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Essays on Firms and Institutions

  • Author(s): Korovkin, Vasily
  • Advisor(s): Wacziarg, Romain T
  • Voigtl�nder, Nico
  • et al.
Abstract

My dissertation contributes towards our understanding of firm behavior in weakly institutionalized environments. It consists of three chapters. The first, “Detecting Auctioneer Corruption: Evidence from Russian Procurement Auctions”, develops a novel method for detecting auctioneer corruption in first-price sealed-bid auctions. I study the leakage of bid information by the auctioneer to a preferred bidder. I construct a formal test for the presence of bid-leakage corruption and apply it to a novel data set of 4.3 million procurement auctions in Russia that occurred between 2011 and 2016. With bid leakage, the preferred bidder gathers information on other bids and waits until the end of the auction to place a bid. Such behavior creates an abnormal correlation between winning and being (chronologically) the last bidder. Informed by this fact, I build several measures of corruption. I document that more than 10% of the auctions were affected by bid leakage. My results imply that the value of the contracts assigned through these auctions was $1.2 billion over the six-year study period. I build a model of bidding behavior to show that corruption exerts two effects on the expected prices of the contracts. The direct effect inflates the price of the contract. The indirect effect reduces the expected price since honest bidders are trying to undercut corrupt bidders. I find both effects in the data, with the direct effect being more pronounced.

My second chapter, “Collusion in Auctions: Evidence from the Timing of Bids”, documents collusion between firms using a unique feature of the same Russian procurement data: the timestamps of all bids. Timestamp data allows developing a new method of collusion detection based on the excessive share of simultaneous bids. My method shows that 8–23% of winner-runner-up pairs bid together, which provides a bound on the share of collusive auctions. Next, I document that simultaneous bidding is correlated with higher procurement prices and smaller bid margins in the auctions. We include a battery of controls to state that collusion leads to 8–9% increase in the final price of the contracts and makes joint bids up to 50% closer to each other. The chapter is the first to show how one can enhance methods of collusion detection by using the data on the timing of bids.

In the third and last chapter, I study the effects of armed conflict on trade transactions between firms. The chapter examines trade in the aftermath of the Russian-Ukrainian conflict (2014). The geographic concentration of fighting in a few regions allows me to study the indirect effects of conflict on trade, as opposed to the direct effects of violence or trade embargoes. I employ a highly granular transaction-level dataset for the universe of import and export transactions in Ukraine and find that firms from more ethnolinguistically Ukrainian counties experienced a deeper drop in trade with Russia relative to the firms in more Russian counties. The richness of panel data allows looking beyond explanations unrelated to ethnicities, such as increased transportation costs and bans on certain products. Instead, I focus on two ethnic-specific explanations: a rise in animosity and a de- crease in trust. In a stylized model of trade with asymmetric information, I show that one can distinguish these two mechanisms based on whether the effect is more pronounced for homogeneous or non-homogeneous goods, the latter pointing to the trust mechanism. The intuition is that trust mitigates the uncertainty behind goods’ quality. Empirically, I show that in contrast to homogeneous goods, the trade of relation-specific goods has not changed differentially across ethnic lines. Hence, I find little evidence in support of a shock to trust. I then use survey data to show that inter-ethnic animosity has indeed escalated in the aftermath of the conflict.

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