Does Cohabitation Predict Divorce?

My article, coauthored with Sarah Kerrigan and published last week, tries to answer the question. In short, the answer seems to be yes- cohabitation before marriage is associated with a 4.6 percentage point increase in the rate of marital dissolution. This is in line with much of the previous literature, which notes one big exception- choosing right (or getting lucky) the first time: “cohabitation had a significant negative association with marital stability, except when the cohabitation was with the eventual marriage partner”.

But we found some even more interesting facts while digging through the National Survey of Family Growth.

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John Duffy Experiments and Crypto

John Duffy and Daniela Puzzello published a paper in 2014 on adopting fiat money. I think of that paper when I hear the ever-more-frequent discussions of crypto currencies around me. To research the topic, I went to John Duffy’s website. There I found a May 2021 working paper about adopting new currencies in which they directly reference crypto. Before explaining that interesting new paper, first I will summarize the 2014 paper “Gift Exchange versus Monetary Exchange.”

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Population Predicts Regulation

Texas is one of the most regulated states in the country.

This is one of the surprises that emerged from the State RegData project, which quantifies the number of regulatory restrictions in force in each state. It turns out that a state’s population size, rather than political ideology or any thing else, is the best predictor of its regulations.

This is what I found, with my coauthors James Broughel and Patrick McLaughlin, when we set out to test whether a previous paper (Mulligan and Shliefer 2005) that showed a regulation-population link held up when we used the better data that is now available. We found that across states, a doubling of population size is associated with a 22 to 33 percent increase in regulation.

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Bad Jobs Exist

I’m James Bailey, an economist at Providence College who studies how government policies affect health care and the labor market. Thanks to Joy for the chance to join the blog for a few months!

For my first post, I have to share the brand new book I wrote a chapter of, “Regulation and Economic Opportunity: Blueprints for Reform“. Normally academic volumes like this are sold for hundreds of dollars, so only a few people with access to academic libraries end up reading them. But the publisher of this volume, the Center for Growth and Opportunity, released it as a free Ebook– so I hope you’ll check it out. It covers everything from housing and health care to energy and education to beer and cigarettes.

I wrote chapter 5, on how various regulations affect wages and employment. Here’s an excerpt:

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Current Research on the Gig Economy – Palagashvili

Online platforms are allowing us to trade used goods more easily than before. Similarly, sites like UpWork and Uber are making it easier to trade small blocks of human labor. Since the gig economy is growing (as documented by Dimitri Koustas), it’s important to understand how it is affecting workers.

Liya Palagashvili of Mercatus has a working paper with Paoula Suarez “Women as Independent Workers in the Gig Economy” examining particularly how the growing opportunities to work on a gig basis has affected women in different ways than men. They note, for example, that (in 2014–2015) 87 percent of independent workers on the Etsy platform were female, while 14 percent of workers on Uber’s platform were female.

Abstract: New technologies and digital platforms have ushered in a rise of gig, freelance, contract, and other types of independent work. Although independent workers and the gig economy as a whole have received plenty of attention, little research has examined the heterogeneity of work characteristics among different independent work opportunities, specifically as it relates to the participation of women in this workforce. Existing data indicate that some digital platforms are more male dominated, whereas others are more female dominated. What accounts for these differences? In this paper, we empirically examine the heterogeneity of work within independent work opportunities in relation to female participation by analyzing work characteristics in the United States from the Occupational Information Network (O*Net) database that reflect greater temporal flexibility, which has been shown to vary across occupations and to attract more female workers. Our findings suggest that women in the independent work context do self-select into the types of independent work jobs that reflect greater temporal flexibility, as is the case for women working in traditional employment. However, our findings also reveal that the way in which the existing literature measures temporal flexibility in traditional work settings may not be the same as the way it is measured in the context of independent work. We discuss the implications of our findings for public policy and labor laws. (emphasis mine)

Current Research on the Gig Economy – Koustas

Dmitri Koustas of U. Chicago has a forthcoming paper “Is New Platform Work Different than Other Freelancing?”

Abstract: The rise of freelance work in the online platform economy (OPE) has received considerable media and policy attention in recent years, but freelance work is by no means a new phenomenon. In this paper, we draw on I.R.S. tax records to identify instances when workers begin doing online platform work versus other freelance/independent contractor “gig” work for firms. We find gig work occurs around major reductions in outside income, and document usage over the lifecycle. Our results provide suggestive evidence on motivations for entering into each type of work. (emphasis mine)

His work was cited in the LA Times last year

people take on this work primarily because they’ve lost a job or some of their income — and particularly for younger workers, app-based services have been significantly more lucrative than more traditional side hustles.

