Battista - Poverty and Discrimination

From Diversifying Economic Quality: A Wiki for Instructors and Departments

Revision as of 13:28, 10 January 2014 by Abayer1 (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

The Economics of Poverty and Discrimination

by Dr. Clare Battista, California Polytechnic State University, San Luis Obispo


Assignments, Learning Objectives, and Resources for Module 3

Course Description:

In this course, we explore poverty, discrimination, inequality, and immigration in the contemporary U.S. macroeconomic context.
We will also move beyond the boundaries of economic theory and examine these topics from an interdisciplinary perspective. This means that we will incorporate economic history, public policy, technological change, legal-institutional environments, as well as social-psychological determinants of behavior, into our understanding of these topics.
In this course, we will also approach the material from the vantage point of integration and model building, focusing on understanding and analyzing the problems, brainstorming solutions, making connections across theory and reality, and connecting the dots of a logical argument. In short, we will try to think and act like economists and enlighten others about the way the world works.

Course Outline:

Module 1: Biases, Irrationality and Systems of Thinking and Behaving
Module 2: Poverty: Misperceptions, Theories, Reality, Narratives, Assumptions, Policies, Data, Measures, and Solutions
Module 3: Theories of Economic Success (income and earnings), Misperceptions, Human Capital, Meritocracy, 10,000 hours, Grit, Resilience, Talent, Failure, Marginal Productivity, Money ball, and Returns to MP -- Assignments, Learning Objectives, and Resources
Module 4: Economic Inequality and Economic Mobility: Reality, Myths, Misperceptions, Data, Historical Trends, Causes, Consequences, Relationships, and Linkages to Immigration
Module 5: Discrimination: Race, Gender, Class and Ethnicity, Connections across Disciplines, Reading the Data.