Christopher Stanton is an Assistant Professor of Business Administration in the Entrepreneurial Management Unit. He is also affiliated with The Center for Economic Policy Research and The National Bureau of Economic Research. Before joining HBS, Professor Stanton was an assistant professor of finance at the University of Utah and an assistant professor of managerial economics and strategy at the London School of Economics.
An applied economist, Professor Stanton directs his research at how technology is changing the management of work. The same technological trends that enable the fragmentation of work and the rise of the gig economy also improve the measurement of work outcomes inside firms. Professor Stanton seeks to understand the resulting managerial and policy implications of these trends. His work has been published in The Review of Economic Studies, Management Science, and the Journal of Labor Economics, and it has been cited by media outlets including The Economist, The Atlantic, The Washington Post, and Fortune/CNN.
Professor Stanton earned a Ph.D. in business administration at the Graduate School of Business at Stanford University. He received a bachelor’s and a master’s degree in political science from Emory University.
Christopher Stanton is an Assistant Professor of Business Administration in the Entrepreneurial Management Unit. He is also affiliated with The Center for Economic Policy Research and The National Bureau of Economic Research. Before joining HBS, Professor Stanton was an assistant professor of finance at the University of Utah and an assistant professor of managerial economics and strategy at the London School of Economics.
An applied economist, Professor Stanton directs his research at how technology is changing the management of work. The same technological trends that enable the fragmentation of work and the rise of the gig economy also improve the measurement of work outcomes inside firms. Professor Stanton seeks to understand the resulting managerial and policy implications of these trends. His work has been published in The Review of Economic Studies, Management Science, and the Journal of Labor Economics, and it has been cited by media outlets including The Economist, The Atlantic, The Washington Post, and Fortune/CNN.
Professor Stanton earned a Ph.D. in business administration at the Graduate School of Business at Stanford University. He received a bachelor’s and a master’s degree in political science from Emory University.
Despite seeming to be an important requirement for hiring, the concept of a slot is absent from virtually all of economics. Macroeconomic studies of vacancies and search come closest, but the implications of slot-based hiring for individual worker outcomes has not been analyzed in a market context. A model of hiring into slots is presented in which job assignment is based on comparative advantage. Crucially, and consistent with almost all realistic hiring contexts, being hired and assigned to a job depends not only on one’s own skill but also on the skill of other applicants. The model has many implications, the most important of which are as follows: First, bumping of applicants occurs when one job seeker is slotted into a lower-paying job or pushed into unemployment by another applicant who is more skilled. Second, less able workers are more likely to be unemployed because high-ability workers are more flexible in what they can do. Third, vacancies are higher for difficult jobs because easy jobs can be filled by more workers. Fourth, some workers are overqualified for their jobs, whereas others are underqualified. Misassigned workers earn less than they would have had they found an open slot in a job that more appropriately matches their skills. Despite that, overqualified workers earn more than the typical worker in that job. These implications are borne out using four different data sets that match the data requirements to test these points and others implied by the model.
There is a tremendous amount of variation in conflict intensity both across and within civil conflicts. Some conflicts result in huge numbers of battle deaths, while others do not. Conflict intensity is also dynamic. Conflict intensity escalates, deescalates, and persists. What explains this variation? We take one of the most prominent explanations for the onset and occurrence of civil conflict—variation in economic conditions—and apply it to the intensity and dynamics of civil conflict. Using an instrumental variables strategy and a rich set of empirical models, we find that the intensity of conflict is negatively related to per capita income. We also find that economic conditions affect conflict dynamics, as poorer countries are likely to experience longer and more intense spells of fighting after the onset of conflict.
How and by how much do supervisors enhance worker productivity? Using a company-based data set on the productivity of technology-based services workers, supervisor effects are estimated and found to be large. Replacing a boss who is in the lower 10% of boss quality with one who is in the upper 10% of boss quality increases a team's total output by more than would adding one worker to a nine member team. Workers assigned to better bosses are less likely to leave the firm. A separate normalization implies that the average boss is about 1.75 times as productive as the average worker.
Lazear, Edward P., Kathryn L. Shaw, and Christopher Stanton. "The Value of Bosses."Journal of Labor Economics 33, no. 4 (October 2015): 823–861.
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Why did productivity rise during recent recessions? One possibility is that average worker quality increased. A second is that each incumbent worker produced more. The second effect is termed "making do with less." Using data from 2006 to 2010 on individual worker productivity from a large firm, these effects can be measured and separated. For this firm, most of the gain in productivity during the recession was a result of increased effort. Additionally, the increase in effort is correlated with the increase in the local unemployment rate, presumably reflecting the costs of losing a job.
