scholarly journals Breaking Bad in Mississippi: Do County-Level Alcohol Sale Bans Encourage Crystal Methamphetamine Production and Consumption?

2016 ◽  
Vol 11 (3) ◽  
pp. 55-66
Author(s):  
Maury Granger ◽  
Gregory Price
Keyword(s):  
Negative Binomial ◽  
Knowledge Spillovers ◽  
The State ◽  
Relative Price ◽  
Parameter Estimates ◽  
County Level ◽  
Crystal Methamphetamine ◽  
Production And Consumption ◽  
Binomial Parameter ◽  
Hard Liquor

AbstractIf alcohol has substitutes, changes in its relative price can encourage the production and consumption of other illicit and harmful drugs. This paper considers if county-level bans on the sale of alcohol in the state of Mississippi encourage the production and consumption of crystal methamphetamine. We estimate the parameters of a drug production function in which the inputs are the density of people and firms, underscoring the importance of learning and knowledge spillovers to production and consumption. Poisson and Negative Binomial parameter estimates reveal that county-level bans on hard liquor sales; but not on beer and wine, increase the number of crystal methamphetamine labs. In the absence of such laws, there would be approximately 308 fewer crystal methamphetamine labs in the state of Mississippi. Our findings suggest that in Mississippi, which is the least healthiest state in the nation, county-level bans on hard liquor sales are not welfare improving as they encourage substitution for a drug that is potentially more harmful to individual health than alcohol.

Assessment of the state of the heat economy of the Republic of Sakha (Yakutia)

2020 ◽  
Vol 93 (4) ◽  
pp. 88-100
Author(s):  
V. R. Darbasov ◽  
◽  
M. Р. Solomonov ◽  
Keyword(s):  
Supply And Demand ◽  
Northern Region ◽  
Heat Energy ◽  
The State ◽  
Resource Saving ◽  
The North ◽  
Production And Consumption ◽  
Fixed Assets ◽  
The Republic ◽  
Heat Energy Market

The article assesses the state of the heat economy of the Northern region. The purpose of the article is to reveal the reasons for chronic backwardness of the region's industry from the average Russian indicators. To achieve the goal, solved the following problems: the features of heat economy in the North, analyzes the housing development, production and consumption of heat energy, as the sources of heat energy and heat networks, and also reforms in the heat economy of the region, based on which conclusions on assessment of the heat economy of the region. In recent years, there has been a twofold decrease in the rate of renewal of fixed assets of the heat economy against the norm, low rates of introduction of the resource-saving technologies in the heat economy, and in general, in the housing and communal services of the region. The level of marginal balance of supply and demand in the heat energy market is determined. The article is written to correct the decisions of the Federal and regional Executive authorities in terms of ensuring the reliability of heat economy of the Northern region.

Surface and Substance: Baroque Dress in Spain and France, 1600–1720

2019 ◽  
pp. 237-263
Author(s):  
Lesley Ellis Miller
Keyword(s):  
Seventeenth Century ◽  
The State ◽  
Global Networks ◽  
Continuity And Change ◽  
Economic Significance ◽  
System A ◽  
Production And Consumption ◽  
State Textile

This article explores the surface and substance of elite dress in the baroque period by unpacking printed texts and images that reveal their political and economic significance in the courts of Europe. It does so by considering the nature and sources of garments and fabrics, continuity and change in their production and consumption in Spain and France, and the shaping of the modern fashion system—a system in which changes in textiles and trimmings were promoted seasonally by the state, textile manufacturers, and the nascent fashion press (Le Mercure galant) from the late seventeenth century onward. It thus underlines the local and global networks involved in the production and consumption of dress.

scholarly journals Assessing the Economic and Environmental Impacts of Alternative Renewable Portfolio Standards: Winners and Losers

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3319
Author(s):  
Jamal Mamkhezri ◽  
Leonard A. Malczynski ◽  
Janie M. Chermak
Keyword(s):  
New Mexico ◽  
Environmental Impacts ◽  
The State ◽  
Policy Implications ◽  
Renewable Portfolio Standard ◽  
County Level ◽  
Renewable Portfolio Standards ◽  
Energy Potential ◽  
Output Analysis ◽  
The Impact

State-mandated renewable portfolio standards affect substantial portions of the total U.S. electricity supply. Renewable portfolio standards are environmentally motivated policies, yet they have the potential to greatly impact economy. There is not an agreement in the literature on the impact of renewable portfolio standards policies on regional economies, especially on job creation. By integrating various methodologies including econometrics, geographic information system, and input–output analysis into a unique system dynamics model, this paper estimates the economic and environmental impacts of various renewable portfolio standards scenarios in the state of New Mexico, located in Southwestern U.S. The state is endowed with traditional fossil fuel resources and substantial renewable energy potential. In this work we estimated and compared the economic and environmental tradeoffs at the county level under three renewable portfolio standards: New Mexico’s original standard of 20% renewables, the recently adopted 100% renewables standard, and a reduced renewable standard of 10%. The final one would be a return to a more traditional generation profile. We found that while the 20% standard has the highest market-based economic impact on the state as a whole, it is not significantly different from other scenarios. However, when environmental impacts are included, the 100% standard yields the highest value. In addition, while the state level economic impacts across the three scenarios are not significantly different, the county-level impacts are substantial. This is especially important for a state like New Mexico, which has a high reliance on energy for economic development. A higher renewable portfolio standard appears to be an economic tool to stimulate targeted areas’ economic growth. These results have policy implications.

