Climate Change is Likely to Alter Sheep and Goat Distributions in Mainland China

Climate change endangers food security worldwide, especially in developing countries. Livestock husbandry is one of the essential livelihoods for farmers and herders in remote arid and semiarid regions. However, it remains unclear how climate change will impact livestock husbandry in the future. This study collected sheep and goat distributions from the “gridded livestock of the world” (GLW) dataset for 1943 counties in Mainland China. Current climate data include precipitation and temperature from the National Meteorological Information Center (NMIC). We disentangled the effects of precipitation and temperature on current distributions of sheep and goats with the Bayesian Hierarchical Model by Integrated Nest Laplace Approximation (INLA). Further, we forecasted the potential sheep and goat distributions in 2030 and 2050 under Coupled Model Intercomparison Project (CMIP) scenarios. Our result showed that sheep distribution is significantly correlated with elevation, slope, market density, and highway distance, with absolute correlation coefficients ranging from 0.019 to 0.411. In addition to elevation, slope, and market density, goat distribution is also affected by gain production, with a correlation coefficient of 0.055. There is a dynamic correlation of temperature and precipitation with sheep and goat density. The sheep density distribution is predicted to increase in Northwest China, while the goat density distribution might increase in farming areas under climate change. Finally, this study suggests for the sheep and goat breeding industry to respond to climate change.

Spatial Modelling for Cholera Incidence Rates in Morogoro Municipality, Tanzania

Spatial modelling was conducted to examine community factors associated with cholera incidence rates in Morogoro Municipality. The study employed both secondary (cholera cases) and primary (geographic coordinates of community risk factors) data. Spatial lag model was applied in examining association between the variables. All wards of Morogoro Municipality were considered in the study to capture their variations because cholera cases have a tendency to be clustered. Results indicated that market density, distance to the market and distance to the dumpster are significant factors associated with cholera incidence rates in the wards (p < 0.05). Geographically weighted Poisson model was used to show the variations of those factors between the wards in Municipality. A statistically significant positive association of cholera incidence rates; and market density was only found in Mazimbu ward (p < 0.05) and distance from the community to the dumpster was found in Kihonda, Kingolwira, Bigwa, Kichangani, Kilakala and Boma wards (p < 0.001) and some wards at the centre of the municipality which are Mji Mkuu and Kingo (p < 0.05). A statistically significant negative association of cholera incidence rates and distance from the centre of the community to the market was found in Kihonda, Kingolwira and Kichangani (p < 0.001) and Bigwa wards (p < 0.05). Therefore, measures taken to control and prevent cholera disease should base on the variations of the risk factors found in the Municipal wards. Keywords: Cholera incidence rate; Spatial lag regression model; Community risk factor; Geographically weighted Poisson model

Opportunities and Constraints of the Partner Market and Educational Assortative Mating

This study aims to determine to what extent the opportunities and restrictions of the partner market influence educational assortative mating. It also analyzes the interplay between the opportunity structure and preferences. Matching district-based partner market indicators to heterosexual couples when they move in together based on the German Socio-Economic Panel, we find strong effects of the opportunity structure on educational homogamy. The results further imply that the density of the supply of potential partners is more important for educational assortative mating than imbalanced supply and competition. While the impact of partner market imbalances on assortative mating is a mere effect of the opportunity structure, the effects of the partner market density of relevant and available partners in space weakly imply that homophile and maximization preferences are simultaneously at work.

Collaboration Model for Service Clustering in Last-Mile Delivery

As e-commerce is rapidly expanding, efficient and competitive product delivery system to the final customer is highly required. Recently, the emergence of a smart platform is leading the transformation of distribution, performance, and quality in express delivery services, especially in the last-mile delivery. The business to consumer (B2C) through smart platforms such as Amazon in America and Coupang in Korea utilizes the differentiated delivery rates to increase the market share. In contrast, the small and medium-sized express delivery companies with low market share are trying hard to expand their market share. In order to fulfill all customer needs, collaboration is needed. This study aims to construct a collaboration model to maximize the net profit by considering the market density of each company. A Baduk board game is used to derive the last-mile delivery time function of market density. All companies in collaboration have to specialize the delivery items into certain service clustering types, which consist of regular, big sized/weighted, and cold items. The multi-objective programming model is developed based on max-sum and max-min criteria. The Shapley value and nucleolus approaches are applied to find the profit allocation. Finally, the applicability of the proposed collaboration model is shown through a numerical example.

