America’s Crowded Statehouses: Measuring and Explaining Lobbying in the U.S. States

2019 ◽  
Vol 19 (3) ◽  
pp. 351-374 ◽  
Author(s):  
James Strickland
Keyword(s):  
Interest Groups ◽  
Direct Democracy ◽  
The United States ◽  
Term Limits ◽  
State Spending ◽  
Data Set ◽  
Institutional Variables ◽  
Party Dominance ◽  
Causal Variables

Across the United States over time, numbers of registered interest groups have continued to increase, but these populations mask the total amount of lobbying that is occurring within America’s statehouses. Among registered interests, average numbers of hired lobbyists have increased markedly since the late 1980s. This study both quantifies this increase and identifies a set of causal variables. Previous studies have proposed a variety of short-term, political and long-term, institutional factors that govern rates of lobbying. Using a new data set spanning multiple decades, I find that changes in lobbying can largely be ascribed to institutional variables, including the implementation of term limits and regulations on lobbying. Lobby regulations, one-party dominance, and legislative expenditures also appear to play a role in determining rates of multiclient lobbying. Direct democracy and state spending do not affect the hiring of lobbyists by registered interest groups.

The Effects of the Opioid Epidemic on Prescribing Practices in Long-Term Care Residents

2019 ◽  
Vol 34 (4) ◽  
pp. 258-267
Author(s):  
Lisa Yamagishi ◽  
Olivia Erickson ◽  
Kelly Mazzei ◽  
Christine O'Neil ◽  
Khalid M. Kamal
Keyword(s):  
Severe Pain ◽  
Long Term Care ◽  
Care Facility ◽  
The United States ◽  
Self Report ◽  
Prescribing Practices ◽  
Term Care ◽  
Data Set ◽  
Opioid Prescribing

OBJECTIVE: Evaluate opioid prescribing practices for older adults since the opioid crisis in the United States.<br/> DESIGN: Interrupted time-series analysis on retrospective observational cohort study.<br/> SETTING: 176-bed skilled-nursing facility (SNF).<br/> PARTICIPANTS: Patients admitted to a long-term care facility with pain-related diagnoses between October 1, 2015, and March 31, 2017, were included. Residents discharged prior to 14 days were excluded. Of 392 residents, 258 met inclusion criteria with 313 admissions.<br/> MAIN OUTCOME MEASURE: Changes in opioid prescribing frequency between two periods: Q1 to Q3 (Spring 2016) and Q4 to Q6 for pre- and postgovernment countermeasure, respectively.<br/> RESULTS: Opioid prescriptions for patients with pain-related diagnoses decreased during period one at -0.10% per quarter (95% confidence interval [CI] -0.85-0.85; P = 0.99), with the rate of decline increasing at -3.8% per quarter from period 1 and 2 (95% CI -0.23-0.15; P = 0.64). Opioid prescribing from top International Classification of Diseases, Ninth Revision category, "Injury and Poisoning" decreased in prescribing frequency by -3.0% per quarter from Q1 to Q6 (95% CI -0.16-0.10; P = 0.54). Appropriateness of pain-control was obtained from the Minimum Data Set version 3.0 "Percent of Residents Who Self-Report Moderate to Severe Pain (Short Stay)" measure; these results showed a significant increase in inadequacy of pain relief by 0.28% per quarter (95% CI 0.12-0.44; P = 0.009).<br/> CONCLUSION: Residents who self-report moderate- to severe pain have significantly increased since October 2015. Opioid prescriptions may have decreased for elderly patients in SNFs since Spring 2016. Further investigation with a larger population and wider time frame is warranted to further evaluate significance.

scholarly journals Long-term changes of tropospheric NO<sub>2</sub> over megacities derived from multiple satellite instruments

2012 ◽  
Vol 12 (12) ◽  
pp. 31767-31828 ◽  
Author(s):  
A. Hilboll ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Time Series ◽  
Middle East ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
The United States ◽  
Central China ◽  
Full Time ◽  
Data Set ◽  
Tropospheric No2

