Hyunjin Kim - Faculty & Research - Harvard Business School
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Hyunjin Kim


Doctoral Student

Hyunjin is a doctoral candidate in the Strategy unit at Harvard Business School. Her research explores how organizations can improve strategic decision-making and productivity in the digital economy.

Hyunjin earned her A.B. in Social Studies at Harvard College, where she was elected to Phi Beta Kappa and graduated with high honors. She also received a M.Sc in Environmental Change and Management at the University of Oxford and a M.Sc in Economics and Management at the London School of Economics. Prior to her graduate studies, Hyunjin co-founded and directed the Emerge Venture Lab, an early-stage venture capital fund for startups that work on solving global social and environmental issues. She has also worked at McKinsey & Company and Knewton.

 

Journal Articles
  1. Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

    Edward L. Glaeser, Hyunjin Kim and Michael Luca

    Data from digital platforms have the potential to improve our understanding of gentrification and enable new measures of how neighborhoods change in close to real time. Combining data on businesses from Yelp with data on gentrification from the Census, Federal Housing Finance Agency, and Streetscore (an algorithm using Google Streetview), we find that gentrifying neighborhoods tend to have growing numbers of local groceries, cafés, restaurants, and bars, with little evidence of crowd-out of other types of businesses. For example, the entry of a new coffee shop into a zip code in a given year is associated with a 0.5% increase in housing prices. Moreover, Yelp measures of local business activity provide leading indicators for housing price changes and help to forecast which neighborhoods are gentrifying.

    Keywords: Forecasting Models; Simulation Methods; Regional Economic Activity: Growth, Development, Environmental Issues, and Changes; Geographic Location; Local Range; Transition; Data and Data Sets; Measurement and Metrics; Economic Growth; Forecasting and Prediction;

    Citation:

    Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change." American Economic Association Papers and Proceedings 108 (May 2018): 77–82.  View Details
Working Papers
  1. Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change

    Edward L. Glaeser, Hyunjin Kim and Michael Luca

    We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e., nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a zip code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.

    Keywords: Geographic Location; Local Range; Transition; Data and Data Sets; Measurement and Metrics; Forecasting and Prediction;

    Citation:

    Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change." NBER Working Paper Series, No. 24952, August 2018.  View Details
  2. Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity

    Edward L. Glaeser, Hyunjin Kim and Michael Luca

    Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of economic activity at the local level. However, these statistics typically appear only after multiyear lags, and the public-facing versions are aggregated to the county or ZIP code level. In contrast, crowdsourced data from online platforms such as Yelp are often contemporaneous and geographically finer than official government statistics. In this paper, we present evidence that Yelp data can complement government surveys by measuring economic activity in close to real time, at a granular level, and at almost any geographic scale. Changes in the number of businesses and restaurants reviewed on Yelp can predict changes in the number of overall establishments and restaurants in County Business Patterns (CBP). An algorithm using contemporaneous and lagged Yelp data can explain 29.2% of the residual variance after accounting for lagged CBP data, in a testing sample not used to generate the algorithm. The algorithm is more accurate for denser, wealthier, and more educated ZIP codes.

    Keywords: Economy; Data and Data Sets; Local Range; Social and Collaborative Networks;

    Citation:

    Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity." Harvard Business School Working Paper, No. 18-022, September 2017. (Revised October 2017.)  View Details
Cases and Teaching Materials
  1. Patagonia

    Ramon Casadesus-Masanell, Hyunjin Kim and Forest L. Reinhardt

    Patagonia was deeply committed to the environment. This commitment, at times, conflicted with the company's goal to create the most innovative products in its industry. Patagonia's founder and executives welcomed imitation of both its environmental commitment and its culture. The question remained whether Patagonia's model would work well for a wide range of companies. In 2003, Patagonia executives were considering which products and markets would fit best into their portfolio of product lines, which included alpine, skiing, snowboarding, fishing, paddling, rock climbing, surfing, kayaking, and mountain biking. There was a tradeoff between alienating its core customers and achieving growth via entry into new product markets.

