A model of the distribution of income is derived from a two market general equilibrium model consisting of a goods market and a labor market. The dynamics of income distributional changes as well as their stationary counterparts are also derived.
In: Phillips , L T , Tepper , S J , Goya-Tocchetto , D , Davidai , S , Ordabayeva , N , Mirza , M U , Szaszi , B , Day , M V , Hauser , O P & Jachimowicz , J M 2020 ' Inequality in People's Minds ' PsyArXiv Preprints . https://doi.org/10.31234/osf.io/vawh9
The extent of inequality that people perceive in the world is often a better predictor of individual and societal outcomes than the level of inequality that actually exists. As such, scholars from across the social sciences, including economics, sociology, psychology, and political science, have recently worked to understand individuals' (mis)perceptions of inequality. Unfortunately, many researchers treat the process underlying such perceptions as a black box, focusing predominantly on lay people's numeric estimates of inequality, and paying less attention to how people come to form these perceptions or what these perceptions mean to participants. In the current review, we draw on research in perception, cognition, and developmental and social psychology, to introduce a novel comprehensive framework for understanding individuals' perceptions of inequality. We argue that subjective perceptions of inequality should be viewed as a process that unfolds across five interlinked and iterative stages. To form perceptions of the scope of inequality in society, people need to (1) have access to inequality cues in the world, (2) attend to these cues, (3) comprehend these cues, (4) process these cues (often succumbing to motivational biases), and (5) summarize these cues into a meaningful representation of inequality. Our framework highlights when and why lay people may misperceive the scope of inequality in society and provides a roadmap for research to examine how the processes in people's minds affect the outcomes researchers are ultimately interested in.
In: Jachimowicz , J M , Davidai , S , Goya-Tocchetto , D , Szaszi , B , Day , M V , Tepper , S J , Phillips , L T , Mirza , M U , Ordabayeva , N & Hauser , O P 2020 ' Inequality in Researchers' Minds: Four Guiding Questions for Studying Subjective Perceptions of Economic Inequality ' PsyArXiv Preprints . https://doi.org/10.31234/osf.io/gn2z5
The extent of inequality that people perceive in the world is often a stronger predictor of individual and societal outcomes than the level of inequality that actually exists. It is therefore imperative for researchers to theoretically conceptualize and empirically operationalize perceived inequality in a coherent and consistent manner. However, the lack of consensus on what constitutes perceived inequality can lead researchers to use the same words to study different phenomena. What seem like minor methodological decisions made in the study of inequality can substantially influence the outcomes and conclusions that researchers dra from their work. In this review, we draw on a wide range of interdisciplinary work, including from social and cognitive psychology, economics, political science, and sociology, to unpack the assumptions researchers often make. We develop the four questions framework which illustrates the important theoretical and empirical decisions researchers are recommended to address when studying perceived inequality: (1) What kind of inequality? (2) What level of analysis? (3) What part of the distribution? and (4) What comparison group? We posit that this framework provides the conceptual clarity necessary for understanding when, how, and why perceptions of inequality affect individuals and societies.
Defect-based quantum systems in wide bandgap semiconductors are strong candidates for scalable quantum-information technologies. However, these systems are often complicated by charge-state instabilities and interference by phonons, which can diminish spin-initialization fidelities and limit room-temperature operation. Here, we identify a pathway around these drawbacks by showing that an engineered quantum well can stabilize the charge state of a qubit. Using density-functional theory and experimental synchrotron X-ray diffraction studies, we construct a model for previously unattributed point defect centers in silicon carbide as a near-stacking fault axial divacancy and show how this model explains these defects robustness against photoionization and room temperature stability. These results provide a materials-based solution to the optical instability of color centers in semiconductors, paving the way for the development of robust single-photon sources and