Pedro Franco de Campos Pinto

PhD Student

Supervisor: Kevin Sheedy

Graduate Teacher's Assistant

Economics Department

Office hour: Thursdays, 12.30-14.30 in S684

Research

Research interests: Macroeconomics, Monetary Economics, Experimental Economics.

Contamination between Experiments: playing social preference games in sequence, March 2009, Master Thesis, pre-comitee paper version (final version available here)

Abstract: Many methodological issues remain in experimental economics. This paper deals primarily with the issue of contamination between experiments, which we define as altered behaviour by participants in a experiment due to experience in a previous experiment. This is tested in the case of Social Preference games and we cannot reject the possibility of contamination having occurred. We propose three channels for this contamination, via an expectations update of players, through an wounded pride/spite model and through fatigue. We test these hypothesis by having participants play either an Ultimatum or an Dictator game or participate in a Test, followed by a novel game design, an Altered Public Goods (APG) game, and compare the results with that of players who only play the APG game. We cannot reject the hypothesis of contamination via an expectations update. We find mixed evidence regarding the possibility that contamination occurs due to players behaving in an wounded pride/spite model. We find fatigue as possible contaminant to be non-significant, although there is evidence that the level of fatigue induced in participants was low.

Unpublished, papers at an alpha stage (comments welcome)

Firm and Consumer Borrowing with Credit Spreads, 2011

Abstract: In this alpha version of the paper, we modify Curdia and Woodford (2010) credit frictions model by introducing firm borrowing, thus creating a model of credit spreads where both consumers and production firms can borrow. We achieve this by making several simplifying assumptions, principally by making firms be in perfect competition. We present the model and an incomplete set of log linearized equations. We discuss how we shall, in the future, run this model to obtain its numerical results.