Demand Fluctuations in the Ready-Mix Concrete Industry

Abstract

I investigate the role of demand shocks in the ready-mix concrete industry. Using Census data on more than 15,000 plants, I estimate a model of investment and entry in oligopolistic markets. These estimates are used to simulate the effect of eliminating short-term local demand changes. A policy of smoothing the volatility of demand has a market expansion effect: The model predicts a 39% increase in the number of plants in the industry. Since bigger markets have both more plants and larger plants, a demandsmoothing fiscal policy would increase the share of large plants by 20%. Finally, the policy of smoothing demand reduces entry and exit by 25%, but has no effect on the rate at which firms change their size.

To follow the market or the parent system: Evidence from health IT adoption by hospital chains

“…I include the following two market observables: total elderly population and the HHI. I control for market unobservables using market‐level group dummies, similar to the method used by Collard‐Wexler (2013), Lin (2015), and Wang (2021). I construct these group dummies in the following steps.…”

Search Frictions and Efficiency in Decentralized Transport Markets

“…Finally, we relate to the literature on industry dynamics (Hopenhayn, 1992, Ericson andPakes, 1995), while our empirical methodology borrows from the literature on the estimation of dynamic setups (e.g. Rust, 1987, Bajari et al, 2007, Pakes et al, 2007applications include Ryan, 2012, Collard-Wexler, 2013, Kalouptsidi, 2014. Related to this paper, a small literature lying in the intersection of search and industry dynamics, has explored trading frictions in decentralized markets (e.g.…”

The Relation Between Accounting Comparability and Firm Productivity

“…I include industry fixed effects because prior literature shows that industry-specific characteristics such as the size of sunk costs (Collard-Wexler, 2013), competition (Bloom et al, 2011; Schmitz, 2005; Syverson, 2004a); product market rivalry (Bloom et al, 2013), and regulation (Arnold et al, 2008; Bridgman et al, 2009; Fabrizio et al, 2007; Greenston et al, 2012; Knittel, 2002; Pavcnik, 2002) influence productivity. Furthermore, to control for changes in productivity due to developments in information technology over my sample period, I include year fixed effects.…”