Data for dissertations November 2, 2017

36673 Eurobarometer 85.1 OVR: European Youth, April 2016

36798 Monitoring the Future: A Continuing Study of American Youth (12th-Grade Survey), 2016

36799 Monitoring the Future: A Continuing Study of American Youth (8th-and 10th-Grade Surveys), 2016

36813 Newly Licensed Registered Nurse Survey, 2009

36873 National Social Life, Health and Aging Project (NSHAP): Wave 3

A world without pensions and the future of the civil service

There’s been lots of press recently on the long term stability of public pensions and what must be done to fix the system. I won’t recount the debate but the upshot is that many pensions are systematically underfunded and the numbers get worse when pensions change their expectations about future market returns. Much of this has to do with actuaries’ admonitions that we should expect many people to live into their centenarian years. 

People who know more about this might dispute the degree of underfunding but nobody really knows anything about expected future market returns. It’s probably safe to say, given current CAPE 10 valuations, that the next decade won’t be pretty.

People who know more than I do about the market suggest there are two basic strategies.  One is to minimize expenses by investing in low-cost index funds; the other is esoteric investments chasing outside returns, such as in MLPs or New Zealand avocado farms or whatever. Of course, such strategies have different implications for volatility. 

Here’s the thing: we know that the pension storm is real and that things could get bad fast. Moreover, even if this is all just a political dynamic fueled by unnecessary fears, it doesn’t matter if the consequences are massive changes to the traditional public pension systems. 

What happens to the traditional civil service when this happens? Will people continue to become public school teachers? Will people demand higher base wages? Will the best and brightest seek jobs in the private sector instead?

Maybe we’ve seen all this before, but I doubt it. And I don’t hear much conversation that starts with the proposition that traditional public pensions won’t exist for much longer. It isn’t a matter of if, but when. 

Data for dissertations October 17, 2017

36371 The Attack on America and Civil Liberties Trade-Offs: A Three-Wave National Panel Survey, 2001-2004

36622 Johns Hopkins University Prevention Research Center – Risks for Transitions in Drug Use Among Urban Adults, Baltimore City, 2008-2011

36652 Afrobarometer Round 6: The Quality of Democracy and Governance in Burkina Faso, 2015

36662 Eurobarometer 82.2: Quality of Transport, Cyber Security, Value Added Tax, and Public Health, October 2014

36666 Eurobarometer 83.2: Perception of Security, Civil Protection, and Humanitarian Aid, March 2015

Design thinking in the public sector

I’m currently working on a project that reviews notable examples of the use of design thinking in the public sector. It’s centered on The Lab at OPM, USDS, and 18F, but I’m also learning about great initiatives at CDC, USDA, the VA, and Education, and in cities like Philadelphia and states like Rhode Island. 

This is a quick bleg asking for other hints or directions. Are there people I should know about who are pushing the envelope?

Comments are closed but you can reach me on Twitter at @abwhitford. 

The speed of information and the instability of public affairs

In 2013, it was discovered that 90% of the world’s data were created in the previous two years. I can only imagine how much faster the speed of information is these days. 

Most decision makers, though, process information just as they did 20 or 50 years ago – on paper, in bite-sized pieces. Some might claim that current decision makers, at least in politics, are less capable now at processing complex, high-dimensional information. 

What’s the impact of this imbalance? It’s easy to speculate, but I think there’s an argument to be made that one key outcome will be instability. 

With the addition of new data, at speed, the existing volume of information increases along with the difficulty of comparability. The likelihood of multidimensionality increases. The aggregation (or dimensional reduction) problem gets harder. 

We can hope that machine learning and mining technologies, probably fueled by AI, will save us. But I’m skeptical. Instead, I think it’s likely that instability increases. And that the demand for low-dimensional “rules of thumb” increases. And that the probability of failure of those rules also increases – if only because high-speed data means the world is changing quickly. 

Maybe I’m wrong. Comments are closed but feel free to correct my thinking on Twitter at @abwhitford. 

The problem of ephemeral data

You’ve collected data, analyzed/tortured them, written the paper, chosen a journal, and then submitted the paper for consideration. After a while, you hear back that the reviewers didn’t see enough promise to move forward so the editor has rejected it. You change some things, rinse, and repeat. Maybe it happens again. It’s expected in a world where many journals have acceptance rates lower than 10 percent. 

Finally, at some point, you receive an “R&R” – an opportunity to revise and resubmit your paper for further consideration. But the reviewers complain that the data are no longer “fresh” and require “updating.” What should you do?

Of course, most of us do whatever it takes and whatever is possible to close the R&R. The goal is to publish so it’s natural to jump through the hoops.

The problem of ephemeral data, though, is a philosophical one. If the data are truly “stale”, how does freshening them improve inference? 

If the world is changing that quickly, won’t even fresh data be outdated by the time the paper survives review, is accepted, is typeset, is processed, and finally is printed in the journal several years later? 

And won’t the data be stale when people notice the paper several years later when assembling reading lists for their own papers, syllabi, or students?

There’s no natural solution to this problem. Because researchers and practitioners meld together as one studies the other and the other changes behavior based on research, it is inevitable that data are ephemeral. The social “data generating process” is a moving target – and researchers are themselves embedded mechanisms.

Perhaps I have this wrong, though. Comments are closed, but please feel free to correct my thinking on Twitter at @abwhitford.