Online markets for remote labor services allow workers and firms to contract with each other directly. Despite this, intermediaries—called outsourcing agencies—have emerged in these markets. This paper shows that agencies signal to employers that inexperienced workers are high quality. Workers affiliated with an agency have substantially higher job-finding probabilities and wages at the beginning of their careers compared to similar workers without an agency affiliation. This advantage declines after high-quality non-affiliated workers receive good public feedback scores. The results indicate that intermediaries have arisen endogenously to permit a more efficient allocation of workers to jobs.
This study examines the role of the Indian diaspora in the outsourcing of work to India. Our data are taken from oDesk, the world's largest online platform for outsourced contracts, where India is the largest country in terms of contract volume. We use an ethnic name procedure to identify ethnic Indian users of oDesk in other countries around the world. We find very clear evidence that diaspora-based links matter on oDesk, with ethnic Indians in other countries 32% (9 percentage points) more likely to choose a worker in India. Yet, the size of the Indian diaspora on oDesk and the timing of its effects make clear that the Indian diaspora was not a very important factor in India becoming the leading country on oDesk for fulfilling work. In fact, multiple pieces of evidence suggest that diaspora use of oDesk increases with familiarity of the platform, rather than a scenario where diaspora connections serve to navigate uncertain environments. We further show that diaspora-based contracts mainly serve to lower costs for the company contacts outsourcing the work, as the workers in India are paid about the market wage for their work. These results and other observations lead to the conclusion that diaspora connections continue to be important even as online platforms provide many of the features that diaspora networks historically provided (e.g., information about potential workers, monitoring, and reputation foundations).
What prevents the spread of information among coworkers, and which management practices facilitate workplace knowledge flows? We conducted a field experiment in a sales company, addressing these questions with three active treatments. (1) Encouraging workers to talk about their sales techniques with a randomly chosen partner during short meetings substantially lifted average sales revenue during and after the experiment. The largest gains occurred for those matched with high-performing coworkers. (2) Worker-pairs given incentives to increase joint output increased sales during the experiment but not afterward. (3) Worker-pairs given both treatments had little improvement above the meetings treatment alone. Managerial interventions providing structured opportunities for workers to initiate conversations with peers resulted in knowledge exchange; incentives based on joint output gains were neither necessary nor sufficient for knowledge transmission.
Using a firm-based dataset from a sales organization, we study employee responses to a staggered compensation shock, which reduced average take-home pay by over 7%.We find minimal changes in sales, suggesting that real world compensation contracts may include features that limit extreme effort reductions. The compensation cuts did, however, contribute to a significant increase in the turnover rate of the firm’s most productive employees. Changes in employee sentiment cannot explain the observed turnover responses, suggesting instead that productive employees’ superior outside options drove their attrition.
Only a small share of employers in high-wage locations procure services from abroad. We document heterogeneous employer adoption of an online labor market that facilitates trade in tasks with global workers. Job vacancies posted by experienced employers who have adopted the market are twice as likely to be filled, and this difference is unrelated to the set of available job applicants or their wages. Instead, hiring demand from experienced employers shifts outward for two reasons that we identify using exogenous variation in workers' wage bids. First, their value for hiring in the market increases—a form of learning-by-doing. Second, experienced employers omit the low-value employers who leave the market. Employers appear to learn their value for online hiring only by trying it out, and new employers' adoption decisions are relatively insensitive to wage rates. Larger firms have lower estimated values for the market. The results suggest that employers' willingness to fragment and outsource production at the task level, rather than the quality of the available online workforce, limits the growth of the gig economy.
Digital labor markets are rapidly expanding and connecting companies and contractors on a global basis. We review the environment in which these markets take root, the micro- and macro-level studies of their operations, their ongoing evolution and recent trends, and perspectives for undertaking research with micro-data from these labor platforms. We undertake new empirical analyses of Upwork data regarding 1) the alignment of micro- and macro-level approaches to disproportionate ethnic-connected exchanges on digital platforms, 2) gravity model analyses of global outsourcing contract flows and their determinants for digital labor markets, and 3) quantification of own- and cross-country elasticities for contract work by wage rate. Digital labor markets are an exciting frontier for global talent flows and are growing rapidly in importance.
Small business owners and others in self-employment have the option to transition to paid work. If there is initial uncertainty about entrepreneurial earnings, this option increases the expected lifetime value of self-employment relative to pay in a single year. This paper first documents that moves between paid work and self-employment are common and consistent with experimentation to learn about earnings. This pattern motivates estimating the expected returns to entrepreneurship within a dynamic lifecycle model that allows for non-random selection and gradual learning about the entrepreneurial earnings process. Using longitudinal data on men from the Panel Survey of Income Dynamics (PSID), the model accurately fits entry patterns into self-employment by age. The option value of returning to paid work is found to constitute a substantial portion of the monetary value of entrepreneurship. The model is then used to evaluate policies that change incentives for entry into self-employment.
A weather technology startup, ClimaCell considers the R＆D trade-offs and financing implications of pursuing a proposed contract with a major automobile maker, rather than continuing its focus on building a scalable, all-purpose weather prediction engine.
