scholarly journals Determinants of the success of equity crowdfunding campaigns

2020 ◽  
Vol 31 (84) ◽  
pp. 560-573
Author(s):  
Israel José dos Santos Felipe ◽  
Bruno César Franca Ferreira
Keyword(s):  
Logistic Regression ◽  
Fixed Effects ◽  
Financial Resources ◽  
Survival Models ◽  
Financial Intermediaries ◽  
Financial Efficiency ◽  
Investment Performance ◽  
Equity Crowdfunding ◽  
Marginal Effects ◽  
Financial Goal

ABSTRACT Given that equity crowdfunding has grown significantly in Brazil and that this market has been frequently sought by startups as an alternative to scarce credit, this study investigated the elements that determine the success of their financing campaigns. The article fills the gap related to the absence of studies analyzing the probability and time of success of startup financing. In Brazil, the research on this is still in its infancy and there has been little discussion regarding what can determine the success of this type of financing. The findings presented here provide managerial contributions for different stakeholders, ranging from platform managers and entrepreneurs to the ordinary citizen, who ultimately acts as an inducer of change in society, without the need for financial intermediaries. The discussion around the elements that influence the success of startup financing has revealed that the characteristics of the venture profile have been able to determine the success of the financing. This information applied to the dynamic of resource allocation in this market can generate more financial efficiency for private agents as an investment performance parameter and, for public agents, as an input for monitoring. The logistic regression with marginal effects and a dummy for time fixed effects were used. The time of success analysis was carried out via survival models. The sample covers 99 startup financing campaigns from 2014 to 2017. The study identified that the financial goal, the venture category, advisor participation, the campaign duration, and the type of equity offered to the investor positively affect both the probability and speed of success of the startup financing. Its contribution lies in the use of these findings to formulate strategies geared toward estimating success, which enable an appropriate allocation of financial resources.

Understanding Charter School Leaders’ Perceptions of Competition in Arizona

2021 ◽  
pp. 0013161X2110373
Author(s):  
Benjamin Creed ◽  
Huriya Jabbar ◽  
Michael Scott
Keyword(s):  
Logistic Regression ◽  
School Choice ◽  
Charter School ◽  
Fixed Effects ◽  
Regression Models ◽  
School Leaders ◽  
State Level ◽  
Local Market ◽  
School Practices ◽  
Logistic Regression Models

Purpose: School choice policies are expected to generate competition leading to improvement in school practices. However, little is known about how competition operates in public education—particularly in charter schools. This paper examines charter-school leaders’ competitive perception formation and the actions taken in response to competition. Research Methods: Using Arizona charter-school leaders’ responses to an original survey, Arizona Department of Education data, and the Common Core of Data, we examined the factors predicting the labeling of a school as a competitor. We estimated fixed effects logistic regression models which examine factors predicting the labeling of competitor schools and of top competitors. We used logistic regression models to understand charter-school leaders’ responses to competition. Findings: We find charter-school leaders in Arizona perceived at least some competition with other schools, and their perceptions vary by urbanicity. While distance between schools mattered generally for labeling a school as a competitor, distance did not factor into labeling “top competitor” schools. Student outcomes did not predict competition between schools, but student demographics were associated with labeling a school a competitor. Charter-school leaders responded to competition through changes in outreach and advertising rather than curriculum and instruction. Competitive responses were related to the respondent school’s quality and the level of perceived competition. Implications for Research and Practice: We found charter-school leaders perceive competition and respond by changing school practices. Responses typically focus on marketing activities over productive responses. The novel state-level analysis allows us to test the effects of local market conditions typically absent in the literature.

scholarly journals Increasing the Size of the Pie: The Impact of Crowding on Nonprofit Sector Resources

2017 ◽  
Vol 8 (3) ◽  
pp. 211-235 ◽  
Author(s):  
Erynn Beaton ◽  
Hyunseok Hwang
Keyword(s):  
Nonprofit Organizations ◽  
Fixed Effects ◽  
Free Market ◽  
Nonprofit Sector ◽  
Financial Resources ◽  
Limited Resources ◽  
Barriers To Entry ◽  
Human Services Organizations ◽  
Nonprofit Competition ◽  
The Impact

