What Matters in Venture Capital? Evidence from Entrepreneurs’ Stated Preferences

We study how entrepreneurs evaluate the ability of different US venture capitalists (VCs) to add value to start-up companies. Analyzing a large data set of entrepreneurs’ stated preferences regarding VCs, we demonstrate that entrepreneurs view independent partnership VCs more favorably than other VC types (e.g., corporate, financial, and government sponsored VCs). Although entrepreneurs are able to correctly identify VCs with better track records, they do not believe them to be more desirable investors. We also find that an entrepreneur's rankings are affected by their overall exposure to VCs, emphasizing the role of experiential learning in the venture capital market.     

Developing Official Stochastic Population Forecasts at the US Census Bureau

The U.S. Census Bureau is approaching a critical decision regarding a major facet of its methodology for forecasting the United States population. In the past, it has relied on alternative scenarios, low, medium, and high, to reflect varying interpretations of current trends in fertility, mortality, and international migration to forecast population. This approach has limitations that have been noted in the recent literature on population forecasting. Census Bureau researchers are embarking on an attempt to incorporate probabilistic reasoning to forecast prediction intervals around point forecasts to future dates. The current literature offers a choice of approaches to this problem. We are opting to employ formal time series modeling of parameters of fertility, mortality, and international migration, with stochastic renewal processes. The endeavor is complicated by the administrative necessity to produce a large amount of racial and Hispanic origin detail in the population, as well as the ubiquitous cross-categories of age and sex. As official population forecasts must meet user demand, we are faced with the added challenge of presenting and supporting the resulting product in a way that is comprehensible to users, many of whom have little or no comprehension of the technical forecasting literature, and are accustomed to simple, deterministic answers. We may well find a need to modify our strategy, depending on the realities that may emerge from the limitations of data, the administrative requirements of the product, and the diverse needs of our user community.     

What Does it Take to Eliminate the use of a Strategy Strictly Dominated by a Mixture?

This paper reports an experiment to determine whether subjects will learn to stop using a strictly dominated strategy that can be an above average reply. It is difficult to find an experimental design that eliminates the play of the strictly dominated strategy completely. The least effective treatment used money to motivate behavior directly. The most effective treatment used a binary-lottery with money prizes to induce preferences, but even this treatment required giving subjects plenty of experience. Doing so reduced the play of the strictly dominated strategy to around 10 percent by the end of a session. There is no evidence for the explosive cycling needed to make the strictly dominated strategy an above average reply.Related research available at http://erl.tamu.edu/     

An impact assessment perspective in research planning: the case of a research institute in China

This paper address the planning process for a Chinese Tecnological Research Institute. We suggest broadening the scope of planning to encompass wideranging impacts. This orientatin facilitates product selection and research utilization.