Government Funding and Private Donations: Crowding-in Versus Crowding-out in the Context of a Big Data Field Experiment

Author(s):  
Sebastian Jilke
2018 ◽  
Vol 29 (4) ◽  
pp. 627-639 ◽  
Author(s):  
Sebastian Jilke ◽  
Jiahuan Lu ◽  
Chengxin Xu ◽  
Shugo Shinohara

Abstract In this article, we introduce and showcase how social media can be used to implement experiments in public administration research. To do so, we pre-registered a placebo-controlled field experiment and implemented it on the social media platform Facebook. The purpose of the experiment was to examine whether government funding to nonprofit organizations has an effect on charitable donations. Theories on the interaction between government funding and charitable donations stipulate that government funding of nonprofit organizations either decreases (crowding-out), or increases (crowding-in) private donations. To test these competing theoretical predictions, we used Facebook’s advertisement facilities and implemented an online field experiment among 296,121 Facebook users nested in 600 clusters. Through the process of cluster-randomization, groups of Facebook users were randomly assigned to different nonprofit donation solicitation ads, experimentally manipulating information cues of nonprofit funding. Contrary to theoretical predictions, we find that government funding does not seem to matter; providing information about government support to nonprofit organizations neither increases nor decreases people’s propensity to donate. We discuss the implications of our empirical application, as well as the merits of using social media to conduct experiments in public administration more generally. Finally, we outline a research agenda of how social media can be used to implement public administration experiments.


2016 ◽  
Vol 27 (3) ◽  
pp. S147
Author(s):  
A. Eifler ◽  
R. Shah ◽  
G. Hwang ◽  
W. Hwang ◽  
V. Arendt ◽  
...  
Keyword(s):  
Big Data ◽  

2021 ◽  
Vol 27 (11) ◽  
pp. 1203-1221
Author(s):  
Amal Rekik ◽  
Salma Jamoussi

Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustered instantly. To evaluate the performance of our method, we have conducted several experiments using the Sanders, HCR and Terr-Attacks datasets.


2021 ◽  
Author(s):  
Sara Molly Pollard

Ms. Penny Powers, a covert British Intelligence Officer during most of the twentieth century and (perhaps) memorialized as Miss. Moneypenny in the James Bond film series, was one of the most unrecognized saviors of children in danger in modern world history. Ms. Powers covertly organized and ran the Kindertransport and Operation Pedro Pan, two shining examples of the British intelligence service's efforts to save thousands of children from danger. Ms. Powers used the same repeatable model twice to save children in danger. Specifically, she helped save 10,000 Jewish children in the Kindertransport and 14,000 Cuban children in Operation Pedro Pan by transporting the children to a safe location, organizing temporary care for the children, planning to reunite the children with their parents when the danger had passed, and using private donations instead of government funding to help the plan appeal to the host countries. In 2016, US Representative Mike Honda proposed to replicate her model to help save children in danger in the Syrian civil war. Now, 25 years after her death, it is high time for Ms. Powers to be recognized for helping save 24,000 children.


2017 ◽  
Vol 15 (2) ◽  
pp. 283-301 ◽  
Author(s):  
Marie Hladká ◽  
Vladimír Hyánek

Government subsidies to the non-profit sector are a significant source of income for non-profit organisations. One significant impact of these subsidies is on the changing scope of private giving. The objective of this paper is to use a regression model to test whether government funding in the Czech Republic encourages private gifts and large amounts of government funding discourages gifts. However, rather than focusing on aggregate data sources, this study examines how these impacts vary among regions and sub-sectors. These models help explain why studies conducted in the past frequently differed and were inconsistent in their findings.


2021 ◽  
Vol 99 ◽  
pp. 103007
Author(s):  
Yuquan Xu ◽  
Yuewen Liu ◽  
Xiangyu Chang ◽  
Wei Huang

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