Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Can money buy control of Congress?

ABSTRACT: Can a political party spend enough across electoral campaigns to garner a majority within the U.S. Congress? Prior research on campaign spending minimizes the importance of campaign heterogeneity and fails to aggregate effects across campaigns, rendering it unable to address this question. Instead, we tackle the question with a system-level analysis of campaign expenditures. First, using a flexible machine learning approach, we show that spending has substantial and nonlinear marginal effects on outcomes at the level of the campaign. Second, by aggregating these effects to the entire U.S. Congress, we show that large seat swings that change congressional control have, in the past, been possible for expenditure levels consonant with those presently observed after having removed the most extreme levels. However, this possibility appears to have faded over the past decade. Our approach also allows us to illustrate the often significant effects that eliminating campaign spending could have.

SUBMITTER: Minozzi W

PROVIDER: S-EPMC11207044 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Can money buy control of Congress?

Minozzi William W Madson Gabriel J GJ Siegel David A DA

PloS one 20240626 6

Can a political party spend enough across electoral campaigns to garner a majority within the U.S. Congress? Prior research on campaign spending minimizes the importance of campaign heterogeneity and fails to aggregate effects across campaigns, rendering it unable to address this question. Instead, we tackle the question with a system-level analysis of campaign expenditures. First, using a flexible machine learning approach, we show that spending has substantial and nonlinear marginal effects on ...[more]

PMID: 38923996

Similar Datasets

Money can't buy you satisfaction.

Project description: Not available

| S-EPMC554041 | biostudies-literature

Friendships that money can buy: financial security protects health in retirement by enabling social connectedness.

Project description:BackgroundResearch on the health and wellbeing of retirees has tended to focus on financial security and financial planning. However, we suggest that one reason why financial security is important for retirees is that it enables social connectedness, which is critical for healthy ageing.MethodsThis paper tests this hypothesis cross-sectionally (N = 3109) and longitudinally (N = 404) using a population-weighted mixed effects mediation model in two nationally representative samples of Australian retirees.ResultsAnalyses provide robust support for our model. Subjective financial security predicted retiree health cross-sectionally and longitudinally. Social connectedness also consistently predicted mental health and physical health, on average four times more strongly than financial security. Furthermore, social connectedness partially accounted for the protective effect of subjective financial security.ConclusionsWe discuss the implications of these findings for public health, with a particular emphasis on how social connectedness can be better supported for people transitioning to retirement.

| S-EPMC6868702 | biostudies-literature

Money can't buy happiness: A randomized controlled trial of a digital mental health app with versus without financial incentives.

Project description:Mental health disorders are prevalent among college students and increasing in frequency and severity. However, there is a significant gap between those who need treatment and those who engage in treatment. Given the documented efficacy of financial incentives for promoting health behavior change and engagement in treatment, financial incentives may help, along with nonfinancial behavioral incentives such as motivational messaging, gamification, and loss aversion techniques. We compared brief (28-day) use of two versions of a behavioral economics-inspired digital mental health app, NeuroFlow: (1) the full app including financial incentives and nonfinancial behavioral incentives (treatment group) and (2) a version of the app with nonfinancial behavioral incentives only (control group). In our intent-to-treat analyses, in order to examine the primary outcome of app engagement, a one-way analysis of variance (ANOVA) (treatment vs. control) was conducted, and to examine the secondary outcomes (depression, anxiety, emotion dysregulation, and wellbeing), a two-way repeated measures ANOVAs (treatment vs. control × baseline vs. post-trial) were conducted. We found that there were no differences between treatment groups on app engagement or the change in the mental health/wellness outcome measures. There was a main effect of timepoint on symptoms of anxiety and emotion dysregulation, such that there were significantly lower self-reported symptoms at post-trial relative to baseline. Our results suggest that financial incentives in digital mental health apps over and above nonfinancial behavioral incentives do not have an impact on app engagement or mental health/wellness outcomes.

| S-EPMC10286537 | biostudies-literature

Money Does Not Always Buy Happiness, but Are Richer People Less Happy in Their Daily Lives? It Depends on How You Analyze Income

Project description:Do people who have more money feel happier during their daily activities? Some prior research has found no relationship between income and daily happiness when treating income as a continuous variable in OLS regressions, although results differ between studies. We re-analyzed existing data from the United States and Germany, treating household income as a categorical variable and using lowess and spline regressions to explore nonlinearities. Our analyses reveal that these methodological decisions change the results and conclusions about the relationship between income and happiness. In American and German diary data from 2010 to 2015, results for the continuous treatment of income showed a null relationship with happiness, whereas the categorization of income showed that some of those with higher incomes reported feeling less happy than some of those with lower incomes. Lowess and spline regressions suggested null results overall, and there was no evidence of a relationship between income and happiness in Experience Sampling Methodology (ESM) data. Not all analytic approaches generate the same results, which may contribute to explaining discrepant results in existing studies about the correlates of happiness. Future research should be explicit about their approaches to measuring and analyzing income when studying its relationship with subjective well-being, ideally testing different approaches, and making conclusions based on the pattern of results across approaches.

| S-EPMC9199446 | biostudies-literature

Article 7. Money, money, money. (Where does it come from, how do we control it, and how far should we go to get more?).

Project description: Not available

| S-EPMC1725838 | biostudies-literature

XI SOLANEP International Congress / XV Cystic Fibrosis Latinamerican Congress / XV Brazilian Congress of Pediatric Pulmonology.

Project description: Not available

| S-EPMC7318085 | biostudies-literature

To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines).

Project description:Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. KEY POINTS: • Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools. • We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications. • Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

| S-EPMC8128726 | biostudies-literature