The trucking industry was significantly depressed by the onset of COVID-19, as driver shortages and supply chain bottlenecks created unseasonal volatility across freight markets. Shipment volumes recovered quickly to pre-COVID levels within less than 4 months, but employment in trucking still has yet to recover to its pre-COVID level (see Figure 1).
While economic stimuli and increased consumer spending (specifically, relatively more spending on goods than services) resulted in elevated demand, supply has been constrained by driver shortages. Low employment numbers have led to persistently increasing freight costs, eclipsing a 75% year-on-year increase in the dry van average rate per mile in May 2021. However, recent data (also shown in Figure 1) points toward accelerated growth in jobs, both in total truck transportation employment and in long-haul truckload employment.
In June and July, 25 states decided to reduce or opt out of their federal unemployment insurance (UI) programs (see Figure 2). Many of these states anticipated that the termination of UI would lead to more workers rejoining the labor force. But some studies (as seen on CNBC and in The Economist) argued that the early end of UI did not yield the expected results.
We wanted to understand how the termination of UI programs would affect the freight market—specifically, how it would translate to spot rates. Using Uber Freight’s data, we compared states that terminated their UI programs with those that did not. Particularly, we looked at overall average dry van spot rates for loads starting in either group, as shown in Figure 3. Our research team derived 2 key conclusions from this analysis:
Uber Freight’s economic research team continues to monitor these and other key drivers of truckload supply and demand. We also continue to investigate the unique dynamics of the post-COVID world to help shippers and carriers stay informed.
Mazen Danaf leads economic research at Uber Freight. He has 10 years of practical and academic experience in transportation with a focus on econometric modeling and causal inference.