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This case is designed to teach how to structure information technology (IT) infrastructure outsourcing deals from both the outsourcer and the client perspective. Office Supply Incorporated (OSI) is a company in crisis, with challenges in its cost structure and poor IT performance. Outsourcing to Technology Infrastructure Solutions is an opportunity to both reduce costs and complexity for the firm, but students first must consider whether outsourcing is a good strategic fit for OSI. Detailed spreadsheet templates are given that are based on a real outsourcing client engagement for a major infrastructure outsourcing company. The spreadsheets are complex but have been simplified so that they automatically calculate when populated, allowing the students to quickly move to answering the management challenge: how should TIS price and structure the outsourcing deal? Answering this question provides deep insights into the business case for IT outsourcing and how outsourcers financially engineer a deal structure to ensure a win-win outcome for both the client and outsource service provider.

Students will: Understand the strategic context of IT outsourcing and when it will benefit a firm; Understand IT infrastructure outsourcing and management issues such as personnel reductions and organizational change; Learn which outsourcing pricing model is the best fit for a project; Create a rigorous cost-benefit financial analysis and ROI model for IT infrastructure outsourcing; Analyze the model and learn how to financially engineer the deal to be a win-win for the outsourcer and client.

The purpose of this paper is to estimate the impact of the COVID-19 pandemic on the patterns of convergence/divergence among the districts in India. Specifically, this paper investigates if the impact is heterogeneous among different cohorts of districts (based on income distribution). The differential impact may lead to heterogeneous long-run growth paths, resulting in unbalanced development across regions within the country. A study of convergence can ascertain the possible trajectory of such development across regions. Investigation of this phenomenon is the primary aim of this study.

This paper uses the panel regression method for estimation. This paper uses high-frequency nighttime light intensity data as a proxy for aggregate output.

The authors observe a significant reduction in the convergence rate as a result of the pandemic. Across the cluster of districts, the drop in ß-convergence rate, compared to the pre-pandemic period, varied from approximately 33% for the poorer districts to close to zero for the richest group of districts. These findings suggest that the pandemic may lead to a wider disparity among different regions within the country.

This paper contributes to the literature in the following ways. First, to the best of the authors’ knowledge, this is the first paper to investigate the impact of COVID-19 on the convergence rate. A detailed look into the possible disparity in convergence among various regions is critical because a larger drop in convergence, especially among the poorer regions, may call for policy attention to attain long-term equitable development. The authors perform this exercise by dividing the districts into four quantile groups based on the distribution of night-light intensity. Second, while previous studies on convergence using nighttime light data have used a cross-sectional approach, this study is possibly the first attempt to use the panel regression method on this data. The application of this method can be useful in tackling district-level omitted variables bias. Finally, the heterogeneity analysis using different quantiles of the distribution of night-light intensity may help in designing targeted policies to mitigate the disparity across districts due to the shock.