57 research outputs found
Search for Rayleigh scattering in the atmosphere of GJ1214b
We investigate the atmosphere of GJ1214b, a transiting super-Earth planet
with a low mean density, by measuring its transit depth as a function of
wavelength in the blue optical portion of the spectrum. It is thought that this
planet is either a mini-Neptune, consisting of a rocky core with a thick,
hydrogen-rich atmosphere, or a planet with a composition dominated by water.
Most observations favor a water-dominated atmosphere with a small scale-height,
however, some observations indicate that GJ1214b could have an extended
atmosphere with a cloud layer muting the molecular features. In an atmosphere
with a large scale-height, Rayleigh scattering at blue wavelengths is likely to
cause a measurable increase in the apparent size of the planet towards the
blue. We observed the transit of GJ1214b in the B-band with the FOcal Reducing
Spectrograph (FORS) at the Very Large Telescope (VLT) and in the g-band with
both ACAM on the William Hershel Telescope (WHT) and the Wide Field Camera
(WFC) at the Isaac Newton Telescope (INT). We find a planet-to-star radius
ratio in the B-band of 0.1162+/-0.0017, and in the g-band 0.1180+/-0.0009 and
0.1174+/-0.0017 for the WHT & INT observations respectively. These optical data
do not show significant deviations from previous measurements at longer
wavelengths. In fact, a flat transmission spectrum across all wavelengths best
describes the combined observations. When atmospheric models are considered a
small scale-height water-dominated model fits the data best.Comment: Accepted for publication in Ap
The Effects of NR2 Subunit-Dependent NMDA Receptor Kinetics on Synaptic Transmission and CaMKII Activation
N-Methyl-d-aspartic acid (NMDA) receptors are widely expressed in the brain and are critical for many forms of synaptic plasticity. Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development; in the forebrain, the NR2B receptor is dominant early in development, and later both NR2A and NR2B are expressed. In heterologous expression systems, NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors. However, conflicting data, showing similar open probabilities, exist for receptors expressed in neurons. Similarly, studies of synaptic plasticity have produced divergent results, with some showing that only NR2A-containing receptors can drive long-term potentiation and others showing that either subtype is capable of driving potentiation. In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse, we constructed a Monte Carlo model of glutamate release, diffusion, and binding to NMDA receptors and of receptor opening and closing as well as a model of the activation of calcium-calmodulin kinase II, an enzyme critical for induction of synaptic plasticity, by NMDA receptor-mediated calcium influx. Our results suggest that the conflicting data concerning receptor open probabilities can be resolved, with NR2A- and NR2B-containing receptors having very different opening probabilities. They also support the conclusion that receptors containing either subtype can drive long-term potentiation. We also are able to estimate the number of functional receptors at a synapse from experimental data. Finally, in our models, the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not. These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function
Stemming the Tide of Antibiotic Resistance (STAR): A protocol for a trial of a complex intervention addressing the 'why' and 'how' of appropriate antibiotic prescribing in general practice
BACKGROUND: After some years of a downward trend, antibiotic prescribing rates in the community have tended to level out in many countries. There is also wide variation in antibiotic prescribing between general practices, and between countries. There are still considerable further gains that could be made in reducing inappropriate antibiotic prescribing, but complex interventions are required. Studies to date have generally evaluated the effect of interventions on antibiotic prescribing in a single consultation and pragmatic evaluations that assess maintenance of new skills are rare. This paper describes the protocol for a pragmatic, randomized evaluation of a complex intervention aimed at reducing antibiotic prescribing by primary care clinicians. METHODS AND DESIGN: We developed a Social Learning Theory based, blended learning program (on-line learning, a practice based seminar, and context bound learning) called the STAR Educational Program. The 'why of change' is addressed by providing clinicians in general practice