spin qubits. ; Funding Agencies|MTA Premium Postdoctoral Research Program; Knut and Alice Wallenberg Foundation through WBSQD2 project [2018.0071]; Swedish Government Strategic Research Areas in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-LiU) [2009-00971]; Swedish e-Science Centre (SeRC); Swedish Research CouncilSwedish Research Council [VR 2016-04068]; Carl-Trygger Stiftelse for Vetenskaplig Forskning [CTS 15:339]; Ministry of Education and Science of the Russian FederationMinistry of Education and Science, Russian Federation [K2-2019-001, 211]; Hungarian NKFIH grants of the National Excellence Program of Quantumcoherent materials project [KKP129866]; EU QuantERA Nanospin project [127902]; EU H2020 Quantum Technology Flagship project ASTERIQS [820394]; NVKP project [NVKP_16-1-2016-0043]; National Quantum Technology Program [2017-1.2.1-NKP-2017-00001]; U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering DivisionUnited States Department of Energy (DOE); DOE Office of ScienceUnited States Department of Energy (DOE) [DE-AC02-06CH11357]; Center for Nanoscale Materials, a U.S. Department of Energy Office of Science User FacilityUnited States Department of Energy (DOE) [DE-AC02-06CH11357]; DOE, Office of Basic Energy SciencesUnited States Department of Energy (DOE)
Frontmatter -- CONTENTS -- ACKNOWLEDGMENTS -- 1 INTRODUCTION -- 2 ATLANTA: IF DIXIE WERE ATLANTA -- 3 MIAMI: THE ETHNIC CAULDRON -- 4 NEW ORLEANS: SUNBELT IN THE SWAMP -- 5 TAMPA: FROM HELL HOLE TO THE GOOD LIFE -- 6 DALLAS-FORT: WORTH MARKETING THE METROPLEX -- 7 HOUSTON: THE GOLDEN BUCKLE OF THE SUNBELT -- 8 OKLAHOMA: CITY BOOMING SOONER -- 9 SAN ANTONIO: THE VICISSITUDES OF BOOSTERISM -- 10 ALBUQUERQUE: CITY AT A CROSSROADS -- 11 LOS ANGELES: IMPROBABLE LOS ANGELES -- 12 PHOENIX THE DESERT METROPOLIS -- 13 SAN DIEGO: THE ANTI-CITY -- CONTRIBUTORS
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An outbreak of the viral zoonotic disease Rift Valley fever in East Africa in 2006/07 left more than 300 people dead and caused economic losses in Kenya alone estimated to exceed USD 30 million. Participatory studies undertaken shortly after the outbreaks abated enabled valuable lessons to be learned; if applied these lessons could significantly reduce the impact of future outbreaks. The lessons included the desirability of complementing and integrating international early warning systems with information from local people on the ground; the need for government-approved contingency plans and emergency funding arrangement to be in place; the need to initiate responses before the first human cases are detected; the difficulties of mounting effective livestock vaccination campaigns; and the need for clear, consistent and authoritative public health messages to be developed and tested well before an outbreak occurs. To address these issues, FAO and ILRI recently led a multi-stakeholder initiative to develop a decision support tool for the prevention and control of RVF in the Greater Horn of Africa. The tool is targeted at directors of veterinary services. The RVF outbreak is divided into a sequence made up of 12 key events: for each event a set of actions are listed that are considered appropriate at that stage. The tool is intended to facilitate timely, evidence-based decision-making that will enable RVF outbreaks to be better prevented or contained, thereby reducing the scale of impacts on lives and livelihoods as well as local, national and regional economies. In September 2008, FAO EMPRES warned that RVF could occur again in East Africa later that year. This early warning and the veterinary department's reaction in Kenya, highlight two encouraging changes. First, the early warning was issued in September, two months earlier than in 2006. Second, the veterinary department immediately established an interdisciplinary, multistakeholder technical coordinating committee. Actions taken, informed by the new decision support tool for the prevention and control of RVF, included drafting of protocols for vaccination, livestock quarantine and vector control. The veterinary department had a limited stock of vaccine in hand, which it targeted to what it considered the highest risk areas. However, taking into consideration production and shipment delays, should these limited vaccination campaigns have had to be expanded it is unlikely that a sufficient level of population immunity from mass vaccination in high risk areas could have been achieved prior to mid-November, the time when suspected livestock cases were occurring in North Eastern Province in 2006. ILRI and partners are engaging in further research to determine what impacts the RVF Decision Support Tool had on Kenya's 2008 response.