Selling Home

This essay explores the emergence of new forms of retail in late nineteenth and early twentieth century Bombay, an era which saw the development of new shopping districts, department stores, showrooms, and retail culture in the city. In a city known for its market density and commercial vibrancy, elite retailers tried to reach out to consumers in new ways, enticing them in from the street with window displays, standardized product lines, and novel assemblages of goods, while also contacting consumers directly through catalogues, flyers, designs sent on request, and home deliveries. Focusing on major department stores like the Army and Navy Stores and Whiteaway Laidlaw, major nationalist concerns like the Bombay Swadeshi Store and Godrej and Boyce, as well as smaller showrooms featuring fewer ranges of goods, the essay argues that novel retail strategies efforts helped to shape not just how things were sold but what was desired in Bombay—noting in particular how efforts to sell domestic furnishings promoted new ideas about what the home should be.

Frictions in a Competitive, Regulated Market: Evidence from Taxis

This paper presents a dynamic equilibrium model of a taxi market. The model is estimated using data from New York City yellow cabs. Two salient features by which most taxi markets deviate from the efficient market ideal are, first, matching frictions created by the need for both market sides to physically search for trading partners, and second, regulatory limitations to entry. To assess the importance of these features, we use the model to simulate the effect of changes in entry, alternative matching technologies, and different market density. We use the geographical features of the matching process to back out unobserved demand through a matching simulation. The matching function exhibits increasing returns to scale, which is important to understand the impact of changes in this market and has welfare implications. For instance, although alternative dispatch platforms can be more efficient than street-hailing, platform competition is harmful because it reduces effective density. (JEL C78, L51, L84, L92, L98, R48)

Pricing and Collaboration in Last Mile Delivery Services

Recently, last mile delivery has emerged as an essential process that greatly affects the opportunity of obtaining delivery service market share due to the rapid increase in the business-to-consumer (B2C) service market. Express delivery companies are investing to expand the capacity of hub terminals to handle increasing delivery volume. As for securing massive delivery quantity by investment, companies must examine the profitability between increasing delivery quantity and price. This study proposes two strategies for a company’s decision making regarding the adjustment of market density and price by developing a pricing and collaboration model based on the delivery time of the last mile process. A last mile delivery time function of market density is first derived from genetic algorithm (GA)-based simulation results of traveling salesman problem regarding the market density. The pricing model develops a procedure to determine the optimal price, maximizing the profit based on last mile delivery time function. In addition, a collaboration model, where a multi-objective integer programming problem is developed, is proposed to sustain long-term survival for small and medium-sized companies. In this paper, sensitivity analysis demonstrates the effect of delivery environment on the optimal price and profit. Also, a numerical example presents four different scenarios of the collaboration model to determine the applicability and efficiency of the model. These two proposed models present managerial insights for express delivery companies.

Using Local Information to Improve Short-Run Corn Price Forecasts

We examine the short-run forecasting problem in a data set of daily prices from 134 corn buying locations from seven states – Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We ask the question: is there useful forecasting information in the cash bids from nearby markets? We use several criteria, including a Granger causality criterion, to specify forecast models that rely on the recent history of a market, the recent histories of nearby markets, and the recent histories of futures prices. For about 65% of the markets studied, the model consisting of futures prices, a market’s own history, and the history of nearby markets forecasts better than a model only incorporating futures prices and the market’s own history. That is, nearby markets have predictive content. But the magnitude varies with the forecast horizon. For short-run forecasts, the forecast accuracy improvement from including nearby markets is modest. As the forecast horizon increases, however, including nearby prices tends to significantly improve forecasts. We also examine the role played by physical market density in determining the value of incorporating nearby prices into a forecast model.

Evaluating the Contribution of the Built Environment on Obesity Among New York State Students

Objectives. One third of school-aged children in New York State (NYS) are overweight or obese, with large geographic disparities across local regions. We used NYS student obesity surveillance data to assess whether these geographical variations are attributable to the built environment. Method. We combined NYS Student Weight Status Category Reporting System 2010-2012 data with other government publicly available data. Ordinary least squares regression models identified key determinants of school district–level student obesity rates for elementary and middle/high schools. Geographical weighted regression models explored spatial variations in local coefficients of the built environment predictors. Results. From ordinary least squares models, higher farmers’ market density was only significantly associated with lower obesity rates among elementary school students (b = −0.116; p < .01). Higher fast-food restaurant density was significantly associated with higher obesity rates (b = 0.014; p < .05), and higher land use mix was only significantly associated with lower obesity rates (b = −0.054; p < .01) among middle/high school students. In geographical weighted regression analyses, the inverse association between market density and obesity rates among elementary school students was more pronounced in the eastern portion of the state. The relationship between higher fast-food restaurant density and higher obesity rates among middle/high school students was found in the southeastern portion of the state. Conclusions. Different patterns of food consumption may explain varying determinants of obesity between younger and older students. Regional variations in local associations between the built environment variables and obesity may suggest differences in how healthy food sources are accessed locally.