Skip to content
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
ACS_14_5YR_B19083.csv
README.md
gini.by.state.csv
mobility.by.county.csv
survey_data.csv

README.md

survey_data.csv; This dataset contains 1,441 survey responses from two qualtrics national panels. The key individual variables here are happiness, economic quintile, age, political ideology, and location (latitude and longitude). We collected these data in our lab as part of larger projects exploring the psychological correlates of perceived economic mobility.

ACS_14_5YR_B19083; this dataset is from the United States census and contains two county identifiers (FIPS code and county name), income inequality (Gini) for each county, and the standard error for each Gini coefficient.

gini.by.state; this dataset is also from the United States census and contains two state identifiers (FIPS code and state name), income inequality (Gini) for each state, and the standard error for each Gini coefficient.

mobility.by.county; this dataset is from the Harvard Mobility Project and contains a measure of income mobility, as well as various population demographics, for each county. The measure I will be using, absolute upward mobility, quantifies the average income percentile for a child whose parents were in the 25th percentile. So, for example, if a county has an absolute upward mobility value of 40 this means that the children of parents who were in the 25th percentile of the income distribution ended up, on average, in the 40th percentile.

You can’t perform that action at this time.