets better, then gets worse. If there are such elements, however, I am not aware of them.” (116)

View 5: The worlds of the sequence are EQUIVALENT with respect to inequality.

– If social institutions there are are responsible for the presence of inequality, but not for the number of people in each group, then the worlds of the sequence are equivalent.

View 5: The worlds of the sequence are EQUIVALENT with respect to inequality. – Temkin’s analogy: two judges who accept bribes for all their cases are equally corrupt even if one has tried fewer cases than the other.

The Complexity of Inequality: If there are only two general principles of equality, and only three ways of having complaints with respect to inequality (which is surely false) then there are six different ways to explain what is happening with The Sequence, broken down as follows:

The Complexity of Inequality below average below best below all better Additive Principle worse then better worse and worse worse then better Maximin Principle better and better worse and worse better and better

Fractal Complexity There are surely additional plausible principles of equality, which, when combined with the reasons for complaint (which may well number more than three) yield further distinct analyses of inequality.

Fractal Complexity There are surely additional plausible principles of equality, which, when combined with the reasons for complaint (which may well number more than three) yield further distinct analyses of inequality. There are also specific modifications of both the principles of equality and the reasons for complaint that yield further variation.

Fractal Complexity Also, consider any situation more complex than The Sequence (e.g. real life) and you see that there are a great many plausible ways to think about when a situation is better or worse than another with respect to inequality.

Common Measures of Inequality Economists sometimes use the following measures of inequality:

Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: calculates the difference between the highest and lowest observations of a particular variable of interest

Some Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: the difference between the highest and lowest observations of a particular variable of interest

Some Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: the difference between the highest and lowest observations of a particular variable of interest – Coefficient of Variation: the standard deviation of a variable divided by the mean.

Some Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: the difference between the highest and lowest observations of a particular variable of interest – Coefficient of Variation: the standard deviation of a variable divided by the mean. – Gini Coefficient: a measure of the degree of deviation from perfect equality (see following)

Some Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: the difference between the highest and lowest observations of a particular variable of interest – Coefficient of Variation: the standard deviation of a variable divided by the mean. – Gini Coefficient: a measure of the degree of deviation from perfect equality (see following)

Range:

Some Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: below best + maximin – Coefficient of Variation: the standard deviation of a variable divided by the mean. – Gini Coefficient: a measure of the degree of deviation from perfect equality (see following)

The curve on the left has more people below the average and more people far below the average than does the much tighter curve on the right, so the sum totals of each individual complaint will be higher in blue than red.

Some Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: below best + maximin – Coefficient of Variation: below average + additive – Gini Coefficient: a measure of the degree of deviation from perfect equality (see following)

Because the total area between the curve and perfect equality, each person with less is being compared to how many people have more and how much more they all have.

Some Common Measures of Inequality Economists sometimes use the following measures of inequality: – Range: below best + maximin – Coefficient of Variation: below average + additive – Gini Coefficient: below all better + additive