scholarly journals Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

2021 ◽  
Vol 13 (11) ◽  
pp. 6214
Author(s):  
Bumjoon Bae ◽  
Changju Lee ◽  
Tae-Young Pak ◽  
Sunghoon Lee
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Random Effect ◽  
Temporal Correlation ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Parameter Estimates ◽  
Crash Frequency ◽  
Crash Frequency Modeling ◽  
Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

Frequency distribution of Wuchereria bancrofti microfilariae in human populations and its relationships with age and sex

Parasitology ◽  
1990 ◽  
Vol 101 (3) ◽  
pp. 429-434 ◽  
Author(s):  
P. K. Das ◽  
A. Manoharan ◽  
A. Srividya ◽  
B. T. Grenfell ◽  
D. A. P. Bundy ◽  
...  
Keyword(s):  
Frequency Distribution ◽  
Negative Binomial ◽  
Age Distribution ◽  
Indirect Evidence ◽  
Wuchereria Bancrofti ◽  
Parameter Estimates ◽  
Human Populations ◽  
Sampling Errors ◽  
Frequency Distributions ◽  
Age And Sex

SUMMARYThis paper examines the effects of host age and sex on the frequency distribution of Wuchereria bancrofti infections in the human host. Microfilarial counts from a large data base on the epidemiology of bancroftian filariasis in Pondicherry, South India are analysed. Frequency distributions of microfilarial counts divided by age are successfully described by zero-truncated negative binomial distributions, fitted by maximum likelihood. Parameter estimates from the fits indicate a significant trend of decreasing overdispersion with age in the distributions above age 10; this pattern provides indirect evidence for the operation of density-dependent constraints on microfilarial intensity. The analysis also provides estimates of the proportion of mf-positive individuals who are identified as negative due to sampling errors (around 5% of the total negatives). This allows the construction of corrected mf age–prevalence curves, which indicate that the observed prevalence may underestimate the true figures by between 25% and 100%. The age distribution of mf-negative individuals in the population is discussed in terms of current hypotheses about the interaction between disease and infection.

SPATIAL PATTERN OF THE IMMATURE STAGES OF HYLEMYA BRASSICAE ON CABBAGE

1970 ◽  
Vol 102 (10) ◽  
pp. 1216-1222 ◽  
Author(s):  
M. K. Mukerji ◽  
D. G. Harcourt
Keyword(s):  
Spatial Pattern ◽  
Poisson Distribution ◽  
Negative Binomial ◽  
Immature Stages ◽  
Expected Number ◽  
Chi Square ◽  
Binomial Series ◽  
Binomial Parameter ◽  
Hylemya Brassicae ◽  
Cabbage Maggot

AbstractCounts of the cabbage maggot, Hylemya brassicae (Bouché), on cabbage did not conform to the Poisson distribution, there being an excess of uninfested and highly infested plants over the expected number. But when the negative binomial series was fitted to the observed distribution, the discrepancies were not significant when tested by chi-square. The negative binomial parameter k tended to increase with density. Using a common k, the distribution of the various stages may be described by expansion of (q − p)−k, when values of k are as follows: egg 0.78, larva 0.71, pupa 0.84. Three different transformations are offered for stabilizing the variance of field counts.

Adaptive Estimation Using Multiagent Network Identifiers With Undirected and Directed Graph Topologies

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Teymur Sadikhov ◽  
Michael A. Demetriou ◽  
Wassim M. Haddad ◽  
Tansel Yucelen
Keyword(s):  
Directed Graph ◽  
Adaptive Estimation ◽  
Parameter Estimate ◽  
The State ◽  
Adaptive Observer ◽  
Parameter Estimates ◽  
Communication Architecture ◽  
Adaptive Identifier ◽  
The Mean ◽  
Additive Term

In this paper, we present an adaptive estimation framework predicated on multiagent network identifiers with undirected and directed graph topologies. Specifically, the system state and plant parameters are identified online using N agents implementing adaptive observers with an interagent communication architecture. The adaptive observer architecture includes an additive term which involves a penalty on the mismatch between the state and parameter estimates. The proposed architecture is shown to guarantee state and parameter estimate consensus. Furthermore, the proposed adaptive identifier architecture provides a measure of agreement of the state and parameter estimates that is independent of the network topology and guarantees that the deviation from the mean estimate for both the state and parameter estimates converge to zero. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

scholarly journals 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

Mixed Generalized Linear Model for Estimating Household Trip Production

2000 ◽  
Vol 1718 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Chang-Jen Lan ◽  
Patricia S. Hu
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Empirical Distribution ◽  
Distribution Functions ◽  
Model Specification ◽  
Parameter Estimates ◽  
Modeling Framework ◽  
Personal Transportation ◽  
Trip Production

An innovative modeling framework to estimate household trip rates using 1995 Nationwide Personal Transportation Survey data is presented. A generalized linear model with a mixture of negative binomial probability distribution functions was developed on the basis of characteristics observed from the empirical distribution of household daily trips. This model provides a more flexible framework and a better model specification for analyzing household-specific trip production behavior. Compared with traditional least squares-based regression models, the parameter estimates from the proposed model are more efficient. Although the mean accuracies from the two modeling approaches are comparable, the mixed generalized linear model is more robust in identifying outliers due to its unsymmetric prediction bounds derived from more correct model specification.

Sign in / Sign up

Export Citation Format

Share Document