Full Steam Ahead: Firms in the US Economy Adjust Inventory for Changes in Transportation Costs But Not the Reverse

Abstract

Logistics theory suggests that transportation and inventory policies are jointly considered by managers because these costs have offsetting properties. For example, when inventory is ordered less frequently, total transportation costs are likely to be lower due to economies of scale; yet inventory carrying costs are likely to be higher. This research analyzes aggregate transportation and inventory costs in the United States from 1960 to 2013, finding that, in the US economy as a whole, inventory and transportation costs are in long-term equilibrium. Furthermore, inventory seems to be adjusted for changes in transportation costs; however, transportation costs are not adjusted for changes in inventory costs.

The Effect of Truckload Driver Turnover on Truckload Freight Pricing

“…The BLS’s methodology ensures both data reliability and consistency over time (Bureau of Labor Statistics 2019a), and the BLS’s resources far exceed the resources available for academics. Third, industry‐level results are more generalizable for shippers, which aligns with Swanson et al’s (2016, p. 284) statement, “One advantage of aggregation techniques is that results and models are more generalizable, a benefit unavailable with disaggregated data.” Lastly, our theorizing postulates mechanisms that are predicted to operate on an aggregate industry‐level basis. Consequently, our variables should be measured at the industry level to match our data to our theorizing (Miller et al 2018).…”

“…In doing so, we make a practical contribution by providing empirical evidence validating statements by Smith (2018b) while also making a theoretical contribution per Makadok et al’s (2018) claim that offering new mechanisms to explain empirical effects is one way for scholars extend theory. Furthermore, our focus on articulating industry‐level mechanisms aligns with calls for logistics scholars to conduct more analyses using data at a higher level of aggregation than the firm (e.g., industry level and economy wide) to devise a more holistic understanding of important phenomena (Swanson et al 2016).…”

“…To test our hypotheses, we rely on time series modeling techniques (Enders 2015), a research design not often seen in the supply chain literature ( c.f., Miller et al 2020a; Swanson et al 2016). To do so, we collect several industry‐level, longitudinal data series from government and private‐sector sources.…”

“…First, per Grunfeld and Griliches (1960) using aggregate data should (hopefully) help cancel‐out idiosyncratic carrier‐specific factors that affect pricing (Kafarski and Caruso 2012). Swanson et al (2016, p. 284) likewise state, “Aggregate techniques are appropriate when researchers must control the heterogeneity of firms and the associated noise that accompanies statistical data.” Second, reliable industry‐level TL pricing data are available from the Bureau of Labor Statistics (BLS) through the BLS’s long‐running producer price index (PPI) program (Bureau of Labor Statistics 2019a). The BLS’s methodology ensures both data reliability and consistency over time (Bureau of Labor Statistics 2019a), and the BLS’s resources far exceed the resources available for academics.…”

Exploring Longitudinal Industry‐Level Large Truckload Driver Turnover

“…Our wage turnover prediction can also be contrasted with research on between-industry differences in turnover rates, where evidence has consistently shown that industries with higher average wages tend to have lower turnover rates (Krueger and Summers 1988). This illustrates the utility of examining logistics topics longitudinally at a macrolevel (Swanson et al 2016).…”