An outbreak of the viral zoonotic disease Rift Valley fever in East Africa in 2006/07 left more than 300 people dead and caused economic losses in Kenya alone estimated to exceed USD 30 million. Participatory studies undertaken shortly after the outbreaks abated enabled valuable lessons to be learned; if applied these lessons could significantly reduce the impact of future outbreaks. The lessons included the desirability of complementing and integrating international early warning systems with information from local people on the ground; the need for government-approved contingency plans and emergency funding arrangement to be in place; the need to initiate responses before the first human cases are detected; the difficulties of mounting effective livestock vaccination campaigns; and the need for clear, consistent and authoritative public health messages to be developed and tested well before an outbreak occurs. To address these issues, FAO and ILRI recently led a multi-stakeholder initiative to develop a decision support tool for the prevention and control of RVF in the Greater Horn of Africa. The tool is targeted at directors of veterinary services. The RVF outbreak is divided into a sequence made up of 12 key events: for each event a set of actions are listed that are considered appropriate at that stage. The tool is intended to facilitate timely, evidence-based decision-making that will enable RVF outbreaks to be better prevented or contained, thereby reducing the scale of impacts on lives and livelihoods as well as local, national and regional economies. In September 2008, FAO EMPRES warned that RVF could occur again in East Africa later that year. This early warning and the veterinary department's reaction in Kenya, highlight two encouraging changes. First, the early warning was issued in September, two months earlier than in 2006. Second, the veterinary department immediately established an interdisciplinary, multistakeholder technical coordinating committee. Actions taken, informed by the new decision support tool for the prevention and control of RVF, included drafting of protocols for vaccination, livestock quarantine and vector control. The veterinary department had a limited stock of vaccine in hand, which it targeted to what it considered the highest risk areas. However, taking into consideration production and shipment delays, should these limited vaccination campaigns have had to be expanded it is unlikely that a sufficient level of population immunity from mass vaccination in high risk areas could have been achieved prior to mid-November, the time when suspected livestock cases were occurring in North Eastern Province in 2006. ILRI and partners are engaging in further research to determine what impacts the RVF Decision Support Tool had on Kenya's 2008 response.
This paper describes the data acquisition and high level trigger system of the ATLAS experiment at the Large Hadron Collider at CERN, as deployed during Run 1. Data flow as well as control, configuration and monitoring aspects are addressed. An overview of the functionality of the system and of its performance is presented and design choices are discussed. ; Funding: We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; the Royal Society and Leverhulme Trust, United Kingdom.
ANPCyT, Argentina ; YerPhI, Armenia ; ARC, Australia ; BMWFW, Austria ; FWF, Austria ; ANAS, Azerbaijan ; SSTC, Belarus ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; NSERC, Canada ; NRC, Canada ; CFI, Canada ; CERN ; CONICYT, Chile ; CAS, China ; MOST, China ; NSFC, China ; COLCIENCIAS, Colombia ; MSMT CR, Czech Republic ; MPO CR, Czech Republic ; VSC CR, Czech Republic ; DNRF, Denmark ; DNSRC, Denmark ; IN2P3-CNRS, CEA-DRF/IRFU, France ; SRNSFG, Georgia ; BMBF, Germany ; HGF, Germany ; MPG, Germany ; GSRT, Greece ; RGC, Hong Kong SAR, China ; ISF, Israel ; Benoziyo Center, Israel ; INFN, Italy ; MEXT, Japan ; JSPS, Japan ; CNRST, Morocco ; NWO, Netherlands ; RCN, Norway ; MNiSW, Poland ; NCN, Poland ; FCT, Portugal ; MNE/IFA, Romania ; MES of Russia, Russian Federation ; NRC KI, Russian Federation ; JINR ; MESTD, Serbia ; MSSR, Slovakia ; ARRS, Slovenia ; MIZS, Slovenia ; DST/NRF, South Africa ; MINECO, Spain ; SRC, Sweden ; Wallenberg Foundation, Sweden ; SERI, Switzerland ; SNSF, Switzerland ; Canton of Bern, Switzerland ; MOST, Taiwan ; TAEK, Turkey ; STFC, United Kingdom ; DOE, United States of America ; NSF, United States of America ; BCKDF, Canada ; CANARIE, Canada ; CRC, Canada ; Compute Canada, Canada ; COST, European Union ; ERC, European Union ; ERDF, European Union ; Horizon 2020, European Union ; Marie Sk lodowska-Curie Actions, European Union ; Investissements d' Avenir Labex and Idex, ANR, France ; DFG, Germany ; AvH Foundation, Germany ; Greek NSRF, Greece ; BSF-NSF, Israel ; GIF, Israel ; CERCA Programme Generalitat de Catalunya, Spain ; Royal Society, United Kingdom ; Leverhulme Trust, United Kingdom ; BMBWF (Austria) ; FWF (Austria) ; FNRS (Belgium) ; FWO (Belgium) ; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; FAPERGS (Brazil) ; MES (Bulgaria) ; CAS (China) ; MoST (China) ; NSFC (China) ; COLCIENCIAS (Colombia) ; MSES (Croatia) ; CSF (Croatia) ; RPF (Cyprus) ; SENESCYT (Ecuador) ; MoER (Estonia) ; ERC IUT (Estonia) ; ERDF (Estonia) ; Academy of Finland (Finland) ; MEC (Finland) ; HIP (Finland) ; CEA (France) ; CNRS/IN2P3 (France) ; BMBF (Germany) ; DFG (Germany) ; HGF (Germany) ; GSRT (Greece) ; NKFIA (Hungary) ; DAE (India) ; DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; MSIP (Republic of Korea) ; NRF (Republic of Korea) ; MES (Latvia) ; LAS (Lithuania) ; MOE (Malaysia) ; UM (Malaysia) ; BUAP (Mexico) ; CINVESTAV (Mexico) ; CONACYT (Mexico) ; LNS (Mexico) ; SEP (Mexico) ; UASLP-FAI (Mexico) ; MOS (Montenegro) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MSHE (Poland) ; NSC (Poland) ; FCT (Portugal) ; JINR (Dubna) ; MON (Russia) ; RosAtom (Russia) ; RAS (Russia) ; RFBR (Russia) ; NRC KI (Russia) ; MESTD (Serbia) ; SEIDI (Spain) ; CPAN (Spain) ; PCTI (Spain) ; FEDER (Spain) ; MOSTR (Sri Lanka) ; MST (Taipei) ; ThEPCenter (Thailand) ; IPST (Thailand) ; STAR (Thailand) ; NSTDA (Thailand) ; TAEK (Turkey) ; NASU (Ukraine) ; SFFR (Ukraine) ; STFC (United Kingdom ; DOE (U.S.A.) ; NSF (U.S.A.) ; Marie-Curie programme ; Horizon 2020 Grant (European Union) ; Leventis Foundation ; A.P. Sloan Foundation ; Alexander von Humboldt Foundation ; Belgian Federal Science Policy Office ; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; F.R.S.-FNRS (Belgium) ; Beijing Municipal Science & Technology Commission ; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic ; Hungarian Academy of Sciences (Hungary) ; New National Excellence Program UNKP (Hungary) ; Council of Science and Industrial Research, India ; HOMING PLUS programme of the Foundation for Polish Science ; European Union, Regional Development Fund ; Mobility Plus programme of the Ministry of Science and Higher Education ; National Science Center (Poland) ; National Priorities Research Program by Qatar National Research Fund ; Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu ; Programa Severo Ochoa del Principado de Asturias ; EU-ESF ; Greek NSRF ; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand) ; Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; Welch Foundation ; Weston Havens Foundation (U.S.A.) ; Canton of Geneva, Switzerland ; Herakleitos programme ; Thales programme ; Aristeia programme ; European Research Council (European Union) ; Horizon 2020 Grant (European Union): 675440 ; FWO (Belgium): 30820817 ; Beijing Municipal Science & Technology Commission: Z181100004218003 ; NKFIA (Hungary): 123842 ; NKFIA (Hungary): 123959 ; NKFIA (Hungary): 124845 ; NKFIA (Hungary): 124850 ; NKFIA (Hungary): 125105 ; National Science Center (Poland): Harmonia 2014/14/M/ST2/00428 ; National Science Center (Poland): Opus 2014/13/B/ST2/02543 ; National Science Center (Poland): 2014/15/B/ST2/03998 ; National Science Center (Poland): 2015/19/B/ST2/02861 ; National Science Center (Poland): Sonata-bis 2012/07/E/ST2/01406 ; Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu: MDM-2015-0509 ; Welch Foundation: C-1845 ; This paper presents the combinations of single-top-quark production cross-section measurements by the ATLAS and CMS Collaborations, using data from LHC proton-proton collisions at = 7 and 8 TeV corresponding to integrated luminosities of 1.17 to 5.1 fb(-1) at = 7 TeV and 12.2 to 20.3 fb(-1) at = 8 TeV. These combinations are performed per centre-of-mass energy and for each production mode: t-channel, tW, and s-channel. The combined t-channel cross-sections are 67.5 +/- 5.7 pb and 87.7 +/- 5.8 pb at = 7 and 8 TeV respectively. The combined tW cross-sections are 16.3 +/- 4.1 pb and 23.1 +/- 3.6 pb at = 7 and 8 TeV respectively. For the s-channel cross-section, the combination yields 4.9 +/- 1.4 pb at = 8 TeV. The square of the magnitude of the CKM matrix element V-tb multiplied by a form factor f(LV) is determined for each production mode and centre-of-mass energy, using the ratio of the measured cross-section to its theoretical prediction. It is assumed that the top-quark-related CKM matrix elements obey the relation |V-td|, |V-ts| « |V-tb|. All the |f(LV)V(tb)|(2) determinations, extracted from individual ratios at = 7 and 8 TeV, are combined, resulting in |f(LV)V(tb)| = 1.02 +/- 0.04 (meas.) +/- 0.02 (theo.). All combined measurements are consistent with their corresponding Standard Model predictions.