Home Bias in Hiring: Evidence from an Online Labor Market

We study the nature of home bias in online employment, wherein the employers prefer workers from their own home countries. Using a unique large-scale dataset from a major online labor market containing employers’ consideration set of workers and their ultimate selection of workers, we first estimate employers’ home bias in their online employment decisions. Moreover, we find that employers from countries with high traditional values, lower diversity, and smaller user base (or population size), tend to have a stronger home bias. Further, we disentangle two types of home bias, i.e., statistical and taste-based, using a quasi-natural experiment wherein the platform introduces a monitoring system to facilitate employers to easily observe workers’ progress in time-based projects. After matching comparable fixed-price projects as a control group using coarsened exact matching, our difference-in-difference estimations show that the home bias in online employment is primarily driven by statistical discrimination

Moral Hazards and Effects of IT-enabled Monitoring Systems in Online Labor Markets

This paper investigates how IT-enabled monitoring systems mitigate moral hazard in an online labor market and their effect on market competition. We exploit a quasi-experiment at Freelancer when it introduced an IT-enabled monitoring system in 2015. We use a difference-in-differences (DID) approach to identify the treatment effect of the monitoring system on employer contractor choice, market competition, and employer surplus. We found that the IT-enabled monitoring system lowers the employers’ willingness to pay the reputation premiums. Meanwhile, comparing the trend of the control group, the IT-enabled monitoring system raised the employer surplus in hourly projects and increased the number of bids. Our result suggests that IT-enabled monitoring systems have a significant effect on alleviating moral hazards, reducing agency costs, and facilitating market competition

Home Bias in Online Employment

We study the nature of home bias in online employment, wherein the employer prefers workers from his/her own home country. Using a unique large-scale dataset from one of the major online labor platforms, we identify employers’ home bias in their online employment decisions. Moreover, we investigate the cause of employers’ home bias using a quasi-natural experiment wherein the platform introduces a monitoring system to facilitate employers to keep track of workers’ progress in time-based projects. After matching comparable fixed-price projects as a control group using propensity score matching, our difference-in-difference estimations show that the home bias does exist in online employment, and at least 54.0% of home bias is driven by statistical discrimination

Effect of Auction Design on Bidder Entry: Evidence from An Online Labor Market

We propose that auction duration and auction description are two important auction design parameters that could serve as screening mechanisms for quality in online auctions. Using data from an online labor matching platform that connects buyers with IT service vendors, we examine the effects of auction duration and auction descriptions on auction outcomes (i.e., number of bids, bidder quality, bidding price) and project outcomes (i.e., project being contracted and being completed). Our empirical analyses show that, in buyer-determined reverse auctions of online labor matching, auctions with a longer duration and a longer description attract more bids, but they also attract more low quality bidders with less experience and lower completion rate, and hence result in a lower probability of successful contracting and completion of software service projects. Our research provides empirical evidence highlighting the strategic roles of auction design parameters like auction duration and descriptions as a potential screening mechanism for online labor matching platforms

Enriching the endophytic bacterial microbiota of Ginkgo roots

Bacterial endophytes of Ginkgo roots take part in the secondary metabolic processes of the fossil tree and contribute to plant growth, nutrient uptake, and systemic resistance. However, the diversity of bacterial endophytes in Ginkgo roots is highly underestimated due to the lack of successful isolates and enrichment collections. The resulting culture collection contains 455 unique bacterial isolates representing 8 classes, 20 orders, 42 families, and 67 genera from five phyla: Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Deinococcus-Thermus, using simply modified media (a mixed medium without any additional carbon sources [MM)] and two other mixed media with separately added starch [GM] and supplemented glucose [MSM]). A series of plant growth-promoting endophytes had multiple representatives within the culture collection. Moreover, we investigated the impact of refilling carbon sources on enrichment outcomes. Approximately 77% of the natural community of root-associated endophytes were predicted to have successfully cultivated the possibility based on a comparison of the 16S rRNA gene sequences between the enrichment collections and the Ginkgo root endophyte community. The rare or recalcitrant taxa in the root endosphere were mainly associated with Actinobacteria, Alphaproteobacteria, Blastocatellia, and Ktedonobacteria. By contrast, more operational taxonomic units (OTUs) (0.6% in the root endosphere) became significantly enriched in MM than in GM and MSM. We further found that the bacterial taxa of the root endosphere had strong metabolisms with the representative of aerobic chemoheterotrophy, while the functions of the enrichment collections were represented by the sulfur metabolism. In addition, the co-occurrence network analysis suggested that the substrate supplement could significantly impact bacterial interactions within the enrichment collections. Our results support the fact that it is better to use the enrichment to assess the cultivable potential and the interspecies interaction as well as to increase the detection/isolation of certain bacterial taxa. Taken together, this study will deepen our knowledge of the indoor endophytic culture and provide important insights into the substrate-driven enrichment

Gender wage gap in online gig economy and gender differences in job preferences

We explore whether there is a gender wage gap in the gig economy and examine to what degree gender differences in job application strategy could account for the gap. With a large-scale dataset from a leading online labor market, we show that females only earn around 81.4% of the hourly wage of their male counterparts. We further investigate three main aspects of job application strategy, namely bid timing, job selection, and avoidance of monitoring. After matching males with females using the propensity score matching method, we find that females tend to bid later and prefer jobs with a lower budget. In particular, the observed gender difference in bid timing can explain 7.6% of the difference in hourly wage, which could account for 41% of the gender wage gap (i.e. 18.6%) observed by us. Moreover, taking advantage of a natural experiment wherein the platform rolled out the monitoring system, we find that females are less willing to bid for monitored jobs than males. To further quantify the economic value of the gender difference in avoidance of monitoring, we run a field experiment on Amazon Mechanical Turk (AMT), which suggests that females tend to have a higher willingness to pay (WTP) for the avoidance of monitoring. The gender difference in WTP for the avoidance of monitoring can explain 8.1% of the difference in hourly wage, namely, 44% of the observed gender wage gap. Overall, our study reveals the important role of job application strategies in the persistent gender wage gap.First author draf