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PhD scholars Garima & Ravikanth win Best Paper Awards

IDSSI conferred at JAGSoM School of Management recognizes duo’s efforts

9 JANUARY, 2022: PhD students Garima Chaklader and Ravikanth Vazrapu did IIMB proud by winning the Best Paper Awards in the Data Science Applications Doctoral Category at the ISDSI conference held at the JAGSoM School of Management.

While Ravikanth’s paper was titled ‘Impact of Auction Closing Mechanism on Price Discovery: Evidence from Tea Auctions in India’, Garima’s paper was about the ‘Servicification of Indian Manufacturing Industries’.

Garima’s paper studies the dependence of Indian manufacturing firms on the service sector for inputs. It estimates the association of increased use of services (or servicification) on a firm’s ability to integrate with the foreign market and export a higher share of output (or export intensity). Garima uses a firm-level dataset from Prowess and studies the impact of servicification from 2000 to 2019 using a Fixed-Effects model and pooled quasi-MLE fractional probit model.

She later relies on the GEE (Generalized Estimating Equation) method to enhance the efficiency of the fractional model. It was found that an increase in the share of services input increased export intensity at a decreasing rate. Firms with lower service intensity experience a higher rise in export intensity than firms with higher service intensity. The results are robust after endogenizing service intensity and TFP in a Dynamic Panel GMM estimation. Garima has also controlled selection bias and endogeneity of service intensity using a Probit & FE-2SLS Model and continued to find results. Further, the paper bisects services into traditional and modern services and finds that the former has a more significant impact on manufacturing exports. 

Ravikanth’s paper, discusses, among other things, the role of ending rule in auction design. Fixed deadline and extendable deadline are commonly employed auction-ending rules. In a fixed deadline auction, sniping or last-minute bidding by experienced bidders (Roth and Ockenfels 2002, Ariely et al. 2005) has been found to limit participant learning and thereby impede price discovery (Onur and Tomak 2006). To counter the sniping problem and improve price discovery, auction designers suggest using an extendable-deadline ending rule.

As opposed to fixed-deadline auctions, extendable-deadline auctions have been found to attract a larger number of bidders (Onur and Tomak 2006), have a longer bidding duration and create better opportunity for learning (Ariely et al. 2005), leading to higher prices (Glover and Raviv 2012). However, they can also increase transaction costs for the participants (Cao et al. 2019). Therefore, it is not obvious when and where fixed or extendable-deadline ending rules should be employed; this work addresses this research gap.

The paper analyses outcomes of a natural experiment conducted by the Tea Board of India, in which the auction ending rule was changed from a fixed-deadline format to an extendable-deadline format for a brief period of time. By employing cluster analysis of the bidding activity such as bidding frenzy, learning opportunities and cognitive costs, the paper attempts to understand the effect of ending rules on auction outcomes and price discovery.

Through this research, Ravikanth hopes to shed light on factors that moderate the effect of auction-ending rules on price discovery in auctions.