Large economies with differential information but without free disposal

We consider exchange economies with a continuum of agents and differential information about finitely many states of nature. It was proved in Einy, Moreno and Shitovitz (2001) that if we allow for free disposal in the market clearing (feasibility) constraints then an irreducible economy has a competitive (or Walrasian expectations) equilibrium, and moreover, the set of competitive equilibrium allocations coincides with the private core. However when feasibility is defined with free disposal, competitive equilibrium allocations may not be incentive compatible and contracts may not be enforceable (see e.g. Glycopantis, Muir and Yannelis (2002)). This is the main motivation for considering equilibrium solutions with exact feasibility. We first prove that the results in Einy et al. (2001) are still valid without freedisposal. Then we define an incentive compatibility property motivated by the issue of contracts’ execution and we prove that every Pareto optimal exact feasible allocation is incentive compatible, implying that contracts of competitive or core allocations are enforceable.

The failure of early-stage technology startups in Brazil: a study about the contributing factors to the early-death of startups between 2009 and 2014 in Southeastern Brazil

This research aimed to find out which are the main factors that lead technology startups to fail. The study focused on companies located in the Southeast region of Brazil that operated between 2009 and 2014. In the beginning, a review of the literature was done to have a better understanding of basic concepts of entrepreneurship as well as modern techniques for developing entrepreneurship. Furthermore, an analysis of the entrepreneurial scenario in Brazil, with a focus on the Southeast, was also done. After this phase, the qualitative study began, in which 24 specialists from startups were interviewed and asked about which factors were crucial in leading a technology startup to fail. After analyzing the results, four main factors were identified and these factors were validated through a quantitative survey. A questionnaire was then formulated based on the answers from the respondents and distributed to founders and executives of startups, which both failed and succeeded. The questionnaire was answered by 56 companies and their answers were treated with the factor analysis statistical method to check the validity of the questionnaire. Finally, the logistical regression method was used to know the extent to which the factors led to the startups’ failure. In the end, the results obtained suggest that the most significant factor that leads technology startups in southeastern Brazil to fail are problems with interpersonal relationship between partners or investors.