The Great Recession ended more than nine years ago, in June 2009. The US unemployment rate declined slowly after that, but it has now been below 5.0% every month for more than two years, since September 2015. Thus, an ongoing mystery for the US economy is: Why haven't wages started to rise more quickly as the labor market conditions improved? Jay Shambaugh, Ryan Nunn, Patrick Liu, and Greg Nantz provide some factual background to address this question in "Thirteen Facts about Wage Growth," written for the Hamilton Project at the Brookings Institution (September 2017). The second part of the report addresses the question: "How Strong Has Wage Growth Been since the Great Recession?"
There may be no more important question for the future of the US economy than whether the ongoing advances in information technology and artificial intelligence will eventually (and this "eventually" is central to their argument) translate into substantial productivity gains. Erik Brynjolfsson, Daniel Rock, and Chad Syverson make the case for optimism in "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics" (NBER Working Paper 24001, November 2017). The paper isn't freely available online, but many readers will have access to NBER working papers through their library. The essay will eventually be part of a conference volume on The Economics of Artificial Intelligence.
Discussions of how advances in technology, trade, and other factors lead to disruption of jobs often seems to begin with an implicit claim that it was all better in the past, when the assumption seems to be that most workers had well-paid, secure, and life-long jobs. Of course, we all know that this story isn't quite right. After all, about one-half of US workers were in agriculture in 1870, down to one-third by early in the 20th century, and less than 3% since the mid-1980s. About one-third of all US nonagricultural workers were in manufacturing in 1950, and that has now dropped to about 10%. These sorts of shifts suggest that job disruption and shifts in occupation have been a major force in the US economy throughout its history.
A few months ago, I wrote this article at the World Economic Forum called “A Yellow Card For The Global Economy“. It tried to serve as a warning on the rising imbalances of the emerging and leading economies. Unfortunately, since then, those imbalances have continued to rise and market complacency reached new highs.
For economists, "prime-age" refers to the ages between 25-54, which is post-school and pre-retirement for most workers. Didem Tüzemen asks "Why Are Prime-Age Men Vanishing from the Labor Force?" in the Economic Review of the Federal Reserve Bank of Kansas City (First Quarter 2018, pp. 5-28). She begins: "The labor force participation rate for prime-age men (age 25 to 54) in the United States has declined dramatically since the 1960s, but the decline has accelerated more recently. From 1996 to 2016, the share of prime-age men either working or actively looking for work decreased from 91.8 percent to 88.6 percent. In 1996, 4.6 million prime-age men did not participate in the labor force. By 2016, this number had risen to 7.1 million."
For most of us, most of the everyday health care we get is from a primary care doctor. But there's a limited number of primary care doctors, not enough to match the number of patients, especially in rural areas. An option slowly being used more broadly across the US health care system is let nurse practitioners (NPs) do primary care. Peter Buerhaus makes the case for accelerating this movement in "Nurse Practitioners: A Solution to America's Primary Care Crisis," written for the American Enterprise Institute (September 2018).
"Two-thirds of those released from prison in the United States will be re-arrested within three years, creating an incarceration cycle that is detrimental to individuals, families, and communities." So writes Jennifer L. Doleac in "Strategies to productively reincorporate the formerly-incarcerated into communities: A review of the literature" (posted on SSRN, July 21, 2018), Doleac's approach is straightforward: look at the studies. In particular, look at fairly recent studies done since 2010 that use a "randomized controlled trial" approach--that is, an approach where a group of participants are randomly assigned either to receive a particular program or not to receive it. When this approach is carried out effectively, comparing the "treatment group" and the "control group" provides a reasonable basis for drawing inferences about what works and what doesn't.