with information on antibiotic resistance in urine samples submitted by their practice and their antibiotic prescribing data, and facilitating a practice-based seminar on the implications of this data. The 'how of change' is addressed through context-bound communication skills training and information on antibiotic indication and choice. This intervention will be evaluated in a trial involving 60 general practices, with general practice as the unit of randomization (clinicians from each practice to either receive the STAR Educational Program or not) and analysis. The primary outcome will be the number of antibiotic items dispensed over one year. An economic and process evaluation will also be conducted. DISCUSSION: This trial will be the first to evaluate the effectiveness of this type of theory-based, blended learning intervention aimed at reducing antibiotic prescribing by primary care clinicians. Novel aspects include feedback of practice level data on antimicrobial resistance and prescribing, use of principles from motivational interviewing, training in enhanced communication skills that incorporates context-bound experience and reflection, and using antibiotic dispensing over one year (as opposed to antibiotic prescribing in a single consultation) as the main outcome
Optical Control of Metabotropic Glutamate Receptors
G-protein coupled receptors (GPCRs), the largest family of membrane signaling proteins, respond to neurotransmitters, hormones and small environmental molecules. The neuronal function of many GPCRs has been difficult to resolve because of an inability to gate them with subtype-specificity, spatial precision, speed and reversibility. To address this, we developed an approach for opto-chemical engineering native GPCRs. We applied this to the metabotropic glutamate receptors (mGluRs) to generate light-agonized and light-antagonized “LimGluRs”. The light-agonized “LimGluR2”, on which we focused, is fast, bistable, and supports multiple rounds of on/off switching. Light gates two of the primary neuronal functions of mGluR2: suppression of excitability and inhibition of neurotransmitter release. The light-antagonized “LimGluR2block” can be used to manipulate negative feedback of synaptically released glutamate on transmitter release. We generalize the optical control to two additional family members: mGluR3 and 6. The system works in rodent brain slice and in zebrafish in vivo, where we find that mGluR2 modulates the threshold for escape behavior. These light-gated mGluRs pave the way for determining the roles of mGluRs in synaptic plasticity, memory and disease
Physiological Correlates of Volunteering
We review research on physiological correlates of volunteering, a neglected but promising research field. Some of these correlates seem to be causal factors influencing volunteering. Volunteers tend to have better physical health, both self-reported and expert-assessed, better mental health, and perform better on cognitive tasks. Research thus far has rarely examined neurological, neurochemical, hormonal, and genetic correlates of volunteering to any significant extent, especially controlling for other factors as potential confounds. Evolutionary theory and behavioral genetic research suggest the importance of such physiological factors in humans. Basically, many aspects of social relationships and social activities have effects on health (e.g., Newman and Roberts 2013; Uchino 2004), as the widely used biopsychosocial (BPS) model suggests (Institute of Medicine 2001). Studies of formal volunteering (FV), charitable giving, and altruistic behavior suggest that physiological characteristics are related to volunteering, including specific genes (such as oxytocin receptor [OXTR] genes, Arginine vasopressin receptor [AVPR] genes, dopamine D4 receptor [DRD4] genes, and 5-HTTLPR). We recommend that future research on physiological factors be extended to non-Western populations, focusing specifically on volunteering, and differentiating between different forms and types of volunteering and civic participation
What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach
Ambiguity surrounding the effect of external engagement on academic research has raised questions about what motivates researchers to collaborate with third parties. We argue that what matters for society is research that can be absorbed by users. We define openness as a willingness by researchers to make research more usable by external partners by responding to external influences in their own research practices. We ask what kinds of characteristics define those researchers who are more open to creating usable knowledge. Our empirical study analyses a sample of 1583 researchers working at the Spanish Council for Scientific Research (CSIC). Results demonstrate that it is personal factors (academic identity and past experience) that determine which researchers have open behaviours. The paper concludes that policies to encourage external engagement should focus on experiences which legitimate and validate knowledge produced through user encounters, both at the academic formation career stage as well as through providing ongoing opportunities to engage with third parties.The data used for this study comes from the IMPACTO project funded by the Spanish Council for Scientific Research - CSIC (Ref. 200410E639). The work also benefited from a mobility grant awarded by Eu-Spri Forum to Julia Olmos Penuela & Paul Benneworth for her visiting research to the Center of Higher Education Policy Studies. Finally, Julia Olmos Penuela also benefited from a post-doctoral grant funded by the Generalitat Valenciana (APOSTD-2014-A-006).Olmos-Peñuela, J.; Benneworth, P.; Castro-Martínez, E. (2015). What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach. Minerva. 53(4):381-410. https://doi.org/10.1007/s11024-015-9283-4S381410534Abreu, Maria, Vadim Grinevich, Alan Hughes, and Michael Kitson. 2009. Knowledge exchange between academics and the business, public and third sectors. Cambridge: Centre for Business Research and UK-IRC.Aghion, Philippe, Mathias Dewatripont, and Jeremy C. Stein. 2008. Academic freedom, private-sector focus, and the process of innovation. RAND Journal of Economics 39: 617–635.Ajzen, Icek. 2001. Nature and operation of attitudes. Annual Review of Psychology 52(1): 27–58.Alrøe, Hugo Fjelsted, and Erik Steen Kristensen. 2002. Towards a systemic research methodology in agriculture: Rethinking the role of values in science. Agriculture and Human Values 19(1): 3–23.Audretsch, David B., Werner Bönte, and Stefan Krabel. 2010. Why do scientists in public research institutions cooperate with private firms. In DRUID Working Paper, 10–27.Baldini, Nicola, Rosa Grimaldi, and Maurizio Sobrero. 2007. To patent or not to patent? A survey of Italian inventors on motivations, incentives, and obstacles to university patenting. Scientometrics 70(2): 333–354.Bandura, Albert. 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.Barnett, R. 2009. Knowing and becoming in the higher education curriculum. Studies in Higher Education 34(4): 429–440.Becher, Tony. 1994. The significance of disciplinary differences. Studies in Higher Education 19(2): 151–161.Becher, Tony, and Paul Trowler. 2001. Academic tribes and territories: Intellectual enquiry and the culture of disciplines. McGraw-Hill International.Bekkers, Rudi, and Isabel Maria Bodas Freitas. 2008. Analysing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy 37(10): 1837–1853.Belderbos, René, Martin Carree, Bert Diederen, Boris Lokshin, and Reinhilde Veugelers. 2004. Heterogeneity in R&D cooperation strategies. International Journal of Industrial Organization 22(8): 1237–1263.Benner, Mats, and Ulf Sandström. 2000. Institutionalizing the triple helix: Research funding and norms in the academic system. Research Policy 29(2): 291–301.Bercovitz, Janet, and Maryann Feldman. 2008. Academic entrepreneurs: Organizational change at the individual level. Organization Science 19(1): 69–89.Berman, Elizabeth Popp. 2011. Creating the market university: How academic science became an economic engine. Princeton University Press.Bleiklie, Ivar, and Roar Høstaker. 2004. Modernizing research training-education and science policy between profession, discipline and academic institution. Higher Education Policy 17(2): 221–236.Bozeman, Barry, Daniel Fay, and Catherine P. Slade. 2013. Research collaboration in universities and academic entrepreneurship: The-state-of-the-art. The Journal of Technology Transfer 38(1): 1–67.Collini, Stefan. 2009. Impact on humanities: Researchers must take a stand now or be judged and rewarded as salesmen. The Times Literary Supplement 5563: 18–19.D’Este, Pablo, and Markus Perkmann. 2011. Why do academics engage with industry? The entrepreneurial university and individual motivations. The Journal of Technology Transfer 36(3): 316–339.D’Este, Pablo, Oscar Llopis, and Alfredo Yegros. 2013. Conducting pro-social research: Cognitive diversity, research excellence and awareness about the social impact of research: INGENIO (CSIC-UPV) Working Paper Series.Deem, Rosemary, and Lisa Lucas. 