Matching in Two-Sided Platforms for IT Services: Evidence from Online Labor Markets

Abstract

Online labor markets, two-sided platforms that match buyers with freelancers for IT services, have become increasingly important for sourcing labor and creating jobs around the globe. However, matching buyers and freelancers is challenging, largely because of the difficulty in pricing idiosyncratic IT services, and buyers and freelancers face uncertainty over the price (termed value uncertainty) they should pay, or bid, for an IT service, respectively. We propose “bid price dispersion” as a key determinant of matching (the percentage of posted IT services that are actually contracted between buyers and freelancers), and we empirically examine the effect of bid price dispersion on the two key sequential stages of matching in online labor markets: (a) buyer indecision — whether a buyer offers a contract to any freelancer; and (b) freelancer regret — whether the freelancer accepts the contract offered by the buyer. Using panel data from a leading online labor market (Freelancer), our results show that bid price dispersion is associated with an increase in both buyer indecision and freelancer regret, thus hurting matching. The results are robust across several alternative model specifications and various measurements of bid price dispersion. We contribute to the literature on two-sided platforms by theorizing and empirically quantifying the negative effect of bid price dispersion on buyer-freelancer matching in online labor markets for IT services. We discuss the study’s practical implications for enhancing the design of online labor markets and the matching capability of two-sided platforms.

Keywords: Two-Sided Platforms, Bid Price Dispersion, Buyer Indecision, Freelancer Regret, Online Labor Markets, Value Uncertainty