Why Participation Is Down
There have been many attempts to answer this question: Is the decline in the U.S. labor force participation rate structural or cyclical? Or, more precisely, to what extent is it either one?
And there have been so many attempts because it really is an important question. Think about the economy as a big machine that takes three inputs -- technology, labor, and capital -- and produces output. The drop in the labor force means that the U.S. has forfeited, perhaps permanently, that labor input and whatever marginal output it would have yielded. A simple calculation1 suggests that the share of output lost is about three percent; more in-depth calculations from Reifschneider, Wascher, and Wilcox (2013) place it at the center of their estimate of a seven-percent drop in potential output. That's a lot. You don't blow three percent of GDP, let alone seven, every day.
Another reason that economists keep coming back to the labor force participation rate is that, ominously, it keeps falling. Not only does that render much of the research overtaken by events, but also the data presents a challenge to reports that see the decline as cyclical and transitory.
I'd also say that the reason that the research continues2 is because it hasn't settled on a single analytical framework. That's not necessarily a bad thing at all, as disagreement over methods forces researchers to reconcile differences in results rather than herd around a single conclusion. Yet to a certain degree it reflects dissatisfaction with the methods offered so far.
In this post I take an approach that is mostly3 new to the question of the decline in the labor force participation rate but will be familiar to most labor economists, the Blinder-Oaxaca decomposition of a probit model for the labor force participation decision. I use microdata from the March 2007 and 2013 supplements to the Current Population Survey, downloaded from IPUMS. I conclude that, of the 2.8-percentage-point decline in the labor force participation rate over that six-year period, more than half (1.7 percentage points) can be explained by underlying changes in demography, though a substantial fraction (1.1 percentage points) cannot.
For the majority of my audience that has no idea what a Blinder-Oaxaca decomposition is, here's a quick 101. It's a statistical technique invented by Alan Blinder and Ronald Oaxaca in 1973 that takes the change in a variable and determines how much of it can be explained by a set of other variables in a model and how much can't. (Note: What comes next gets rather mathy, but you can skip down to "My idea..." if math isn't your thing.)
For example, Blinder and Oaxaca both wanted to understand why people differ in their earnings. Let's say that you think pay is determined by a bunch of factors, like your education, work experience, occupation, and so on. Let's put all of those factors into a matrix X, which contains data on lots of people. Let's put all of their earnings into another matrix Y. Then we can estimate the impact of all of those factors by an ordinary least squares regression:
It also turns out that there's no single category that absorbs most of the unexplained share. In fact, the model puts almost all of the unexplained share into a constant. Which basically means that the model is saying, "Whatever your background, take what your probability of being in the labor force was in 2007 and mark it down by some amount for your 2013 probability." I found this compelling evidence that what our model says is unexplained really is the business cycle, and not some omitted structural explanation.
I've been meaning to write a post on this for a long time. It is the analytical challenge of our era for economists. It's taken me so long to put together an estimate because I wanted an approach I could defend.
I should also mention some shortcomings of this analysis. One of them is that I've only used data from two months, the March 2007 and 2013 CPS supplements. This was mainly out of convenience, as that was the data available on IPUMS, the database I linked to earlier.
Another concern is the obvious endogeneity problem with education. That is, if the economy's terrible, that affects your decision of whether to work now or to go back to school. But note that this problem is insoluble without a model of how the economy affects education decisions, something well beyond the scope of my work here. What my work suggests, though, is that this exercise is worthwhile. Since you get a year older every year, there's not a lot of mystery to the aging-working link. But, since we know now that education decisions were actually important to driving down overall labor force participation, maybe we should go back and think about it carefully.
A final concern is that a lot of the prior research I looked at includes what are called "cohort effects," that is, you think about labor force participation evolving differently for different generations of people, based on their pre-recession starting age. I don't do that in this model. If cohorts matter, this approach will miss it.
Part of my hope of writing this post, whether or not you agree with the overall conclusion, is to enlighten people about the explanatory power of all the theories on the table. If you're on the right, and walk away from this post saying, "Gosh, I wasn't convinced that the decline in the labor force participation rate is partly cyclical, but wow, maybe it really isn't all about more people on welfare," I'll take that as a victory. Or, if you're on the left, and think, "Gosh, I wasn't convinced that the decline in the labor force participation rate is more than half structural, but wow, maybe aging is a bigger part of the story than I thought," I'll also take that as a victory. And, for sure, this won't be the last word. There are many other compelling approaches, each with their advantages and disadvantages. But I think this is an important one that needs to be added to the conversation.
If you have questions, I'm happy to answer them in the comments.
2: You can find a good literature review in Erceg and Levin (2003).
3: There is an exception, Hotchkiss and Rios-Avila (2013). But it does something I think is not good, which is that it includes a measure of labor-market conditions. My approach differs importantly in that I don't include one because I want to see the conclusions of the model without telling it about the recession. I have some other concerns about the particular measure they've chosen and whether we really can include it in the model if it is codetermined with labor force participation.
Update: I've made my fully cleaned up .dta file available for direct download here.
Alan Reynolds of the Cato Institute asked me to try repeating the decomposition with broader measures of welfare programs -- the one I used originally was narrow, i.e. TANF, and Reynolds wanted SNAP (food stamps), Medicare, and Medicaid.
Following other ideas in the comments, I also included cubic and quartic terms in the age, so as to better approximate the curve of the LFPR in the cage. I found that inclusion of the extra age terms didn't do much.
I found that the increase in the fraction receiving public health insurance was an important explanatory variable for the decline of the labor force participation rate: It explains about 0.6 percentage points. I found the increase from SNAP was rather small: It explains 0.2 percentage points. In the new specification, fully 2.5 percentage points of the 2.8 percentage point from in the LFPR is explained by changes in the composition of the workforce.
I would strongly caution Alan, or anyone really, from interpreting this as a causal result. Don't conclude that because Obama expanded Medicaid and food stamps, those new recipients aren't working any more. I imagine that most of this growth was the result of the business cycle. The causal pathway probably goes from unemployment to those programs. I am aware Medicaid expanded permanently, but there is no way to disentangle this.