Pre-conference Session: Data Bootcamp

Speakers

• Sebastian Hickey, Economic Policy Institute (EPI)
• Zane Mokhiber, Economic Policy Institute (EPI)
• Ben Zipperer, Economic Policy Institute (EPI)

Session Description

In this session, participants will use Current Population Survey (CPS) data to perform analyses in statistical software packages Stata and R.

• Data analysis in R
  1:30 – 2:40 pm ET
  We will use R to inflation adjust minimum wage data and supplement this analysis using the EPI Current Population Survey extracts. In general we will show how to produce basic tables and graphics using data downloaded from the BLS and other public sources. Beginners are welcome, but it will help if everyone has basic familiarity with the programming concepts introduced in the R workshops at last year’s data bootcamp.
  Key concepts: Data analysis in R, inflation adjustment, EPI CPS microdata extracts
• Introducing the new EARN code library
  2:50 – 3:10 pm ET
  EARN is excited to announce a new resource for network members: The EARN code library. The code library will make it easier for EARN researchers to share their own work and learn from analysis done by EPI and other EARN groups. In this session we will go over the components of the code library, the process for adding new projects to the repository, and the plan for launching the code library website.
• Data analysis in Stata
  3:20 – 4:30 pm ET
  In this 70-minute module, we will run a basic analysis of BLS published statistics and Current Population Survey (CPS) microdata. This module will assume basic proficiency in Stata and will build on concepts learned in last year’s data bootcamp modules. This module will cover using published state level statistics from BLS in Stata in a “tidy” manner, supplementing these published statistics with CPS microdata to allow for more fine grain analysis, dealing with small sample sizes and data volatility over time using the CPS microdata, and finally, putting it all together to produce a presentable table and graphics.
  Key concepts: Tidy data analysis in Stata, dealing with small sample sizes, data smoothing (dealing with volatility and clumping), EPI CPS microdata extracts