On December 13, the City Club of Eugene hosted a forum on asset poverty and invited me to give a presentation.Continue reading
These ‘moving bubbles charts’ have caught my attention.
Most recently, I found this one from the Generations and Gender Programme (GGP) that shows family structure, number of children and age across a number of countries. (see bottom of page)
The GGP plot cites this Nathan Yau piece from Flowing Data A Day in the Life: Work and Home.
And one of the first I’ve seen was from CBPP showing SNAP churn, Most SNAP Participants Move In and Out of Work: An Animated Look.
I expect this type of visualization will be helpful as we consider program dynamics (entry and exit) from Oregon’s Self-Sufficiency programs.
I recently presented research at the 23rd Society for Social Work and Research Conference in San Francisco. With Tim Ottusch and Katie Cherney, we analyzed the increase in student loan debt over time and the relationship with financial insecurity, defined as negative net worth (total assets > total debts). The focus was on young adults age 25-45. Additionally, we considered 3 counterfactual scenarios: (1) financial insecurity under conditions of zero student debt, (2) financial insecurity under student debt levels held constant at 1995 levels, (3) financial insecurity in 2016 under student debt levels held by Canadians. Below is one of the main figures. On the vertical axis is rate of financial insecurity (negative net worth) over time (x axis). The black line displays the observed increase over time. The orange line is the counterfactual scenario of what financial insecurity would be if student loans were eliminated, with other components of net worth left unchanged. The takeaway is the student loan debt explains much more of financial insecurity today than they did twenty years ago.
See full slides here.
This was part of a symposium I analyzed Financial Well-Being across the Life Course.
Over the years I’ve compiled resources to help graduate students succeed. I’ve come across several more recent posts that provide new and different resources. I include them here as a list of resources to avoid searching for them every time. More recent posts appear first.
Amanda Agan compiled list of resources for writing, presenting, and reviewing: here.
The Twitter thread compiled by Mathew E. Hauer will help you learn the basics of open-access and reproducible research (full list here). Even if you are not ready to make the jump to R, the philosophy is the future of social science.
How to write paragraphs. From the LSE Impact blog
Study, productivity, and self-care tips from Claire Kamp Dush:
How to tell the policy narrative, by my OSU colleague Michael Jones:
Identify scholars who have given this considerable thought. Chris Blattman (see professional Advice section) and Raul Pacheco-Vega come to mind. Raul’s posts the dissertation two pager will help most students focus on the essentials.
Last but not least, you should have a hobby or two.
Some resources by Hugh Kearns and Maria Gardiner (via Megan McClelland) on the 7 Secrets of successful grad students. Kearns.Gardiner.2011.7Secrets, Kearns.Gardiner.2011.Advisor, Kearns.Gardiner.2011.Motivation.
I intend to update this page with other resources. Let me know if you have others I should add or if the links are broken.
Here we post the Stata code and results for the talk “What’s behind Oregon’s rising rural child poverty? Changing economies and families.” delivered at the 2017 Oregon Parenting Education Collaborative Conference.
Here we post the Stata code for the talk Family [work] structure and the rise in Oregon’s rural child poverty rate.
Recently I’ve needed to plot compositional data by one or more groups. These are usually in the form of a categorical variable (ordered or not) and a binary variable to distinguish two groups; e.g., minority status or poverty (0/1). I was struggling to plot the categorical variable across the two groups so that the bars sum to 100% for each group. Let’s start with a simple example.
Data is from IPUMS. My data is (here) with setup (here). We have an exhaustive five category grouping of family structure: (1) two adults no working woman, (2) two adults with working woman, (3) single woman not working, (4) single woman working, and (5) single male.
Let’s say we want to examine poverty status across family structure. Poverty is measured using the US Census official poverty measure. We want to analyze the family structure compositions of the poor versus non-poor. Continue reading