Section #: 01

Students must abide by UVic academic regulations and observe standards of scholarly integrity (i.e. no plagiarism or cheating). Therefore, this assignment must be taken individually and not with a friend, classmate, or group. You are also prohibited from sharing any information about the assignment with others. I affirm that I will not give or receive any aid on this assignment and that all work will be my own. Shiying Hu

A big part of environmental economics is estimating peoples willingness to pay for an improvement in environmental quality and/or their willingness to accept for a degradation in environmental quality. Theoretically these two measures should be very close for small changes for the good in question. Nevertheless there are countless studies that show that these two measures can be significantly different even for inconsequential i.e. small value goods… like chocolate bars. One possibility explanation for this divergence is that humans suffer from what is known an endowment effect. I emphasized suffer because this is an irrational behaviour. The endowment effect, in a nutshell, is that you demand more to give up something than what you are willing to pay for it in the first place. It is as if possessing a good makes it more valuable to you. In the context of environmental economics, people demand more compensation for a slight decrease in environmental quality than what they are willing to pay for a slight improvement. It is as if possessing the current level of environmental quality makes it more valuable that what your were willing to pay to get that level of environmental quality in the first place. It is an open question whether or not the endowment effect is an actual feature of human behaviour or is:

Recall that in experiment 3 there were 3 separate parts:

  1. A second price sealed bid auction where you bid on a chocolate bar.
  2. You were asked with what probability you think you won the auction in part 1.
  3. Finally, you stated the minimum that you would be willing to sell a chocolate bar back to me (if you in fact won the auction in part 1).

The treatments were designed to manipulate both alternative explanations of the endowment effect.

Note that this is a very demanding test of the endowment effect: The probability of you becoming endowed (winning the auction) is low and unknown, so very little opportunity to become attached to the chocolate bar.

1 (0 marks)

If you participated in the experiment, tell me which treatment you were in and describe how you decided to what to bid and ask.

2 (10 marks)

In words, why is it in your best interest to bid your true value in a second price sealed bid auction, regardless of the number of bidders?

In second price auction, your bid will only affect your chances of winning auctioned, not your price, because you pay the second highest price. If the bid is low, it will only affect your auctioned odds of winning instead of the actual price paid.

3 (10 marks)

In words, why is it in your best interest to ask your forgone value in a second price procurement auction, regardless of the number of askers?

In second price auction, the price you give may affect whether you can sell the goods, and will not affect your actual income. The most likely to sell goods.

4 (5 marks)

In your .R file use the assignment operator <- and the pipe operator %>% overwrite mydf using mydf as the input to the following functions: i.e. start with mydf <- mydf %>%, THEN do the following: group_by() variable oneid, THEN create new variables using the mutate() function: mean_bid=mean(bid), mean_ask=mean(ask), sd_bid=sd(bid), sd_ask=sd(ask), ask_minus_bid=ask-bid, mean_ask_minus_bid=mean(ask_minus_bid). Put a copy of your code into the chunk below, noting eval=FALSE, which means that the code is not evaluated (run).

mydf <-mydf%>%
  mutate(mean_bid=mean(bid), mean_ask=mean(ask), sd_bid=sd(bid), sd_ask=sd(ask), ask_minus_bid=ask-bid, mean_ask_minus_bid=mean(ask_minus_bid))

5 (5 marks)

In your .R file use functions ggplot() with argument data=mydf and aes() with arguments x=bid, y=ask and col=treatment to create first_plot. To this blank plot add (using the + operator) geom_abline() with arguments slope=1,intercept=0,col="white",lwd=2 and geom_jitter() with arguments alpha=.5,width=1,height=1. Give the plot a descriptive title using function labs() with argument title="a short description of what I think the plot shows".