Xiaodong Liu, John H. Kagel and Lung-fei Lee, "Learning from Peers in Signaling Game Experiments", Journal of Applied Econometrics, forthcoming.

The data are in two files, both are provided in two formats, Excel (.xlsx) and plain text (.txt). "high_cost_entrants" contains the data for experiments with high-cost entrants. "low_cost_entrants" contains the data for experiments with low-cost entrants.

In "high_cost_entrants", there are 4360 observations. In "low_cost_entrants", there are 9136 observations. Both files have 13 variables (columns). The variables are:

1. Experimental session number.
2. Experimental cycle number.
3. Experimental period number (within a cycle).
4. Team ID. In this paper, we focus on individual decision-making instead of team decision-making, so a team has only one Player A (Monopolist) and one Player B (Entrant).
5. Player A (Monopolist) ID.
6. Player B (Entrant) ID.
7. Player A's type, which is 1 if A is a high-cost type and 2 if A is a low-cost type.
8. Player B's choice, which is 1 if B chooses OUT and 2 if B chooses IN.
9. Player A's output (or price) choice.
10. Player A's payoff in the current period.
11. Player B's payoff in the current period.
12. Player A's gender, which is 0 if male, 1 if female, and 2 if unknown.
13. A indicator variable for experimental treatment given by 100*exper+10*ctx+pitts, where "exper" is 1 if the session recruits experienced subjects and 0 otherwise, "ctx" is 1 if the session uses natural language (meaningful context) for instructions and 0 otherwise, and "pitts" is 3 if the session was conducted at the University of Pittsburgh and 0 if it was conducted at the Ohio State University.