arun g. chandrasekhar


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themes. [informal markets] [signaling and reputation] [social learning] [statistics] [health]

econ 125: economic development, social networks, and microfinance (upper div. undergrad)

syllabus

this course will provide an introduction to the study of the financial lives of households in less developed countries. we will focus on savings, credit and microfinance, informal insurance, social learning (how communities learn about new technologies, products, and job opportunities), corruption, and redistribution. We will emphasize the role that social networks play in each of these study areas. We will focus on questions such as:
  • why do the poor seem to systematically undersave and what institutions have organically developed to cope with this
  • without access to formal credit or insurance, how do households in less developed countries deal with irregular income? item is microfinance a panacea?
  • how should a policymaker structure information dissemination campaigns? what strategies are likely to succeed, what are likely to fail and lead to misinformation traps?
to address these sorts of questions we will make use of economic theory to structure our thinking and then turn to empirical analyses that test competing theories.

econ 216: development economics III (phd level)

syllabus

this course is aimed at economics phd students who have satisfied all the first year requirements. we will study the financial lives of the poor (savings, credit, informal insurance), information (with applications to technology adoption, labor markets, financial products), public finance, and firm organization. i will assume that you are comfortable with theory and econometrics at the first year phd level and are familiar with Matlab and R.

econ 291: social and economic networks (phd level)

syllabus

this course is aimed at economics phd students who have satisfied all the first year requirements. we will study the literature on social and economic networks. we will cover social learning, games on graphs, strategic network formation, statistical models of network formation, network effects and identification, among other topics. the course will consist of a mix of theory, statistical, and applied work. i will assume that you are comfortable with theory and econometrics at the first year phd level and are familiar with Matlab and R.