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 enhanced offline tracking features in 2015. Using a large dataset including 17,827 fixed-price projects and 8,563 hourly projects, we use a difference-in-differences (DID) approach to identify the treatment effect of the implementation of IT-enabled monitoring systems on employer contractor choice, employer surplus and market competition. We found that the IT-enabled monitoring system lowers the employers’ preference for high-reputable bidders, and thus reduces the reputation premiums. Meanwhile, comparing the trend of fixed-price projects, the implementation of the monitoring systems increased the number of bids by 17.4% and increased employer surplus in hourly projects by 21.5%. Our result suggests that IT-enabled monitoring systems have a significant effect on alleviating moral hazards, reducing agency costs, and facilitating market competition

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 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

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

Photoelectrochemical Sensors for the Rapid Detection of DNA Damage Induced by Some Nanoparticles

Photoelectrochemcal sensors were developed for the rapid detection of oxidative DNA damage induced by titanium dioxide and polystyrene nanoparticles. Each sensor is a multilayer film prepared on a tin oxide nanoparticle electrode using layer- by-layer self assembly and is composed of separate layer of a photoelectrochemical indicator, DNA. The organic compound and heavy metals represent genotoxic chemicals leading two major damaging mechanisms, DNA adduct formation and DNA oxidation. The DNA damage is detected by monitoring the change of photocurrent of the indicator. In one sensor configuration, a DNA intercalator, Ru(bpy)2 (dppz)2+ [bpy=2, 2′ -bipyridine, dppz=dipyrido( 3, 2-a: 2′ 3′-c) phenazine], was employed as the photoelectrochemical indicator. The damaged DNA on the sensor bound lesser Ru(bpy)2 (dppz)2+ than the intact DNA, resulting in a drop in photocurrent. In another configuration, ruthenium tris(bipyridine) was used as the indicator and was immobilized on the electrode underneath the DNA layer. After oxidative damage, the DNA bases became more accessible to photoelectrochemical oxidation than the intact DNA, producing a rise in photocurrent. Both sensors displayed substantial photocurrent change after incubation in titanium dioxide / polystyrene solution in a time – dependent manner. According to the data, damage of the DNA film was completed in 1h in titanium dioxide / polystyrene solution. In addition, the titanium dioxide induced much more sever damage than polysterene. The results were verified independently by gel electrophoresis and UV-Vis absorbance experiments. The photoelectrochemical reaction can be employed as a new and inexpensive screening tool for the rapid assessment of the genotoxicity of existing and new chemicals

A Comprehensive Analysis of Fermi Gamma-Ray Burst Data. IV. Spectral Lag and its Relation to E p Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts observed by the Fermi Gamma-ray Burst Monitor (GBM) telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S; 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. The main trend of spectral evolution (lag behavior) is estimated with ( ), where E p is the peak photon energy in the radiation spectrum, t + t 0 is the observer time relative to the beginning of pulse −t 0, and is the spectral lag of photons with energy E with respect to the energy band 8–25 keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between and k E is found, where W is the pulse width. We also study the spectral lag behavior with peak time of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with . It is found that is correlated with k E . We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately high-σ outflow

[meso-Tetra­kis(4-heptyl­oxyphen­yl)porphyrinato]nickel(II)

In the title compound, [Ni(C72H84N4O4)], the four-coordinate NiII ion in the middle of the planar 24-membered porphyrin ring is located on a crystallograpic inversion center, with Ni—N distances of 1.946 (2)–1.951 (2) Å. The 4-heptyl­oxyphenyl groups are twisted with respect to the porphyrin mean plane, the dihedral angles being 88.5 (3) and 79.1 (2)°

A comprehensive analysis of Fermi Gamma-Ray Burst Data: IV. Spectral lag and Its Relation to Ep Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts (GRBs) observed by the Fermi GBM telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S, 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. %in hard-to-soft pulses (H2S, 64/92) and H2S-dominating-tracking (21/92) pulses. The main trend of spectral evolution (lag behavior) is estimated with $\log E_p\propto k_E\log(t+t_0)$ (${\hat{\tau}} \propto k_{\hat{\tau}}\log E$), where $E_p$ is the peak photon energy in the radiation spectrum, $t+t_0$ is the observer time relative to the beginning of pulse $-t_0$, and ${\hat{\tau}}$ is the spectral lag of photons with energy $E$ with respect to the energy band $8$-$25$ keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between $k_{{\hat{\tau}}}/W$ and $k_E$ is found, where $W$ is the pulse width. We also study the spectral lag behavior with peak time $t_{\rm p_E}$ of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with $\log t_{\rm p_E}\propto k_{t_p}\log E$. It is found that $k_{t_p}$ is correlated with $k_E$. We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately-high-$\sigma$ outflow.Comment: 58 pages, 11 figures, 3 tables. ApJ in pres