Abstract. Tropospheric NO2, a key pollutant in particular in cities, has been measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. These data provide a unique global long-term data set of tropospheric pollution. However, the measurements differ in spatial resolution, local time of measurement, and measurement geometry. All these factors can severely impact the retrieved NO2 columns, which is why they need to be taken into account when analysing time series spanning more than one instrument. In this study, we present several ways to explicitly account for the instrumental differences in trend analyses of the NO2 columns derived from satellite measurements, while preserving their high spatial resolution. Both a physical method, based on spatial averaging of the measured earthshine spectra and extraction of a resolution pattern, and statistical methods, including instrument-dependent offsets in the fitted trend function, are developed. These methods are applied to data from GOME and SCIAMACHY separately, to the combined time series and to an extended data set comprising also GOME-2 and OMI measurements. All approaches show consistent trends of tropospheric NO2 for a selection of areas on both regional and city scales, for the first time allowing consistent trend analysis of the full time series at high spatial resolution and significantly reducing the uncertainties of the retrieved trend estimates compared to previous studies. We show that measured tropospheric NO2 columns have been strongly increasing over China, the Middle East, and India, with values over East Central China triplicating from 1996 to 2011. All parts of the developed world, including Western Europe, the United States, and Japan, show significantly decreasing NO2 amounts in the same time period. On a megacity level, individual trends can be as large as +27 ± 3.7% yr−1 and +20 ± 1.9% yr−1 in Dhaka and Baghdad, respectively, while Los Angeles shows a very strong decrease of −6.0 ± 0.37% yr−1. Most megacities in China, India, and the Middle East show increasing NO2 columns of +5–10% yr−1, leading to a doubling to triplication within the observed period. While linear trends derived with the different methods are consistent, comparison of the GOME and SCIAMACHY time series as well as inspection of time series over individual areas shows clear indication of non-linear changes in NO2 columns in response to rapid changes in technology used and the economical situation.

The Structures of Interest Coalitions: Evidence from Environmental Litigation

2003 ◽  
Vol 5 (01) ◽  
pp. 45-64 ◽  
Author(s):  
Andrew B. Whitford
Keyword(s):  
Interest Groups ◽  
Interest Group ◽  
Coalition Formation ◽  
Low Cost ◽  
United States Supreme Court ◽  
The United States ◽  
Environmental Interest Groups ◽  
Minority Interests ◽  
Regulatory Politics

This paper addresses the intersection of coalition formation, judicial strategies, and regulatory politics. Coalitions are a low-cost means for assembling minority interests into more powerful blocs. However, in most cases in regulatory politics, judicial strategies are high cost efforts. I argue that coalitions among interests form one basis for judicial participation, but that participation manifests in an array of coalition “microstructures.” For any one event, the microstructure of the interest group coalition varies, but across events the coalitions take on general forms. The paper offers evidence for a variety of coalition microstructures in interest group participation as amici curiae (“friends of the court”) in cases before the United States Supreme Court. The evidence is drawn from the case of the Group of Ten, a stable, long-term coalition of environmental interest groups that operated from 1981 to 1991.

scholarly journals The COVID-19 Pandemic and the Rental Market: Evidence From Craigslist

2021 ◽  
pp. 000276422110031
Author(s):  
John Kuk ◽  
Ariela Schachter ◽  
Jacob William Faber ◽  
Max Besbris
Keyword(s):  
Rental Housing ◽  
Past Research ◽  
The United States ◽  
Data Set ◽  
Rental Market ◽  
Environmental Disasters ◽  
Local Spread ◽  
White Neighborhoods ◽  
Long Term Trends

Past research has demonstrated the racially and spatially uneven impacts of economic shocks and environmental disasters on various markets. In this article, we examine if and how the first few months of the COVID-19 pandemic affected the market for rental housing in the 49 largest metropolitan areas in the United States. Using a unique data set of new rental listings gathered from Craigslist and localized measures of the pandemic’s severity we find that, from mid-March to early June, local spread of COVID-19 is followed by reduced median and mean rent. However, this trend is driven by dropping rents for listings in Black, Latino, and diverse neighborhoods. Listings in majority White neighborhoods experience rent increases during this time. Our analyses make multiple contributions. First, we add to the burgeoning literature examining the rental market as a key site of perpetuating sociospatial inequality. Second, we demonstrate the utility of data gathered online for analyzing housing. And third, by reflecting on research that shows how past crises have increased sociospatial inequality and up-to-date work showing the racially and spatially unequal effects of the COVID-19 pandemic, we discuss some possible mechanisms by which the pandemic may be affecting the market for rental housing as well as implications for long-term trends.

scholarly journals Calling referendums on domestic policies: how political elites and citizens differ

2021 ◽  
Author(s):  
Sergiu Gherghina ◽  
Nanuli Silagadze
Keyword(s):  
Direct Democracy ◽  
National Level ◽  
Original Data ◽  
Political Elites ◽  
Data Set ◽  
Institutional Variables ◽  
Coalition Governments ◽  
Country Level ◽  
Neighboring Country ◽  
Domestic Policies

AbstractMost national level referendums in Europe since 1793 are initiated either by political elites or by citizens. It remains unclear why these two types of initiators call for referendums. This article aims to explain under what circumstances political elites and citizens call referendums on domestic policies. The analysis is conducted at country level using an original data set that covers 461 national level referendums in Europe between 1793 and 2019. It tests the influence of four institutional variables that in theory are expected to have a divergent effect for the two types of initiators. The experience with direct democracy increases the likelihood to have referendums called by elites and reduces the incidence of citizen-initiated referendums. More authoritarian countries and longer time passed from referendums in a neighboring country explain why political elites initiate referendums. Coalition governments are more prone to citizen-initiated referendums on domestic policies compared to single-party governments.

