Research

Most of my research centers on the processes of microeconomic adjustment dynamics.  That is, I am interested how and when firms (or establishments) adjust to stochastic shocks to factor and product markets.  Schumpeter, in his book Capitalism, Socialism, and Democracy, wrote, "The fundamental impulse that keeps the capitalist engine in motion comes from the new consumers' goods. the new methods of production and transportation, the new markets...[The process] incessantly revolutionizes from within, incessantly destroying the old one, incessantly creating a new one.  This process of Creative Destruction is the essential fact about capitalism."  This sentiment underlies my interest; the Process of Creative Destruction.

So, I have some working papers that examine a number margins on which firms can adjust to the Creative Destruction process.  Some of these involve using acquisitions as a corrective mechanism for deteriorating productivity, how resource exhaustion changes the hazard of business failure, and how jobs are created and destroyed.

Here are some abstracts to some papers that come from my dissertation titled "Microeconomic Adjustment Dynamics in U.S. Coal Mining."

"Acquisitions and Productivity in U.S. Coal Mining"

Abstract.  This paper extends the literature on the productivity incentives for mergers and acquisitions.  We develop a stochastic matching model that describes the conditions under which a coal mine will change owners.  This model suggests two empirically testable hypotheses: i.  acquired mines will exhibit low productivity prior to being acquired relative to non-acquired mines and ii.  extant acquired mines will show post-acquisition productivity improvements over their pre-acquisition productivity levels.  Using a unique micro data set on the universe of U.S. coal mines observed from 1978 to 1996, it is estimated that acquired coal mines are significantly less productive than non-acquired mines prior to having been acquired.  Additionally, there is observable and significant evidence of post-acquisition productivity improvements.  Finally, it is found that having been acquired positively and significantly influences the likelihood that a coal mine fails.

"Implications of Resource Exhaustion on Exit Patterns in U.S. Coal Mining"

Abstract.  This paper examines the determinants of mine closure in the U.S. coal mining industry.  Based on a notion of firm dynamics that incorporates energy market demand conditions, productivity differences, and resource exhaustion, Cox proportional hazard models are estimated to examine the competing nature of market and productivity effects versus resource depletion effects.  These models are estimated using a unique panel data set with multiple failures containing the statistical universe of coal mines observed from 1974 to 1995.  It is found, consistent with the theoretical structure, that favorable energy market demand conditions as well as productivity differences tend to lower the time to failure, while older mines tend to have shorter time to failure, ceteris paribus.

"Gross Employment Flows in U.S. Coal Mining" (with Timothy Dunne)

Abstract.  This paper examines the patterns of job creation and destruction in the U.S. coal mining industry and compares those patterns to known regularities in U.S. manufacturing.  Using a unique panel data set containing the statistical universe of coal mines from 1973 to 1996, this paper calculates rates of job creation, job destruction, job reallocation, and excess job reallocation.  The main findings of this paper are as follows: i.  Annual employment flows in coal mining are substantially higher than corresponding flows in manufacturing, ii.  The high rate of job creation, destruction, and reallocation is attributable in part to mine openings and closing, iii.  A significant amount of job destruction and job creation is attributable to temporary shutdowns and re-openings, and iv.  Job destruction is counter-cyclical and more volatile that job creation—similar to the known patterns in manufacturing.  (Note: Here is a version of this paper that is currently under review at Economics Letters.)

I also have worked on a project where industrial organization meets regional science.  We are interested in whether there is more or less geographic concentration of industries (or Silicon valley effects)  in wholesale and retail than was found in manufacturing. 

"Geographic Concentration in U.S. Wholesale and Retail Sectors" (with Shawn D. Klimek)

Because they are fun, I also collaborate on policy topics as well.  In fact, my first publication was a policy paper on the effectiveness of state vehicle safety inspections.  Recently, I am working on a project that examines the cost and benefits of a tornado shelter/saferoom program initiated in Oklahoma; here is an abstract and a link to this paper.

Tornadoes

Finally, as a part of my job at the U.S. Bureau of the Census, I am involved with a team charged wiht converting historical economic census data from the old Standard Industrial Classification system to the new North American Industry Classification System that was implemented at part of the NAFTA treaty.  This change severely threatened the ability to compare Economic Census data over time.  Basically, we had to develop and implement a statistical methodology that allows the Census Bureau to produce high quality aggregate tabulations while simultaneously preserving the underlying microdata that can be used for research purposes.  This methodology has a lot of bells and whistles that we use to try to get some handle on measurement errors for the aggregates and to make sure that the random assignments are made as correctly as possible (probabilistically speaking) on the microdata.

"On Reclassifying Industries from the Standard Industrial Classification System to the North American Industry Classification System" (with Shawn D. Klimek)

Abstract.  The North American Industry Classification System (NAICS) presents significant challenges to users of Census Bureau economic data by limiting the time series dimension of the data. We develop an algorithm to reclassify the 1992 Retail and Wholesale Economic Censuses on a NAICS basis.  First, we use a SIC-NAICS concordance to assign establishments in Standard Industry Classification (SIC) industries that match uniquely to a NAICS industry in 1997.  Second, using establishment identifiers, we link establishments in operation in both 1992 and 1997.  If the five-digit SIC codes match in the two censuses, we apply the 1997 NAICS code.  The remaining establishments are classified in SIC industries that match to multiple NAICS industries.  We construct the proportion of 1997 establishments migrating from an SIC to each NAICS code.  Using these proportions as weights, the algorithm draws from the uniform distribution and randomly assigns the remaining establishments in a 1992 SIC industry to a 1997 NAICS industry.