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Sep 11

SSRI/DuPRI Seminar Series ~ Intergenerational Transmission of Poverty and Inequality: Parental Resources and Schooling Attainment and Children's Human Capital in Ethiopia, India, Peru, and Vietnam*

ABSTRACT: Academic and policy literatures on intergenerational transmissions of poverty and inequality suggest that improving schooling attainment and income for parents in poor households will lessen poverty and inequality in their children's generation through increased human capital accumulated by their children. However, magnitudes of such effects are unknown. We use data on children born in the 21st century in four developing countries to simulate how changes in parents' schooling attainment and consumption would affect poverty and inequality in both the parent's and their children's generations. We find that increasing minimum schooling or income substantially reduces poverty and inequality in the parent's generation, but does not carry over to reducing poverty and inequality substantially in the children's generation. Therefore, while reductions in poverty and inequalities in the parents' generation are desirable in themselves to improve welfare among current adults, they are not likely to have large impacts in reducing poverty and particularly in reducing inequality in human capital in the next generation. Seminar #4388
Sep 15

DVS Workshop: Basic Data Cleaning and Analysis for Data Tables

Tables of data, like those you see in spreadsheets or relational databases, are the foundation of most data-driven research today. There are many pitfalls of working with these tables, though, that most people end up having to learn the hard way. In this workshop, we'll take a dataset that has a variety of different properties and learn to work through many common steps of data-driven research to clean and begin analyzing the data. We'll be using Excel to make sure the methods we suggest can be reproduced easily "at home," but many of these techniques are important for other data analysis tools as well. No data experience necessary.
Sep 16

DVS Workshop: Analysis with R

Explore the basics of the R programming language for statistics and graphing in this introductory workshop. This hands on workshop covers the basics of getting help, loading, managing, graphing, and analyzing data in R. No previous experience with R is required. Course materials will be available before the class for workshop participants.
Sep 17

DVS Workshop: Data Wrangling with Google Refine

Prepare and explore your data using this simple but powerful tool: normalizing, cleaning and reshaping without learning yet another programming language. Google Refine allows you to detect and fix inconsistencies in your data, transform your data from one structure to another, and provides a simple way to understand patterns in your data.
Sep 17

SSRI~ Workshop: Quantitative Analysis for Novice Researchers

This workshop targets advanced undergraduates and others planning their first independent quantitative research project (for example, an Independent Study or an Honors Thesis). It provides an overview of what quantitative analysis offers the researcher, what its shortcomings can be, and ways to structure data collection (or selection), data management, and model design to maximize the researcher's ability to generate results that provide meaningful tests of the hypotheses of interest. Registration required; please click "more information" to access the registration form.
Sep 18

SSRI/DuPRI Seminar Series ~ Wiki Surveys

ABSTRACT: In the social sciences, there is a longstanding tension between data collection methods that facilitate quantification and those that are open to unanticipated information. Advances in technology now enable new, hybrid methods that can combine some of the benefits of both approaches. Drawing inspiration both from online information aggregation systems like Wikipedia and from traditional survey research, we propose a new class of research instruments called wiki surveys. Just as Wikipedia evolves over time based on contributions from participants, we envision an evolving survey driven by contributions from respondents. We develop three general principles that underlie wiki surveys: they should be greedy, collaborative, and adaptive. Building on these principles, we develop methods for data collection and data analysis for one type of wiki survey, a pairwise wiki survey. We then present results from www.allourideas.org, a free and open-source website we created that enables groups all over the world to deploy wiki surveys. To date, more than 4,000 wiki surveys have been created, and they have collected over 200,000 ideas and 5 million votes. We describe the methodological challenges involved in collecting and analyzing this type of data and present a case study of a wiki survey created by the New York City Mayor's Office. [Joint work with Karen E.C. Levy] Seminar# 4378

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The Social Science Research Institute at Duke University is a part of Interdisciplinary Studies at Duke University.