Repeat After Me: The Quantity of Labor Demanded is Not Always Equal to the Quantity Supplied

I've been teaching a class on intermediate macroeconomics this quarter. Increasingly, over the past twenty years or more, intermediate macro classes at UCLA (and in many other top schools), have focused almost exclusively on economic growth. That reflected a bias in the profession, initiated by Finn Kydland and Ed Prescott, who persuaded macroeconomists to use the Ramsey growth model as a paradigm for business cycle theory. According to this Real Business Cycle view of the world, we should think about consumption, investment and employment 'as if' they were the optimal choices of a single representative agent with super human perception of the probabilities of future events. 

Although there were benefits to thinking more rigorously about inter-temporal choice, the RBC program as a whole led several generations of the brightest minds in the profession to stop thinking about the problem of economic fluctuations and to focus instead on economic growth. Kydland and Prescott assumed that labor is a commodity like any other and that any worker can quickly find a job at the market wage. In my view, the introduction of the shared belief that the labor market clears in every period, was a huge misstep for the science of macroeconomics that will take a long time to correct.

In my intermediate macroeconomics class, I am teaching business cycle theory from the perspective of Keynesian macroeconomics but I am grounding old Keynesian concepts in the theory of labor market search, based on my recent books (2010a, 2010b) and articles (2011, 2012, 2013a, 2013b).  I am going to use this blog to explain some insights that undergraduates can easily absorb that are adapted from my understanding of Keynes' General Theory. Today's post is about measuring employment.  In later posts, I will take up the challenge of constructing a theory to explain unemployment.

Ever since Robert Lucas introduced the idea of continuous labor market clearing, the idea that it may be useful to talk of something called 'involuntary unemployment' has been scoffed at by the academic chattering classes. It's time to fight back. The concept of 'involuntary unemployment' does not describe a loose notion that characterizes the sloppy work of heterodox economists from the dark side. It is a useful category that describes a group of workers who have difficulty finding jobs at existing market prices. 

The Impact of Financial Market Volatility on Emerging Market Economies

Early in the New Year, economists from all over the world will congregate in Boston for the 2015 annual meetings of the American Economics Association. The main purpose of these meetings is to interview new Ph.D. candidates for potential jobs as academics and in the public and private sectors as research and/or policy economists.  

Sangyup Choi

As an academic economist at UCLA, my job includes teaching undergraduates, carrying out economic research for publication in books and journals and, (my favorite part), training new Ph.D. economists. Teaching graduate students is a rewarding experience for an academic as we get to watch our students progress from undergraduates to colleagues. What begins as a teaching experience in year 1 ends up as a learning experience in year 5. 

Today's blog features my student, Sangyup (Sam) Choi, who is working on  the impact of financial market volatility on emerging market economies.  My colleague Aaron Tornell and I are Sam's principal advisors.

Sam is studying the VIX and its impact on economic activity. This is a hot topic amongst macroeconomists ever since Nick Bloom showed, in a paper published in Econometrica,  that shocks to uncertainty are a causal factor in US. recessions. What, you ask is the VIX?


The VIX is an index of volatility that goes up when traders are less certain about the future. In his Econometrica paper, Nick showed that shocks to the VIX are an independent causal factor that helps to predict future U.S. output. Here is a graph of the VIX for the period 2000 to 2014.

Figure 1: The VIX from 2000 to 2014

In a paper published last year in Economics Letters, Sam showed that Nick’s results are sensitive to the period of study. The VIX does predict future output in data from 1950 through 1982, but that result goes away after 1983. The largest recession in post war history in which the VIX jumped by a factor of four, (see Figure 1), did not have a significant independent impact on the U.S. economy, once other explanatory variables have been accounted for. That in itself is surprising. But it gets better.