Finally, while we use the terminology “Indian” for our ethnic assignment, it is worth noting that the procedure more broadly captures South Asian ethnic origin. Names originating from India, Pakistan, Bangladesh, etc. overlap too much to allow strict parsing.<sup>8</sup> We assign ethnicities to company contacts undertaking hiring on oDesk, with a match rate of 88%.<sup>9</sup> The company contact is the individual within each firm that hires and pays for the service. In most all cases, this company contact is the decision maker for a hire. This is good for our study in that we want to evaluate the role of ethnic connections in outsourcing decisions, and this structure illuminates for us the person within the larger firm making the hiring choice.<sup>10</sup> It is important to note that during our sample period job postings only list the company location, not the company contact’s name. We know the contact’s identity through oDesk’s administrative records, but potential job seekers do not observe the names of individuals. This asymmetry removes the potential sorting of job applicants across contract opportunities in terms of company contact ethnicity (e.g., workers in India bidding more frequently for postings from ethnic Indians in the United States). Conditional on the year, job type, and country of the company contact, there are no statistical differences in the rate at which workers in India apply for the jobs posted by ethnic Indians versus other ethnic groups.<sup>11</sup> 8 We do not believe this name overlap has material consequences. The imprecision will lead to our descriptive estimates being slightly off in terms of their levels, but not by much given that India has by far the largest South Asian diaspora. For regressions, measurement error would typically result in the estimates of network effects being downward biased, but even here this is not clear to the extent that other South Asians more likely to work with India. Significantly, the estimations in Table 10 use specific data on diaspora and paint a similar message, including showing how a higher Indian diaspora strongly correlates with our ethnic Indian measure on oDesk.

9 This match rate rises somewhat when removing records that are either missing names or have non-name entries in the name field (e.g., either the company is listed in the name field or a bogus name like “test”). The four most common surnames linked with the Indian ethnicity are Kumar, Singh, Ahmed, and Sharma.

10 A related limitation, however, is that the oDesk data do not easily link company contacts into larger firms. This structure limits our ability to describe the firm size distribution on oDesk, but for most applications this has limited consequence. For researchers, this structure is operationally quite similar to patent assignee codes/names.

11 Our data do not indicate whether side arrangments form between companies and workers. We suspect, but cannot verify, that the number of cases where an employer asks a pre-arranged contact to enlist on oDesk in order to employ them is low due to the fees that oDesk charges. It is more likely that successful employment relationships move offline and into side arrangements to circumvent oDesk fees. This would potentially impact our analysis to the extent that the likelihood of moving offline was greater for diaspora-based connections. We have not seen evidence to suspect that side arrangements have an ethnic bias to them.

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