Krieger, Joshua Lev, Christopher Stanton, and James Barnett. "Forecasting ClimaCell." Harvard Business School Case 820-044, December 2019.
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The note introduces a framework to consider factors that influence the value of employee equity at early stage ventures. Valuing equity is complex, and it important to account for expected dilution, assess exit potential, and acknowledge the high rate of failure in early stage companies. The note uses a fictional example of a recent HBS graduate who makes a compensation comparison (combined salary and equity) between a startup and consulting firm. In order to determine approximate valuation of common equity, it illustrates steps to factor in pre-money valuations in each round, dilution based on the amount raised, and the probability of success/failure at each stage Ultimately, no path is without uncertainty, and intangible factors such as long-term career potential and personal fulfillment can be weighed against the value of the equity package while making a similar decision.
Facing threats to its growth, digital marketing and sales company Clear Link Technologies, LLC (Clearlink) decides to run a field experiment to test strategies for improving sales productivity. Designed in collaboration with a team of academic researchers, the study compares the efficacy of two interventions: incentives and management-directed peer learning.
In addition to providing an introduction to the call center industry and an overview of some of the cultural factors that make experimentation challenging in a business organization, the case also provides basic information about experimental design. Instructors may use the case to introduce students to fundamental concepts such as control, randomization, statistical power, and minimum detectable effect size. Students will consider best practices for experimental design, build skills for analyzing experimental findings, and explore strategies for implementation.
Leschly, Stig, Mark Roberge, and Christopher T. Stanton. "The Entrepreneurial Manager, Module 3: Operating the Business Model." Harvard Business School Module Note 819-109, March 2019.
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This case is about Tinder. It discusses different business models and ways of structuring the initial team. With a $6 million investment from IAC/Interactive in 2010, Dinesh Moorjani founded Hatch Labs to build mobile apps. His mission was to attract entrepreneurial talent to work on Lab projects which, after gaining traction, could be acquired by IAC or spun out as independent companies. To pitch talent to work at Hatch, Moorjani laid out a framework to compare the expected returns to founding an independent company versus joining an incubator. While Hatch provided lower levels of equity ownership in a single company, Moorjani argued that the complementary inputs provided by the Lab and the diversification from holding equity in the portfolio of Lab projects made the incubator structure attractive. After growing several projects from different teams, at the end of the case, Moorjani faces two critical choices: Should he support a B2B merchant services business or a dating app, Glow (a disguised name for Tinder). Second, should he go forward with a second Hatch Labs? Was the success of the first Hatch experiment replicable?
The case can be supplemented with a startup valuation Exercise (#818-705), and a technical/background note on “Valuing Employee Equity at Early Stage Ventures” (#819-167).
Ghosh, Shikhar, Christopher Stanton, Allison Ciechanover, and Jeff Huizinga. "Dinesh Moorjani and Hatch Labs." Harvard Business School Case 818-026, September 2017. (Revised February 2018.)
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Stanton, Christopher, and Shikhar Ghosh. "Hatch Startup Equity Valuation exercise for Instructor Use." Harvard Business School Spreadsheet Supplement 818-705, March 2018.
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Stanton, Christopher, and Shikhar Ghosh. "Collage.com: Scaling a Distributed Organization." Harvard Business School Teaching Note 818-100, January 2018. (Revised April 2018.)
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Throughout the second half of the 20th century, Polaroid first invented—and then continuously reinvented—the field of instant photography. Under the leadership of its mercurial founder Edwin Land, the company regularly released new instant cameras and films, often without any market research. Land created a culture of innovation and exploration within Polaroid that became conducive to the development of new customer value propositions. However, this proved difficult to sustain over the long run, and the business ultimately went into bankruptcy in 2001. How did Polaroid rise to a position of such preeminence, and was its downfall inevitable?
For roughly six weeks between late December 1936 and February 1937, a major strike at several critical General Motors (GM) plants in Flint, Michigan, essentially halted the corporation’s U.S. production and resulted in significant gains for the nascent United Automobile Workers of America union and the Committee for Industrial Organization, both of which had supported the strike. The Flint, Michigan, Sit-Down Strike represented a stunning victory for organized labor in a context where New Deal era legislation—most notably the National Labor Relations Act of 1935—created a labor friendly environment in the short run, with possibly adverse consequences for the performance of the U.S. automobile industry in the long run.
Kevin Borders and Joe Golden, co-founders and co-CEOs of Collage.com, must decide how to grow their custom photo-products startup in the face of fierce competition. From 2011 through 2016, the business evolved from a hobby to a startup with $22 million in revenue and 45 employees, all of whom worked remotely from home. Customer acquisition was becoming more difficult and repeat purchase rates lagged behind Shutterfly, the industry leader. New hires would help to integrate new products and grow marketing efforts, but several experienced team members wondered whether virtual collaboration could continue to work with an influx of new people.
