The Moderating Effect of COVID-19 on the Relationship between Spot Market Prices and Capital Investment in the Motor-Carrier Sector

Miller, Jason W.; Darby, Jessica L.; Scott, Alex

doi:10.5325/transportationj.61.2.0151

Cited by 4 publications

(7 citation statements)

References 122 publications

(210 reference statements)

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“…(1) To test H3 that carrier age is more strongly related to job losses from exits than to job losses from downsizing, we use the decomposition of JLRT into JLRTFE and JLRTD and estimate Equations 5 and 6. Although JLRTFE and JLRTD are rates, across equation comparisons of the strength of the same predictor are best done when dependent variables have the same standard deviation (Miller, Ganster, et al, 2018;Miller, Darby, et al, 2022;Miller & Kulpa, 2022). We consequently denote these standardized measures as JLRTFE* and JLRTD*.…”

Section: Statistical Model Formulation and Resultsmentioning

confidence: 99%

Job gain and job loss dynamics in the truck transportation industry

Miller,

Phares,

Burks

2024

J of Business Logistics

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Every year, roughly 27% of all jobs in the truck transportation sector (NAICS 484) are reshuffled across motor carriers as existing carriers grow or shrink, new entrants begin operations, and existing firms exit. We take a first look at job gain and job loss dynamics in truck transportation, with a special emphasis on the roles of carrier age and job gain and loss dynamics in the manufacturing sector, the source for most trucking ton‐miles. In doing so, we draw on and extend theory in both supply chain management and economics. We test our predictions using archival administrative data covering 1995 through 2019 from the Census Bureau's Business Dynamics Statistics program that tracks the universe of truck transportation firms with employees. Results from fitting a series of mixed effects models provide strong evidence that job gain and job loss dynamics at trucking firms decline rapidly as carriers age. We further find these age‐related dynamics are moderated by employment dynamics in the manufacturing sector. Robustness testing shows job gains and losses dynamics in manufacturing are more predictive than the same dynamics in the distribution sector (wholesaling, retailing, and warehousing). We discuss implications of these findings for theory and practice.

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Section: Statistical Model Formulation and Resultsmentioning

confidence: 99%

Job gain and job loss dynamics in the truck transportation industry

Miller,

Phares,

Burks

2024

J of Business Logistics

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“…Analogously, our second outcome of Vehicle Violation Rate is operationalized as the sum of maintenance violations at the size cohort by day level divided by the number of inspections for that size cohort on that day. We normalize these measures by their respective grand means and grand standard deviations, given (i) z‐scores are more informative regarding effect magnitudes and (ii) our interest in comparing across‐equation effects necessitates that these variables be on the same scale (Miller, Darby, & Scott, 2022). Our third dependent variable is Model Year , which is the average model year of inspected trucks for a given size cohort on a given day based on the VIN number recorded during the inspection.…”

Section: Methodsmentioning

confidence: 99%

“…H6 involves an across-equation comparison in that our theory implies 𝜓 2 < 𝜑 2 . Across-equation comparisons of the same predictor are akin to two-way interactions and are increasingly common in logistics and supply chain management research, given they provide nuanced tests (Miller, Darby, & Scott, 2022;Miller, Golicic, & Fugate, 2018;Miller, Skowronski, & Saldanha, 2022).…”

Section: Statistical Model Formulationmentioning

confidence: 99%

How do trucking companies respond to announced versus unannounced safety crackdowns? The case of government inspection blitzes

Balthrop

Scott

Miller

2023

J of Business Logistics

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Ensuring motor carriers comply with safety rules is critical to the efficient workings of supply chains and the safety of the motoring public. However, little is understood regarding how carriers respond to changes in the likelihood of inspection (a.k.a., “crackdowns”) undertaken by the Department of Transportation. Drawing on the regulatory compliance and criminology literature, we extend the rational cheater explanation that undergirds carrier safety research by incorporating principles from attention‐based theory to devise new theoretical predictions regarding how carriers respond to announced versus unannounced inspection crackdowns. To test our theory, we rely on exogenous variation in the probability of inspection from the DOT's use of announced and unannounced inspection “blitzes.” We test predictions using a longitudinal dataset of nearly 10 million truck inspections from 2012 to 2016. We find firms with lower costs of compliance, and higher costs of avoiding inspections improve compliance prior to and during announced blitzes. Small firms with lower costs of avoidance tend to avoid announced blitzes. Unannounced blitzes result in no changes in compliance or avoidance, providing evidence that awareness is driving our results.

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“…One explanation is that many of the jobs lost in To test H3, we use the decomposition of JDRT into JDRTE and JDRTD and estimate Equations 5 and 6. Though JDRTE and JDRTD are rates, across equation comparisons of the strength of the same predictor are best done when dependent variables have been transformed to have the same standard deviation (Miller et al , 2022a(Miller et al , 2022b. We consequently denote these standardized measures as JDRTE* and JDRTD*.…”

Section: Model Free Evidencementioning

confidence: 99%