2007. Research and teaching cultures in two contrasting UK policy contexts: Academic life in education departments in five English and Scottish universities. Higher Education 54(1): 115–133.DiMaggio, Paul J., and Walter W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review 48(2): 147–160.Downing, David B. 2005. The knowledge contract: Politics and paradigms in the academic workplace. Lincoln: Nebraska University of Nebraska Press.Donovan, Claire. 2007. The qualitative future of research evaluation. Science and Public Policy 34(8): 585–597.Durning, Bridget. 2004. Planning academics and planning practitioners: Two tribes or a community of practice? Planning Practice and Research 19(4): 435–446.Edquist, Charles. 1997. System of innovation approaches: Their emergence and characteristics. In Systems of innovation: Technologies, institutions and organizations, ed. C. Edquist, 1–35. London: Pinter.Etzkowitz, Henry, and Loet Leydesdorff. 2000. The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy 29(2): 109–123.Fromhold-Eisebith, Martina, Claudia Werker, and Marcel Vojnic. 2014. Tracing the social dimension in innovation networks. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Geuna, Aldo, and Alessandro Muscio. 2009. The governance of university knowledge transfer: A critical review of the literature. Minerva 47(1): 93–114.Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and Martin Trow. 1994. The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.Gläser, Jochen. 2012. How does Governance change research content? On the possibility of a sociological middle-range theory linking science policy studies to the sociology of scientific knowledge. Technical University Berlin. Technology Studies Working Papers. http://www.ts.tu-berlin.de/fileadmin/fg226/TUTS/TUTS-WP-1-2012.pdf . Accessed 16 Feb 2015.Goethner, Maximilian, Martin Obschonka, Rainer K. Silbereisen, and Uwe Cantner. 2012. Scientists’ transition to academic entrepreneurship: Economic and psychological determinants. Journal of Economic Psychology 33(3): 628–641.Gulbrandsen, Magnus, and Jens-Christian Smeby. 2005. Industry funding and university professors’ research performance. Research Policy 34(6): 932–950.Haeussler, Carolin, and Jeannette Colyvas. 2011. Breaking the ivory tower: Academic entrepreneurship in the life sciences in UK and Germany. Research Policy 40(1): 41–54.Hessels, Laurens K., Harro van Lente, John Grin, and Ruud E.H.M. Smits. 2011. Changing struggles for relevance in eight fields of natural science. Industry and Higher Education 25(5): 347–357.Hessels, Laurens K., and Harro Van Lente. 2008. Re-thinking new knowledge production: A literature review and a research agenda. Research Policy 37(4): 740–760.Hoye, Kate, and Fred Pries. 2009. ‘Repeat commercializers’, the ‘habitual entrepreneurs’ of university–industry technology transfer. Technovation 29(10): 682–689.Jacobson, Nora, Dale Butterill, and Paula Goering. 2004. Organizational factors that influence university-based researchers’ engagement in knowledge transfer activities. Science Communication 25(3): 246–259.Jain, Sanjay, Gerard George, and Mark Maltarich. 2009. Academics or entrepreneurs? Investigating role identity modification of university scientists involved in commercialization activity. Research Policy 38(6): 922–935.Jasanoff, Sheila, and Sang-Hyun Kim. 2013. Sociotechnical imaginaries and national energy policies. Science as Culture 22(2): 189–196.Jensen, Pablo. 2011. A statistical picture of popularization activities and their evolutions in France. Public Understanding of Science 20(1): 26–36.Kitcher, Philip. 2001. Science, truth, and democracy. Oxford: Oxford University Press.Knorr-Cetina, Karin. 1981. The manufacture of knowledge: An essay on the constructivist and contextual nature of science. Oxford: Pergamon Press.Kronenberg, Kristin, and Marjolein Caniëls. 2014. Professional proximity in research collaborations. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Krueger, Rob, and David Gibbs. 2010. Competitive global city regions and sustainable development’: An interpretive institutionalist account in the South East of England. Environment and planning A 42: 821–837.Lam, Alice. 2011. What motivates academic scientists to engage in research commercialization: ‘Gold’, ‘ribbon’ or ‘puzzle’? Research Policy 40(10): 1354–1368.Landry, Réjean, Malek Saïhi, Nabil Amara, and Mathieu Ouimet. 