Working Papers

Rebate Schemes and the Sunk Cost Fallacy

We test the sunk cost fallacy using different rebate schemes in a museum

Using Coin Flipping to Resolve Choice Difficulty Problem

We study the usage of coin flipping to help resolve choice difficulty problem in the setting of donation.

Consumer learning in response to cyber-fraud

Using a field experiment, we study how consumers learn to respond to phishing attack

Pay Enough to Go to the Gym

We study how firm should set the optimal price for investment goods (e.g., gym membership) and leisure goods (e.g., video games) when consumers exhibit both the self-control problem and the sunk cost fallacy

Publications

In a repeated trust game with monitoring, we found trustors misattributed the strategic, compliant behavior they observed as signals of trustees’ trustworthiness. As a result, trustors misplaced their trust when they were unable to monitor their counterparts.
In Management Science, 2016.

We study the relationship between impatience (measured by delay discounting task) and telomere length, a biological marker of aging at the cellular level.
In PNAS, 2016.

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Motivation Two-lines Approaches Issue with Discrete Dependent Variable Motivation In this paper by Uri Simonsohn (2017), the author proposed a noval method to test U-Shape relationship. In the literature, the popular way of testing U-shapeness relationship between x and y is to add a quadratic term in the regression \(y=\beta_0+\beta_1 x + \beta_2 x^2 +\epsilon\) (\(\epsilon\) is an i.i.d noise). If \(\beta_1\) is statistitally significant, then the relationship betewen x and y are U-shape.

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To form accurate beliefs about the world (e.g., whether the earth is flat or a sphere, whether vaccination causes autism, etc), people must encounter diverse views and opinions which will sometimes contradict their pre-existing views. Many scholars concerned that the emergence of internet especially recent social media reduces the cost of acquiring information from a wide range of sources, facilitating consumers to self-segregate and limit themselves to the information sources that are likely to confirm their views.

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One function I miss about Stata is its tabstat. By using just one line code, it can produce very useful summary statistics such as mean, and standard error by groups by conditions. R has its own built-in summary function – summary(), too, but in most cases in my research, I found the summaries produced is barely useful. Consider the following pseudo-data: library(data.table) set.seed(10) N = 120 DT = data.table(x = rnorm(N,1), y = rnorm(N,2), category = sample(letters[1:3], N, replace = T)) DT[1:10] ## x y category ## 1: 1.

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In this paper by Benjamin et al (2017) on redefining statistical significance, they proposed to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries. That is the proposed p-value is one tenth of the conventional one!! Suppose the world changed to p=0.005. Do we need 10X more sample? As a researcher without sufficient funding, we care about how much additional sample we need suppose our hypothesis is true.

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When requesting individual level data from others (a company or a government agency), we usually need to properly anomymize the individuals to protect their privacy. The following is an example: (Data = data.frame(Name = c("John Smith", "Jenny Ford","Vivian Lee"), Secret = c("Hate dog","Afraid of ghost","A bathroom dancer"))) ## Name Secret ## 1 John Smith Hate dog ## 2 Jenny Ford Afraid of ghost ## 3 Vivian Lee A bathroom dancer One simple way is we can just drop the Name, and only keep the Secret since we are more interested in their secrets.

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Teaching Experiences

  • Tutor for Principles of Marketing, 2015 (Teaching Evaluation: 4.25.0; Department Average: 4.0/5.0)
  • Teaching Assistant for Marketing Research, 2013
  • Guest lecturer in Behavioral and Experimental Economics, 2010
  • Part-time guitar tutor, 2006-2007

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