scholarly journals Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data

10.2196/21418 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e21418
Author(s):  
Danny Valdez ◽  
Marijn ten Thij ◽  
Krishna Bathina ◽  
Lauren A Rutter ◽  
Johan Bollen
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Social Media ◽  
Media Use ◽  
The United States ◽  
Social Media Use ◽  
Data Set ◽  
Daily Lives ◽  
At Home

Background The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being. Objective This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic? Methods We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. Results LDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March. Conclusions Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Evaluation of a Model for Estimating Infragravity Waves in Shallow Water: Accuracy and Suitability for Long-Term Climatologies

2009 ◽  
Author(s):  
Matthijs Bijl ◽  
Ad Reniers ◽  
Kevin Ewans ◽  
Stephen Masterton ◽  
Rene´ Huijsmans
Keyword(s):  
Shallow Water ◽  
Surf Zone ◽  
The United States ◽  
Design Criteria ◽  
Data Set ◽  
Infragravity Waves ◽  
North West ◽  
Wave Heights ◽  
Infragravity Wave

With interest in developing shallow-water facilities on the increase, primarily for offloading LNG, there is also growing interest in infragravity waves. In particular, it is recognized that infragravity waves can have an important influence on the motions of tankers in shallow-water regions exposed to the open ocean, and therefore they need to be considered in the design and operation of the moorings and offloading facilities. Accordingly, there is a need to model infragravity waves both for design calculations and to be able to estimate the design criteria themselves. This paper is concerned with the later — setting infragravity wave design criteria, or more precisely on details involved in establishing an appropriate infragravity wave database from which design criteria can be estimated. The focus is on the accuracy of using the 1D linear Surf Beat model (IDSB) for estimating nearshore infragravity wave heights. The study has focused on field measurements made by U.S. Corp of Army Engineers Field Research Facility location at Duck on the east coast of the United States, and another location at Baja on the north west coast of Mexico. At the Duck location, the study involved data recorded in shallow water (8 meters water depth) with a pressure transducer array, while at the Baja location, data from a directional Waverider buoy with GPS are used. The “short wave” directional spectra from the measured data are used as input to the IDSB model, to compute the total infragravity response generated by the transformation of the grouped short waves through the surf zone including bound long waves, leaky waves and edge waves. The computed root mean square infragravity wave heights have been compared with measured infragravity waves at the respective sites, and assessment has been made of the accuracy of the predictions. The computed results show good agreement with the measured infragravity waves and provide confidence that the IDSB is a suitable tool for developing a long-term infragravity data set for developing design criteria.

scholarly journals Operational-based annual energy production uncertainty: are its components actually uncorrelated?

2020 ◽  
Vol 5 (4) ◽  
pp. 1435-1448
Author(s):  
Nicola Bodini ◽  
Mike Optis
Keyword(s):  
Wind Speed ◽  
Linear Regression ◽  
Interannual Variability ◽  
Wind Power ◽  
Energy Production ◽  
The United States ◽  
Reanalysis Data ◽  
Data Set ◽  
Positive Correlation

Abstract. Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1 % and as large as 0.5 % for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

scholarly journals Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data (Preprint)

2020 ◽  
Author(s):  
Danny Valdez ◽  
Marijn ten Thij ◽  
Krishna Bathina ◽  
Lauren A Rutter ◽  
Johan Bollen
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Social Media ◽  
Media Use ◽  
The United States ◽  
Social Media Use ◽  
Data Set ◽  
Daily Lives ◽  
At Home

BACKGROUND The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being. OBJECTIVE This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic? METHODS We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. RESULTS LDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March. CONCLUSIONS Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Speech-Language Pathology in the Peace Corps: Necessity and Sustainability

2012 ◽  
Vol 2 (1) ◽  
pp. 11-18
Author(s):  
Melissa A. Pierce
Keyword(s):  
United States ◽  
Special Education ◽  
Peace Corps ◽  
People With Disabilities ◽  
Communication Disorders ◽  
The United States ◽  
Speech Language Pathology ◽  
Speech Language Pathologists ◽  
Language Pathology

In countries other than the United States, the study and practice of speech-language pathology is little known or nonexistent. Recognition of professionals in the field is minimal. Speech-language pathologists in countries where speech-language pathology is a widely recognized and respected profession often seek to share their expertise in places where little support is available for individuals with communication disorders. The Peace Corps offers a unique, long-term volunteer opportunity to people with a variety of backgrounds, including speech-language pathologists. Though Peace Corps programs do not specifically focus on speech-language pathology, many are easily adapted to the profession because they support populations of people with disabilities. This article describes how the needs of local children with communication disorders are readily addressed by a Special Education Peace Corps volunteer.

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