    Keywords: Business History; Environmental Sustainability; Business Model; Business Strategy; Expansion; Consumer Products Industry;

    Citation:

    Casadesus-Masanell, Ramon, Hyunjin Kim, and Forest L. Reinhardt. "Patagonia." Harvard Business School Case 711-020, August 2010. (Revised October 2010.)  View Details
  2. Coursera

    Ramon Casadesus-Masanell and Hyunjin Kim

    By providing free and open-access online courses at a large scale, Massive Open Online Course (MOOC) platforms seek to innovate the business models of the traditional higher education industry. In a little over a year, Coursera had grown at a rapid rate to emerge as a leader of the MOOCs in terms of the number of student enrollments, courses, and partners. The case examines two aspects of these developments in the industry: (1) What choices did Coursera make that enabled it to grow so quickly? (2) In what ways did Coursera's success impact the success of its competitors, Udacity and edX? Would one player naturally come to dominate the industry, and if so, what choices should Coursera make to retain its market positioning?

    Keywords: business models; strategy; competition; competitive advantage; Business Model; Online Technology; Higher Education; Competitive Advantage; Education Industry;

    Citation:

    Casadesus-Masanell, Ramon, and Hyunjin Kim. "Coursera." Harvard Business School Case 714-412, August 2013. (Revised September 2015.)  View Details
  3. Advertising Experiments at RestaurantGrades

    Weijia Dai, Hyunjin Kim and Michael Luca

    This exercise provides students with a data set consisting of results from a hypothetical experiment, and asks students to make recommendations based on the data. Through this process, the exercise teaches students to analyze, design, and interpret experiments. The context is an experiment in a hypothetical restaurant review company called RestaurantGrades (RG) whose main source of revenue comes from advertising. Like Yelp and TripAdvisor, RG advertisements are shown above the organic search results when someone searches on the page. RG is trying to understand whether its current advertising package is effective in practice. To do this, RG has run an experiment with two treatment arms and a control group of restaurants. The control group has no advertising, the first treatment arm consists of giving restaurants RG's current advertising package, and the second treatment arm is an alternative package that RG designed with a different approach to consumer targeting. Students are given the data to analyze, and asked to make a recommendation about which, if either, advertising package is effective.

    Keywords: Advertising Campaigns; Marketing; Online Advertising; Analysis; Performance Effectiveness;

    Citation:

    Dai, Weijia, Hyunjin Kim, and Michael Luca. "Advertising Experiments at RestaurantGrades." Harvard Business School Teaching Note 916-039, March 2016.  View Details
  4. Advertising Experiments at RestaurantGrades

    Weijia Dai, Hyunjin Kim and Michael Luca

    This exercise provides students with a data set consisting of results from a hypothetical experiment, and asks students to make recommendations based on the data. Through this process, the exercise teaches students to analyze, design, and interpret experiments. The context is an experiment in a hypothetical restaurant review company called RestaurantGrades (RG) whose main source of revenue comes from advertising. Like Yelp and TripAdvisor, RG advertisements are shown above the organic search results when someone searches on the page. RG is trying to understand whether its current advertising package is effective in practice. To do this, RG has run an experiment with two treatment arms and a control group of restaurants. The control group has no advertising, the first treatment arm consists of giving restaurants RG's current advertising package, and the second treatment arm is an alternative package that RG designed with a different approach to consumer targeting. Students are given the data to analyze, and asked to make a recommendation about which, if either, advertising package is effective.

    Keywords: Analysis; Online Advertising;

    Citation:

    Dai, Weijia, Hyunjin Kim, and Michael Luca. "Advertising Experiments at RestaurantGrades." Harvard Business School Exercise 916-038, March 2016. (Revised February 2018.)  View Details
  5. Advertising Experiments at RestaurantGrades

    Weijia Dai, Hyunjin Kim and Michael Luca

    This exercise provides students with a data set consisting of results from a hypothetical experiment, and asks students to make recommendations based on the data. Through this process, the exercise teaches students to analyze, design, and interpret experiments. The context is an experiment in a hypothetical restaurant review company called RestaurantGrades (RG) whose main source of revenue comes from advertising. Like Yelp and TripAdvisor, RG advertisements are shown above the organic search results when someone searches on the page. RG is trying to understand whether its current advertising package is effective in practice. To do this, RG has run an experiment with two treatment arms and a control group of restaurants. The control group has no advertising, the first treatment arm consists of giving restaurants RG's current advertising package, and the second treatment arm is an alternative package that RG designed with a different approach to consumer targeting. Students are given the data to analyze, and asked to make a recommendation about which, if either, advertising package is effective.

    Keywords: marketing; digital marketing; experimental methods; analytics; social media; web technology; Marketing; Online Advertising; Analysis; Performance Effectiveness;

    Citation:

    Dai, Weijia, Hyunjin Kim, and Michael Luca. "Advertising Experiments at RestaurantGrades." Harvard Business School Spreadsheet Supplement 916-702, March 2016.  View Details