Call for Papers

Urban Affairs Association, Spring 2009 in Chicago

Southern Sociological Society, Spring 2009 in New Orleans

New Research 2

OK, so the second paper that's been emerging is going to be called something like "Unemployment in the Upper Tidewater: A Job Flows Approach".

Background: Northern Virginia and Southern Maryland have had impressively low unemployment rates for the last two decades or so. I've read reports in the business press (which I should really track down again for this paper) refering to the region as "recession-proof". Oft-cited reasons are government contracts and the IT boom in the region, and the explosive population growth which keeps construction and service workers employed as well. It's very insightful to look at the BLS's LAUS (local area unemployment statistics) maps. Maps for 1999 and 2000 - before the last recession - are especially instructive. Northern Virginia is one big patch of white on the map's color scale - under 2% unemployment. The largest area of land in the country that had unemployment under 2% at the time. Not far to the west, West Virginia is dark purple - one of the largest contiguous areas of high unemployment in the country.

So lots of justifications for why Northern Virginia does so well have been forthcoming - one other explanation I want to explore is the relationship between job flows and unemployment. There are two job flows that people look at: job creation, and job destruction. Job creation occurs when a firm expands its workforce. Job creation statistics are different from hiring and firing statistics. A firm of 50 employs can hire 25 employees and fire 25 employees, and they've added nothing to job creation statistics. Job destruction is just the opposite - the number of positions a company has eliminated. So job creation and job destruction tell a slightly different story from employment, hiring, firing, and unemployment statistics. Net change in employment has to equal job creation minus job destruction - but the job flow dynamics in an economy may differ from their employment dynamics.

John Haltiwanger, of the University of Maryland, has lead the way in research on job creation and destruction. In his most famous book on the subject (creatively called "Job Creation and Destruction"), Haltiwanger identifies many properites of job flows, and in one chapter explores the relationship between job flows and unemployment. The overarching conclusion is that during periods of high unemployment, job destruction spikes and job creation remains relatively constant. It sounds fairly straightforward, but it's an important finding with policy implications. The policy response to this finding would be different, for example, from the policy response if job destruction stayed relatively constant during recessions, and job creation declined significantly.

The problem with Haltiwanger's research is that he primarily looks at aggregate job flow and unemployment statistics. However, the Census's Quarterly Workforce Indicators provide county level job flow data to track whether these relationships occur at the local level. The problem is, these data are only available for certain states at certain times. Very important states - like Massachusetts, Ohio, and New York haven't even produced any. Some, like Maryland, produce the statistics as far back as 1990. So it's a crapshoot. I've been poking around each state's website to see how good their statistics are, and I've discovered that every state in the Fifth Federal Reserve District (Virginia, Maryland, West Virginia, DC, North Carolina, and South Carolina) have data going back to at least 1998 - in other words, covering the last recession.

My plan is to run Haltiwanger's basic analysis on every state in these counties, and determine whether high unemployment counties have a different relationship between job flows and unemployment than low unemployment counties in the district. In other words, does the Upper Tidewater behave just like Haltiwanger's national statistics - only with a smaller increase in job destruction - or does it show different patterns entirely? Perhaps the Upper Tidewater sees a spike of job creation and job destruction during recessions - a sort of "creative destruction" a la Schumpeter. I think it should be interesting.

If things work out, all these stats are available by industry as well.

I envision this being presented at a brown bag at work, where I'll refine it, and then submitted to a minor labor economics journal. Perhaps the Journal of Labor Research, which is published by George Mason. I want a good shot of getting in and publishing something in a journal for once - that's all. I think it's a decent idea - and it would be useful for policymakers in schizophrenic states with areas of very high and very low unemployment. Or even for whole countries where this occurs, like Belgium... in fact, I wonder if Belgium publishes gross job flow statistics....

New Research

OK, I'm far enough along on two "back burner" research projects that I think it's worth talking about them here.

The first is a paper that I'll be presenting at the Southern Economic Association conference in D.C. this November. It addresses the issue of "skills mismatch" in the United States, or more accurately - it computes an index of skills mismatch suggested by Petrongolo and Pissarides (2001) that I haven't seen estimated empirically anywhere and was curious about.

There are lots of "matching functions" out there that try to describe frictions involved with matching one party to another in any of a variety of transactions. The matching I'm concerned with is job matching. A job matching function is usually of the form: M = z(U, V). The number of matches is a function of the number of unemployed workers, the number of job vacancies, and a matching technology z. I won't get into the weeds here, but there is a variation on this model called the "ball-urn model" that looks like this: M = V(1-e^(U/V)). Petrongolo and Pissarides (2001) mention a variation of the ball-urn model that includes "K" - an index of skills mismatch: M = V(1-e^(KU/V)). With a little math, you can solve for K (although they don't). K is the percent of unemployed workers who are qualified for a job vacancy. As K goes up, the second equation I presented converges to the first equation. Anyway, its a nifty, easy little index they suggest that even this public policy grad student can understand - so I thought, why not calculate it for the U.S., and see what I can say about (1.) whether this is even a valuable index, and (2.) trends in skills mismatch in the U.S.

I use the Job Opening and Labor Turnover Survey (JOLTS) data produced by the Bureau of Labor Statistics, along with unemployment figures. The JOLTS data are relatively new - going back only to 2000 - but it is produced monthly, so there's a fair amount of data points. My calculations for K are below:
Conclusions so far? K is clearly very cyclical - it goes up in the summer and down in the winter. Not sure why this could be yet - I don't know much about the seasonality of unemployment. But basically that says that unemployed workers are more qualified for summer jobs than they are for other jobs. I guess that's believable... What is kind of neat is that K is not responsive to the U/V ratio (the red line above). The U/V ratio (also known as the Beveridge Curve) is an index of labor market tightness. When the ratio is high, it means that there are a lot of workers chasing very few jobs. If it is low, it means that there are a lot of jobs out there available for workers. U/V is also the denominator of K when you solve the matching function I presented above. So you would expect that when U/V increases dramatically (as it does in the first few months of the graph), K will decrease. Maybe you could convince me there's a slight drop at that period in time, but not really. K stays very consistent. This is probably a good thing. I don't think many people would be covinced by the idea that skills shortages in the US economy swing dramatically over time. If there are too many workers chasing too few jobs or too few workers chasing too many jobs, you would think the skills ratios among workers and jobs should stay pretty constant.

Next on the list:

(1.) measuring a standard Cobb-Douglas matching function (M = b0 + b1ln(U) + b2ln(V)), allowing b0 to vary over time. b0 is equivalent to the matching technology "z", that I mentioned above. It's an index of general frictions in matching. I'll then chart K against z to determine whether K is really picking up skills mismatch, or whether its just "the share of workers who have a tough time matching to jobs."

(2.) The American Community Survey collects information on the time it takes to travel to work, which could be a proxy for spatial mismatch - one of the most common types of mismatch discussed in the literature. Its an annual survey, unfortunately, so I won't get nearly as many data points - but I want to compare changes in spatial mismatch to K to make sure K isn't picking up those changes accidentally.

(3.) I'm going to reestimate K after differentiating between full time and part time job seekers. In effect, the equation will look like this: M = V(1-e^((-KFT + PT)/V)). This version of K assumes that all part-time job seekers are qualified for whatever jobs they may seek, but some full time job seekers may not be. High paid consultants aside, this seems like a reasonable assumption... and it may get some different dynamics for K.

OK, I need to get a few things ready for work now, so I won't talk about my second research project right now - but here's a teaser title for you:

"Unemployment in the Upper Tidewater: A Job Flows Explanation"... using Quarterly Workforce Indicators data from Census.