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Over the past 200 years, rising rates of information proliferation have created new environments for information competition and, consequently, new selective forces on information evolution. These forces influence the information diet available to consumers, who in turn choose what to consume, creating a feedback process similar to that seen in many ecosystems. As a first step towards understanding this relationship, we apply animal foraging models of diet choice to describe the evolution of long and short form media in response to human preferences for maximising utility rate. The model describes an increase in information rate (i.e., entropy) in response to information proliferation, as well as differences in entropy between short-form and long-form media (such as social media and books, respectively). We find evidence for a steady increase in word entropy in diverse media categories since 1900, as well as an accelerated entropy increase in short-form media. Overall the evidence suggests an increasingly competitive battle for our attention that is having a lasting influence on the evolution of language and communication systems.
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Since the 1980s, technology business incubators (TBIs), which focus on accelerating businesses through resource sharing, knowledge agglomeration, and technology innovation, have become a booming industry. As such, research on TBIs has gained international attention, most notably in the United States, Europe, Japan, and China. The present study proposes an entrepreneurial ecosystem framework with four key components, i.e., people, technology, capital, and infrastructure, to investigate which factors have an impact on the performance of TBIs. We also empirically examine this framework based on unique, three-year panel survey data from 857 national TBIs across China. We implemented factor analysis and panel regression models on dozens of variables from 857 national TBIs between 2015 and 2017 in all major cities in China and found that a number of factors associated with people, technology, capital, and infrastructure components have various statistically significant impacts on the performance of TBIs at either national model or regional models.
This paper considers how to elicit information from sensitive survey questions. First we thoroughly evaluate list experiments (LE), a leading method in the experimental literature on sensitive questions. Our empirical results demonstrate that the assumptions required to identify sensitive information in LE are violated for the majority of surveys. Next we propose a novel survey method, called Multiple Response Technique (MRT), for eliciting information from sensitive questions. We require all of the respondents to answer three questions related to the sensitive information. This technique recovers sensitive information at a disaggregated level while still allowing arbitrary misreporting in survey responses. An application of the MRT provides novel empirical evidence on sexual orientation and Lesbian, Gay, Bisexual, and Transgender (LGBT)-related sentiment.
Online traces of human activity offer novel opportunities to study the dynamics of complex knowledge exchange networks, and in particular how the relationship between demand and supply of information is mediated by competition for our limited individual attention. The emergent patterns of collective attention determine what new information is generated and consumed. Can we measure the relationship between demand and supply for new information about a topic? Here we propose a normalization method to compare attention bursts statistics across topics that have an heterogeneous distribution of attention. Through analysis of a massive dataset on traffic to Wikipedia, we find that the production of new knowledge is associated to significant shifts of collective attention, which we take as a proxy for its demand. What we observe is consistent with a scenario in which the allocation of attention toward a topic stimulates the demand for information about it, and in turn the supply of further novel information. Our attempt to quantify demand and supply of information, and our finding about their temporal ordering, may lead to the development of the fundamental laws of the attention economy, and a better understanding of the social exchange of knowledge in online and offline information networks.
The article presents the results of multivariate classification of Russian regions by the indicators characterizing the population income and their concentration. The clusterization was performed upon an author approach to selecting the characteristics which determines the academic novelty in the evaluation of regional differentiation by population income and the interconnected characteristics. The performed analysis was aimed at the evaluation of the real scale of disproportions in spatial development of the country territories by the considered characteristics. The clusterization results allowed to formulate the condition of a relatively strong position of a group of high-income regions (the changes in the array of regions constituting it is highly unlikely in the foreseeable future). Additionally there has been revealed a group of Russian regions that the population is struggling to live on quite low income. These so-called poor regions, within the crisis conditions caused by Covid-19 are in need of additional public support, without which their population will impoverish.
We examine how the institutional context affects the relationship between gender and opportunity entrepreneurship. To do this, we develop a multi-level model that connects feminist theory at the micro-level to institutional theory at the macro-level. It is hypothesized that the gender gap in opportunity entrepreneurship is more pronounced in low-quality institutional contexts and less pronounced in high-quality institutional contexts. Using data from the Global Entrepreneurship Monitor (GEM) and regulation data from the economic freedom of the world index (EFW), we test our predictions and find evidence in support of our model. Our findings suggest that, while there is a gender gap in entrepreneurship, these disparities are reduced as the quality of the institutional context improves.
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