I got to (virtually) talk to Dmitri Koustas, who is now a leading expert on gig work, this week. He became interested in the gig economy when he was thinking through a more traditional econ. question of generally how people modulate their labor supply in response to income shocks.

He also has a working paper “Is Gig Work Replacing Traditional Employment? Evidence from Two Decades of Tax Returns”

First half of the Abstract: We examine the universe of tax returns in order to reconcile seemingly contradictory facts about the rise of alternative work arrangements in the United States. Focusing on workers in the “1099 workforce,” we document the share of the workforce with income from alternative, non-employee work arrangements has grown by 1.9 percentage points of the workforce from 2000 to 2016. More than half of this increase occurred over 2013 to 2016 and can be attributed almost entirely to dramatic growth among gigs mediated through online labor platforms. We find that the rise in online platform work for labor is driven by earnings that are secondary and supplemental sources of income. Many of these jobs do not show up in self-employment tax records… (emphasis mine)

Old Lives Matter

Bryan Caplan has kindly responded to my latest blog post, which was in turn a response to his blog post on the relative value of human lives by age. Caplan has always been kind in his responses, even when responding to pesky graduate students — kind in both his approach and the time he dedicates to responding thoughtfully. So I appreciate his taking the time to respond to me, and I will offer a few more thoughts on the matter.

To briefly summarize: Caplan believes that young lives (10 year olds) are worth 100-1,000 as much as old lives (80 year olds). I contend that they are closer to roughly equally valued. My disagreement with Caplan can be broken down into two categories:

  • A. Caplan’s three reasons why young lives are worth more (a lot more!) than old lives. I didn’t respond to that directly, but I will do so here. I think Caplan is narrowing the goalposts.
  • B. A disagreement over the shape of the VSL curve over the lifetime, specifically whether an inverted-U-shaped curve makes sense. I’ll say more about this too, but Caplan doesn’t just have a beef with me, but with almost everyone in the VSL literature!

Let’s start with Caplan’s three reasons, which he calls “iron-clad”: young people have more years to live, those years are generally healthier, and young people will be missed more when they are gone. The first in undeniably true on average, the second is probably true almost all the time, and I’m not sure on the third, but I’m willing to admit it’s not a slam dunk either way.

So how can I disagree? These are only three things. There are many other considerations, and we can imagine other reasons that old lives are valued as much or more than younger lives! I’ll call mine 4-6 to go with Caplan’s 1-3:

  1. Old age spending is the largest component of public budgets in developed countries (and this is unlikely mostly due to rent seeking or the self interest of younger generations).
  2. The elderly possess wisdom which is highly valuable and that the young benefit from.
  3. The last years of your life are, on average, worth a lot more — you are usually very wealthy, have no employment obligations, you have grandchildren you love (without the responsibilities of parenting), and are (until the very end) generally healthy too.

Taken as a whole, I think these three reasons present a strong counterargument to Caplan’s three reasons. And I think we could certainly come up with more! My point being that Caplan has picked three areas where clearly young lives have the advantage, but ignored all the good reasons why old lives are more valuable. These is what I mean by we shouldn’t rely on our intuitions. Neither of our lists are exhaustive, but let me elaborate on a few of these.

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Economic Research on COVID-19

The past 12 months has been dominated by COVID-19, the related recession, the government response, and other matters. But it has not just dominated our lives, it has also dominated new research, including research by economists!

Working papers from the National Bureau of Economic Research are one place to track on-going research by economists. While not all economic research is released as an NBER working paper (there are other series, and some economists just post them on their own website or department page), the volume of NBER papers should tell us something about the trends.

Here’s a chart showing the weekly NBER working papers that are in some way related to COVID-19. The first batch of three papers was released in late February, one long year ago. The second batch of nine papers came one month later. Since then, there have been papers released every single week, with the exception of the week of Christmas.

In total, there have 373 papers released that relate to COVID-19. The peak comes in late May and early June, with 61 papers released in a 4-week period and 21 of those papers coming out on May 25 alone. Since the May-June peak, we’ve seen a slow decline in papers on COVID-19, and we are now at our lowest level, with just 14 papers released in the past 4 weeks.

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Talking about redistribution in the lab

I am grateful to Yang Zhou for inviting me to talk about a working paper (with Gavin Roberts) on Friday. Yang told me that this audience is not familiar with lab experiments, so I’m going to take a few minutes out of my time to set the stage for my research.

There is a new book out, Causal Inference by Scott Cunningham, that is the talk of #EconTwitter (Cunningham, 2021). The book is 500 pages of dense prose and code. Here is a review saying that Cunningham left out many key things that a practitioner would need to know. Causal inference from naturally occurring data is hard!

Lab experiments bring something important to the research community. Lab experiments give the researcher a lot of control, which is why they are particularly useful for causal inference  (Samek, 2019).

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