2010. Evidence on how academics manage their portfolio of knowledge transfer activities. Research Policy 39(10): 1387–1403.Lee, Alison, and David Boud. 2003. Writing groups, change and academic identity: Research development as local practice. Studies in Higher Education 28(2): 187–200.Lee, Yong S. 1996. ‘Technology transfer’ and the research university: A search for the boundaries of university–industry collaboration. Research Policy 25(6): 843–863.Lee, Yong S. 2000. The sustainability of university–industry research collaboration: An empirical assessment. The Journal of Technology Transfer 25(2): 111–133.Leisyte, Liudvika, Jürgen Enders, and Harry De Boer. 2008. The freedom to set research agendas—illusion and reality of the research units in the Dutch Universities. Higher Education Policy 21(3): 377–391.Louis, Karen Seashore, David Blumenthal, Michael E. Gluck, and Michael A. Stoto. 1989. Entrepreneurs in academe: An exploration of behaviors among life scientists. Administrative Science Quarterly 34(1): 110–131.Lowe, Philip, Jeremy Phillipson, and Katy Wilkinson. 2013. Why social scientists should engage with natural scientists. Contemporary Social Science 8(3): 207–222.Martín-Sempere, María José, Belén Garzón-García, and Jesús Rey-Rocha. 2008. Scientists’ motivation to communicate science and technology to the public: Surveying participants at the Madrid Science Fair. Public Understanding of Science 17(3): 349–367.Martin, Ben. 2003. The changing social contract for science and the evolution of the university. In Science and innovation: Rethinking the rationales for funding and governance, eds. A. Geuna, A.J. Salter, and W.E. Steinmueller, 7–29. Cheltenhan: Edward Elgar.Merton, Robert K. 1973. The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.Miller, Thaddeus R., and Mark W. Neff. 2013. De-facto science policy in the making: how scientists shape science policy and why it matters (or, why STS and STP scholars should socialize). Minerva 51(3): 295–315.Muthén, Bengt O. 1998–2004. Mplus Technical Appendices. Muthén & Muthén. Los Angeles, CA.: Muthén & Muthén.Nedeva, Maria. 2013. Between the global and the national: Organising European science. Research Policy 42(1): 220–230.Neff, Mark William. 2014. Research prioritization and the potential pitfall of path dependencies in coral reef science. Minerva 52(2): 213–235.Nelson, Richard R. 2001. Observations on the post-Bayh-Dole rise of patenting at American universities. The Journal of Technology Transfer 26(1): 13–19.Nowotny, Helga, Peter Scott, and Michael Gibbons. 2001. Re-thinking science: Knowledge and the public in an age of uncertainty. Cambridge: Polity Press.Olmos-Peñuela, Julia, Paul Benneworth, and Elena Castro-Martínez. 2014a. Are ‘STEM from Mars and SSH from Venus’? Challenging disciplinary stereotypes of research’s social value. Science and Public Policy 41: 384–400.Olmos-Peñuela, Julia, Elena Castro-Martínez, and Manuel Fernández-Esquinas. 2014b. Diferencias entre áreas científicas en las prácticas de divulgación de la investigación: un estudio empírico en el CSIC. Revista Española de Documentación Científica. doi: 10.3989/redc.2014.2.1096 .Ouimet, Mathieu, Nabil Amara, Réjean Landry, and John Lavis. 2007. Direct interactions medical school faculty members have with professionals and managers working in public and private sector organizations: A cross-sectional study. Scientometrics 72(2): 307–323.Perkmann, Markus, Valentina Tartari, Maureen McKelvey, Erkko Autio, Anders Brostrom, Pablo D’Este, Riccardo Fini, et al. 2013. Academic engagement and commercialisation: A review of the literature on university-industry relations. Research Policy 42(2): 423–442.Philpott, Kevin, Lawrence Dooley, Caroline O’Reilly, and Gary Lupton. 2011. The entrepreneurial university: Examining the underlying academic tensions. Technovation 31(4): 161–170.Rutten, Roel, and Frans Boekema. 2012. From learning region to learning in a socio-spatial context. Regional Studies 46(8): 981–992.Sarewitz, Daniel, and Roger A. Pielke. 2007. The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science & Policy 10(1): 5–16.Sauermann, Henry, and Paula Stephan. 2013. Conflicting logics? A multidimensional view of industrial and academic science. Organization Science 24(3): 889–909.Schein, Edgar H. 1985. Organizational culture and leadership: A dynamic view. San Francisco, CA: Jossey-Bass.Shane, Scott. 2000. Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science 11(4): 448–469.Spaapen, Jack, and Leonie van Drooge. 2011. Introducing ‘productive interactions’ in social impact assessment. Research Evaluation 20(3): 211–218.Stokes, Donald E. 1997. Pasteur’s quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Tartari, Valentina, and Stefano Breschi. 