International Outsourcing in Information Technology
Why this work is in the frame
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Bibliographic record
Abstract
Offshoring is prevalent among Colorado information technology companies, and IT jobs have been and will be lost to international outsourcing. However, the number of IT jobs lost from offshoring is likely to be less than the tens of thousands of jobs being predicted by Colorado's popular press. This is a principal finding of a study I recently completed on offshoring activities by Colorado IT firms. In 2003, Colorado ranked first among the 50 states in concentration of high-tech workers, and there has been considerable concern over losses of domestic high-tech jobs. Study respondents were cautiously optimistic that IT jobs lost to international outsourcing can and will be replaced-provided that Colorado high-tech workers are willing to move up the value chain and acquire new higher-value-added skills to make them more competitive in their business. The study, sponsored by the Colorado Institute of Technology (CIT), addressed the drivers of international outsourcing, how these companies' international outsourcing operations are performing, what IT jobs these companies are offshoring, and how their international outsourcing activities are impacting IT employment in Colorado. Forty executives from 34 companies were interviewed between February and November 2004. These included firms based in Colorado, as well as companies headquartered outside of Colorado and the United States with a meaningful presence in the state. The firms represent different industries, such as financial services, IT services and consulting, computer equipment and peripherals, telecommunications, software, and others. Several of Colorado's largest employers and a few entrepreneurial firms participated in the study. Drivers of International Outsourcing Of the 34 companies in the study, 22 were engaged in offshoring. Fifty-four percent of these companies' offshoring operations started prior to 2001, signifying that this is not a recent development. These companies' offshore operations were located in over a dozen countries (e.g., China, Germany, UK, South Africa) but the lion's share was in India. Figure 1 shows the key factors driving offshoring. Lower labor costs, staff augmentation, access to high-quality employees, and access to technology were identified as the most important drivers of international outsourcing. Firms that outsourced in order to take advantage of lower labor costs did so under different conditions before and after 2001. Companies that off shored work prior to 2001 did so in the context of strong employment. Labor shortages, escalating labor costs, and the need to handle bursts in IT workloads prompted these companies to offshore. In contrast, most firms that engaged in offshoring after 2001 did so as part of cost-cutting efforts under difficult business conditions. Access to technology and to high-quality employees/contractors were important drivers for several companies, particularly those that engaged in development and research. For example, one company launched a project in Canada to conduct R&D in wireless technologies. A second company has maintained an offshore facility in Japan to work on software development projects for manufacturing. Companies that conducted software development in India noted the advantages provided by their CMM (Capability Maturity Model) providers. According to these companies, their Indian providers are very good in process management and improvement and in executing projects that have well-defined specifications. Magnitude and Scope of Offshoring The majority of the companies' offshoring operations had fewer than 50 employees or contractors dedicated to the offshoring company. However, the notable exceptions were seven companies that had more than 300 employees or contractors working on offshore projects. Of these, the largest offshore outsourcers were companies in IT services, software and telecommunications. Figure 2 lists the different types of offshored work. …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it