AbstractThe number of nonprofit organizations is rapidly increasing, which has led nonprofit practitioners to complain of funding scarcity, nonprofit scholars to closely study nonprofit competition, and policymakers to consider increasing nonprofit barriers to entry. Underlying each of these perspectives is an assumption of limited financial resources. We empirically examine this assumption using county-level panel data on nonprofit human services organizations from the National Center for Charitable Statistics. Contrary to the limited resources assumption, our fixed-effects models show that increasing nonprofit density, at its current levels, has the effect of increasing sector financial resources in each county. We suggest that these findings prompt a tradeoff for policymakers. A sector with free market entry results in a nonprofit sector with more, smaller nonprofits, but such a sector may have the capacity to serve more people because it has more total sector financial resources. Conversely, a sector with higher barriers to entry would translate to a sector with fewer, larger nonprofits with less overall capacity due to fewer sector financial resources.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

Network meta-analysis (NMA) of pembrolizumab for first-line (1L) treatment of recurrent/metastatic (R/M) head and neck squamous cell carcinoma (HNSCC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18012-e18012
Author(s):  
Karthik Ramakrishnan ◽  
Ali Mojebi ◽  
Dieter Ayers ◽  
Diana Romana Chirovsky ◽  
Rebekah Borse ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Fixed Effects ◽  
Goodness Of Fit ◽  
Proportional Hazards ◽  
Treatment Options ◽  
Deviance Information Criterion ◽  
Progression Free Survival ◽  
Survival Models ◽  
Base Case ◽  
Eligibility Criteria

e18012 Background: In the KEYNOTE-048 trial, pembrolizumab as monotherapy (P) and in combination with platinum+5FU chemotherapy (P+C) versus cetuximab+platinum+5FU (EXTREME regimen) significantly improved overall survival (OS) in the combined positive score (CPS) ≥1 (hazard ratio: 0.74; 95% confidence interval: 0.61-0.90) and total (0.72; 0.60-0.87) R/M HNSCC populations, respectively, and was approved by the FDA in these patient populations. While the EXTREME regimen is considered standard of care in 1L R/M HNSCC, other systemic treatment options including cetuximab+platinum+docetaxel (TPEx regimen), platinum+paclitaxel/taxane (Pt+T), and platinum+5FU (Pt+F) are also commonly used. Due to lack of head-to-head comparisons with pembrolizumab, an NMA was conducted to estimate the comparative efficacy of P and P+C versus these interventions in 1L R/M HNSCC. Methods: A systematic literature review (SLR) was conducted on November 13, 2019 to identify randomized controlled trials for the relavant interventions. Data were extracted for the OS and progression-free survival (PFS) outcomes. NMA analyses were conducted for the total population and for the CPS ≥1 and CPS ≥20 subgroups in a Bayesian framework using proportional hazards (base case) and time-varying (sensitivity analysis) treatment-effect models. The deviance information criterion was used to compare the goodness-of-fit of the alternative survival models. Results: The SLR identified 28 trials, of which six trials matched the trial eligibility criteria of KEYNOTE-048 and were included in the NMA. Results from the fixed-effects NMA for P and P+C are summarized in table below for the FDA indicated population. Improvement in OS was noted for P and P+C versus EXTREME, Pt+T, and Pt+F, and a trend in improved OS versus TPEx was observed. The sensitivity analysis showed improved OS over time across all comparisons. PFS was improved with P and P+C versus Pt+F and comparable versus other interventions. These results were generally consistent for P and P+C in the CPS (CPS ≥1 or CPS ≥20) patient subgroups. Additionally, NMA results versus EXTREME were consistent with the KEYNOTE-048 trial results. Conclusions: Pembrolizumab (P or P+C), showed improved OS and comparable PFS outcomes versus alternative 1L R/M HNSCC interventions, consistent with the efficacy results versus EXTREME observed in the KEYNOTE-048 trial. [Table: see text]

scholarly journals Asymmetric Fixed-effects Models for Panel Data

2019 ◽  
Vol 5 ◽  
pp. 237802311982644 ◽  
Author(s):  
Paul D. Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

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