2012. Set them free: scientists’ evaluations of the benefits and costs of university–industry research collaboration. Industrial and Corporate Change 21(5): 1117–1147.Tinker, Tony, and Rob Gray. 2003. Beyond a critique of pure reason: From policy to politics to praxis in environmental and social research. Accounting, Auditing & Accountability Journal 16(5): 727–761.van Rijnsoever, Frank J., Laurens K. Hessels, and Rens L.J. Vandeberg. 2008. A resource-based view on the interactions of university researchers. Research Policy 37(8): 1255–1266.Venkataraman, Sankaran. 1997. The distinctive domain of entrepreneurship research: An editor’s perspective. Advances in Entrepreneurship, Firm Emergence, and Growth 3: 119–138.Verspagen, Bart. 2006. University research, intellectual property rights and European innovation systems. Journal of Economic Surveys 20(4): 607–632.Villanueva-Felez, Africa, Jordi Molas-Gallart, and Alejandro Escribá-Esteve. 2013. Measuring personal networks and their relationship with scientific production. Minerva 51(4): 465–483.Watermeyer, Richard. 2015. Lost in the ‘third space’: the impact of public engagement in higher education on academic identity, research practice and career progression. European Journal of Higher Education (online first, doi: 10.1080/21568235.2015.1044546 ).Weingart, Peter. 2009. Editorial for Issue 47/3. Minerva 47(3): 237–239.Ziman, John. 1996. ‘Postacademic science’: Constructing knowledge with networks and norms. Science Studies 1: 67–80.Zomer, Arend H., Ben W.A. Jongbloed, and Jürgen Enders. 2010. Do spin-offs make the academics’ heads spin? The impacts of spin-off companies on their parent research organisation. Minerva 48(3): 331–353
Recommended from our members
Structure and flexibility in cortical representations of odour space
The cortex organizes sensory information to enable discrimination and generalization1-4. As systematic representations of chemical odour space have not yet been described in the olfactory cortex, it remains unclear how odour relationships are encoded to place chemically distinct but similar odours, such as lemon and orange, into perceptual categories, such as citrus5-7. Here, by combining chemoinformatics and multiphoton imaging in the mouse, we show that both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odour relationships through correlated patterns of activity. However, cortical odour codes differ from those in the bulb: cortex more strongly clusters together representations for related odours, selectively rewrites pairwise odour relationships, and better matches odour perception. The bulb-to-cortex transformation depends on the associative network originating within the piriform cortex, and can be reshaped by passive odour experience. Thus, cortex actively builds a structured representation of chemical odour space that highlights odour relationships; this representation is similar across individuals but remains plastic, suggesting a means through which the olfactory system can assign related odour cues to common and yet personalized percepts
Stochastic Income and Conditional Generosity
We study how other-regarding behavior extends to environments with uncertain income and conditional commitments. Should fundraisers ask a banker to donate "if he earns a bonus" or wait and ask after the bonus is known? Standard EU theory predicts these are equivalent; loss-aversion and signaling models both predict a larger commitment before the bonus is known; theories of affect predict the reverse. In field and lab experiments, we allow people to donate from lottery winnings, varying whether they decide before or after learning the lottery's outcome. Males are more generous when making conditional donations before knowing the outcome, while females' donations are unaffected. Males also commit more in treatments where income is certain but the donation's collection is uncertain. This supports a signaling explanation: it is cheaper to commit to donate before the uncertainty is unresolved, thus a larger donation is required to maintain a positive image. This has implications for experimental methodology, for fundraisers, and for our understanding of pro-social behavior
Dictator Games: A Meta Study
Over the last 25 years, more than a hundred dictator game experiments have been published. This meta study summarizes the evidence. Exploiting the fact that most experiments had to fix parameters they did not intend to test, the meta study explores a rich set of control variables for multivariate analysis. It shows that Tobit models (assuming that dictators would even want to take money) and hurdle models (assuming that the decision to give a positive amount is separate from the choice of amount, conditional on giving) outperform mere meta-regression and OLS
- …