We show how to numerically solve for a Markov-perfect equilibrium of a dynamic auction game where a procurer repeatedly purchases construction services from capacity-constrained firms. We find that the procurer is best off scheduling frequent auctions for small project sizes. Otherwise, firm capacity utilization rates become larger and more asymmetric, which softens competition and increases procurement costs. We also find that forward-looking bidding dampens the competitionsoftening effects of asymmetry: farsighted firms compete more intensely than myopic ones. This can undermine the goal of a bid-preference-style affirmative action program: more farsighted firms respond less to the asymmetry induced via bid preferences. * Oberlin College; viplav.saini@oberlin.edu. I thank editor Phil Haile and two anonymous referees for very careful comments that have greatly benefited the article. I also wish to acknowledge helpful comments from capacity utilization experienced a first-order stochastic dominance shift in their cost distributions compared to less constrained firms. 2 Similarly, De Silva, Dunne, and Kosmopoulou (2002, 2003) find that bids increased with bidder backlogs in procurement auctions in Oklahoma. The idea underlying this effect is that a firm's capacity in terms of workforce, equipment, and managerial resources tends to stay fixed in the short term. As a result, when a firm's work commitments increase beyond a point, it must augment its capacity by paying overtime wages, hiring additional workers, renting additional equipment, or moving specialized equipment from site to site, all of which typically lead to higher marginal costs for later projects.As a firm's costs increase with capacity utilization, firms that have asymmetric capacity utilization tend to have asymmetric costs for the same project. Such cost asymmetry can arise endogenously when auctions are sequential: even if the firms have symmetric capacity utilization prior to a given auction, the current winner will be more capacity constrained in future auctions relative to the current losers. 3 As a result, the firms face an intertemporal trade-off: higher profits in the current auction (from winning) lead to lower profits in future auctions (due to higher capacity utilization). The forgone future profits represent an opportunity cost of winning the current auction, and must be incurred by the current winner in addition to the usual monetary costs (wages, equipment rentals, and so on) of completing a project.Several questions are of interest here. What is the strategic response of forward-looking bidders to this intertemporal trade-off? What schedule of auctions minimizes the procurer's costs? Should the procurer have frequent auctions for small projects, or less frequent auctions but for larger projects? Suppose the procurer wishes to implement an affirmative action policy to encourage participation by certain disadvantaged bidders in the auction. 4 What are the implications of farsighted strategic bidder behavior for the effectiveness of th...

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We utilize laboratory experiments to study behavior in sequential procurement auctions where winning an auction round increases a bidder's future costs. The game admits competitive as well as bid-rotation style collusive equilibria. We find that (a) bidders show some propensity to account for the opportunity cost of winning an auction, but underestimate its magnitude; (b) revealing all bids (instead of only the winning bid) after each round leads to dramatically higher procurement costs. The rise in procurement costs is accompanied by an increase in very high (extreme) bids, a fraction of which appear to be collusive in nature. (JEL C91, D44, L44)

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The market for modern Indian art is an emerging art market, having come into a proper existence only in the late 1990s. This market saw tremendous growth in its initial years and then a downturn that started around [2007][2008]. Using data from auctions conducted by a major Indian art auctioneer, we estimate via hedonic regression a price index for paintings and drawings by Indian artists sold during 2000-2013. We are able to thus estimate a rate of return on Indian art as an investment and also shed light on what drives the price of a painting in the Indian market. In doing so, we document quantitatively the extent of the rise and fall in Indian art prices. We also distinguish empirically two segments in the Indian art market, namely modern painters and contemporary painters, who appear to command different prices at auction. We find a positive and statistically significant relationship between the state of the Indian stock market and art auction prices. Finally, we use our econometric results to construct a ranking of Indian painters in terms of the market prices for their work. JEL Classification: C20, Z11.