Microfinance and entrepreneurship:An introduction

As microfinance gains increasing attention and application as a financing mechanism for entrepreneurs at the base of the economic pyramid, this Special Issue of International Small Business Journal aims to enhance scholarly understanding of how microfinance fosters entrepreneurial activity. Microfinance brings a range of financial services, including microcredit loans, savings, and insurance, within the reach of millions of poor households not served by traditional banks. This introduction summarizes the articles in this Special Issue of International Small Business Journal on ‘Microfinance’, which address a range of topics in this important domain of research and practice

Cooperation, collective action, and the archeology of large-scale societies

Archeologists investigating the emergence of large-scale societies in the past have renewed interest in examining the dynamics of cooperation as a means of understanding societal change and organizational variability within human groups over time. Unlike earlier approaches to these issues, which used models designated voluntaristic or managerial, contemporary research articulates more explicitly with frameworks for cooperation and collective action used in other fields, thereby facilitating empirical testing through better definition of the costs, benefits, and social mechanisms associated with success or failure in coordinated group action. Current scholarship is nevertheless bifurcated along lines of epistemology and scale, which is understandable but problematic for forging a broader, more transdisciplinary field of cooperation studies. Here, we point to some areas of potential overlap by reviewing archeological research that places the dynamics of social cooperation and competition in the foreground of the emergence of large-scale societies, which we define as those having larger populations, greater concentrations of political power, and higher degrees of social inequality. We focus on key issues involving the communal-resource management of subsistence and other economic goods, as well as the revenue flows that undergird political institutions. Drawing on archeological cases from across the globe, with greater detail from our area of expertise in Mesoamerica, we offer suggestions for strengthening analytical methods and generating more transdisciplinary research programs that address human societies across scalar and temporal spectra

Do smoke-free laws affect revenues in pubs and restaurants?

In the debate about laws regulating smoking in restaurants and pubs, there has been some controversy as to whether smoke-free laws would reduce revenues in the hospitality industry. Norway presents an interesting case for three reasons. First, it was among the first countries to implement smoke-free laws, so it is possible to assess the long-term effects. Second, it has a cold climate so if there is a negative effect on revenue one would expect to find it in Norway. Third, the data from Norway are detailed enough to distinguish between revenue from pubs and restaurants. Autoregressive integrated moving average (ARIMA) intervention analysis of bi-monthly observations of revenues in restaurants and pubs show that the law did not have a statistically significant long-term effect on revenue in restaurants or on restaurant revenue as a share of personal consumption. Similar analysis for pubs shows that there was no significant long-run effect on pub revenue

An Information Theory Approach to Hypothesis Testing in Criminological Research

Background: This research demonstrates how the Akaike information criterion (AIC) can be an alternative to null hypothesis significance testing in selecting best fitting models. It presents an example to illustrate how AIC can be used in this way. Methods: Using data from Milwaukee, Wisconsin, we test models of place-based predictor variables on street robbery and commercial robbery. We build models to balance explanatory power and parsimony. Measures include the presence of different kinds of businesses, together with selected age groups and social disadvantage. Results: Models including place-based measures of land use emerged as the best models among the set of tested models. These were superior to models that included measures of age and socioeconomic status. The best models for commercial and street robbery include three measures of ordinary businesses, liquor stores, and spatial lag. Conclusions: Models based on information theory offer a useful alternative to significance testing when a strong theoretical framework guides the selection of model sets. Theoretically relevant ‘ordinary businesses’ have a greater influence on robbery than socioeconomic variables and most measures of discretionary businesses

Analysis of the Lung Microbiome in the “Healthy” Smoker and in COPD

Although culture-independent techniques have shown that the lungs are not sterile, little is known about the lung microbiome in chronic obstructive pulmonary disease (COPD). We used pyrosequencing of 16S amplicons to analyze the lung microbiome in two ways: first, using bronchoalveolar lavage (BAL) to sample the distal bronchi and air-spaces; and second, by examining multiple discrete tissue sites in the lungs of six subjects removed at the time of transplantation. We performed BAL on three never-smokers (NS) with normal spirometry, seven smokers with normal spirometry (“heathy smokers”, HS), and four subjects with COPD (CS). Bacterial 16 s sequences were found in all subjects, without significant quantitative differences between groups. Both taxonomy-based and taxonomy-independent approaches disclosed heterogeneity in the bacterial communities between HS subjects that was similar to that seen in healthy NS and two mild COPD patients. The moderate and severe COPD patients had very limited community diversity, which was also noted in 28% of the healthy subjects. Both approaches revealed extensive membership overlap between the bacterial communities of the three study groups. No genera were common within a group but unique across groups. Our data suggests the existence of a core pulmonary bacterial microbiome that includes Pseudomonas, Streptococcus, Prevotella, Fusobacterium, Haemophilus, Veillonella, and Porphyromonas. Most strikingly, there were significant micro-anatomic differences in bacterial communities within the same lung of subjects with advanced COPD. These studies are further demonstration of the pulmonary microbiome and highlight global and micro-anatomic changes in these bacterial communities in severe COPD patients

Compositionality and Concepts

In this chapter I aim to explain how psychology understands concepts, and why there is a need for semantic theory to take on the challenge of psychological data. All of the contributors to this volume are (presumably) in the business of trying to understand and explain how language has meaning, and the primary source of evidence for this has to be our intuitions of what things mean. Furthermore, if my semantic intuitions (as a theorist) are out of kilter with those of the common language user, then it is my theory which should be called into question and not the lay intuition. This chapter describes a range of results from my research program over the last 30 years, some old and some new, with the aim of giving a general account of using Prototype Theory as a way to explain semantic intuitions

Standards and Practices for Forecasting

One hundred and thirty-nine principles are used to summarize knowledge about forecasting. They cover formulating a problem, obtaining information about it, selecting and applying methods, evaluating methods, and using forecasts. Each principle is described along with its purpose, the conditions under which it is relevant, and the strength and sources of evidence. A checklist of principles is provided to assist in auditing the forecasting process. An audit can help one to find ways to improve the forecasting process and to avoid legal liability for poor forecasting

Evaluating Forecasting Methods

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods