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20 results for “Sanctions”
Replication data for An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia
<p>This is the dataset to replicate all the tables and figures in the paper <a href="https://doi.org/10.1093/epolic/eiad027">"An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia"</a>, published in <i>Economic Policy</i>, 2023, by Jean Imbs and Laurent Pauwels. All data manipulations and programming are detailed on the GitHub site:<a href="https://github.com/laurentpauwels/sanctionpaper"> https://github.com/laurentpauwels/sanctionpaper</a>. The raw and processed data are in this <i>sanctionpaperdata_v1/matlab/data folder. </i>For convenience the simulation output <i>(simulationoutput.txt) </i>required to build the scatter plots in Figure 1 with STATA is available in<i> sanctionpaperdata</i>_v1<i>/matlab/output</i>.</p><p><strong>Instructions</strong> </p><p> If you clone the GitHub repository:</p><p>1. Place the downloaded <i>data</i> folder (located in <i>sanctionpaperdata_v1/matlab/)</i> in the <i>matlab</i> folder of the GitHub repository. </p><p>2. Place the downloaded <i>simulation_output.txt</i> I(located in <i>sanctionpaperdata_v1/matlab/output/) </i>in the <i>matlab/output </i>folder of the GitHub repository if you do not want to run the simulations as detailed on GitHub.</p><p><strong>Description</strong></p><p>The <i>matlab/data/raw</i> folder contains an <i>ICIO21</i> folder with the ICIO21 data, and a <i>WIOD</i> folder with the SEA16 data (in <i>data/raw/WIOD/SEA16</i>) and the WIOT16 data in CSV format (in <i>data/raw/WIOD/WIOT16</i>).</p><p>NOTE: WIOD provides the data in XLSB format. The XLSB WIOD data is in the <i>WIOT_in_EXCEL.zip</i> located in the <i>matlab/data/raw/WIOD/</i>. Python is used to convert XLSB into CSV files. See python code in GitHub repository for unzipping and conversion to CSV. The converted CSV files are provided for convenience.</p><p>The parsed and pre-processed ICIO21, SEA16, and WIOT16 data are stored in the <i>/matlab/data/processed</i> folder into three separate .mat structure files:</p><p><i>icio21_strc.mat</i> contains:</p><ul><li>the meta data (<i>icio21_text</i>), i.e., the information about the structure of the numerical data such as lists of countrycode, countries, industrycode, industries, isic_rev4 codes, years covered, name of final categories, etc.</li><li>the numerical data (<i>icio21_data</i>):<ul><li>Z (<i>icio21_data.Z</i>), the intermediate IO data for the listed industries (R), countries (N), and years (T). Its structure is 3-dimensionsal: (NxR)x(NxR)xT.</li><li>F (<i>icio21_data.F</i>), the final demand data for the same countries, industries and years. Its structure is 3-dimension: (NxR)x(NxC)xT. The columns are NxC where C are the number of final demand categories.</li></ul></li></ul><p><br><i>wiod16_strc.mat</i> has the same structure as <i>icio21_strc.ma</i>t with the meta data in <i>wiot16_text</i> and the numerical data in <i>wiot16_data</i>.</p><p><i>sea16_strc.mat</i> has the meta data in <i>sea16_text</i> and the numerical data in <i>sea16_data</i>. SEA16 contains 16 variables instead of Input-Output type data. The country, industry, and year coverage is not the same as ICIO21.</p><p>NOTE: <i>matlab/scripts/convertMatlabStruc2data.m</i> in the GitHub repository converts <i>MATLAB v7.3 </i>format ("structure data") to an updated format without structure so that it is more easily compatible with other software. All data parsing and preprocessing are done with MATLAB, see GitHub repository for details.</p><p><strong>Sources</strong></p><p>The raw data come from these sources:</p><p>1. OECD Inter-Country Input-Output (ICIO) data November 2021 release (downloaded on 2 July 2023)</p><p>- Source: OECD-ICIO 2021 release data is available at <a href="http://oe.cd/icio">http://oe.cd/icio</a></p><p>2. WIOD Socio-Economic Accounts (SEA) data 2016 release (downloaded on 30 May 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a></p><p>3. WIOD World Input-Output Tables (WIOT) data November 2016 (downloaded on 23 June 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a> </p>
Data from: The impact of aid sanctions on maternal and child mortality, 1990–2019: A panel analysis
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Data from: Managing friends and foes: Sanctioning mutualists in mixed‐infection nodules trades off with defense against antagonists
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How do Less-expensive Nitrogen Alternatives Affect Legume Sanctions on Rhizobia?
<p>Mutualistic interactions involving multiple partners require 'sanctioning' - the ability to influence the fitness of each partner based on its respective contribution. Sanctions must be sensitive to even small differences if even slightly less-beneficial partners could gain a fitness advantage by diverting resources away from the mutualistic service towards their own reproductive fitness. Here, we test whether legume hosts sanction even mediocre N2-fixing rhizobial strains by influencing either its nodule growth or carbon accumulation (polyhydroxybutryate or PHB) per rhizobia cell. We also test if sanctions depend on the availability of less-expensive nitrogen alternatives, either as nitrate or co-inoculation with a more-efficient isogenic strain. We found that nitrate eliminated differences in nodule size between the mediocre and more-efficient strains, suggesting that host sanctions were compromised. However, nitrate additions also decreased PHB accumulation by the mediocre strain, which may eliminate any fitness advantages of lower fixation by this strain. Co-inoculation with a more-efficient strain could also compromise host sanctions if reduction in fitness from smaller nodules does not offset the potential fitness gain from greater PHB accumulation that we observed in the mediocre strain. Hence, a host's ability to sanction mediocre strains depends not only on alternative sources of nitrogen but also the relative importance of different components of rhizobial fitness.</p>
Dataset: Sanctions and international interaction improve cooperation to avert climate change
<p>Imposing sanctions on non-compliant parties to international agreements is advocated as a remedy for international cooperation failure. Nevertheless, sanctions are costly, and rational choice theory predicts their ineffectiveness in improving cooperation. We test sanctions effectiveness experimentally in international collective-risk social dilemmas simulating efforts to avoid catastrophic climate change. We involve individuals from countries where sanctions were shown to be effective (Germany) or ineffective (Russia) in increasing cooperation. Here we show that, while this result still holds nationally, international interaction backed by sanctions is beneficial. Cooperation by low cooperator groups increases relative to national cooperation and converges to the levels of high cooperators. This result holds regardless of revealing other group members' nationality, suggesting that participants' specific attitudes or stereotypes over the other country were irrelevant. Groups interacting under sanctions contribute more to catastrophe prevention than what would maximise expected group payoffs. This behaviour signals a strong propensity for protection against collective risks.</p>
The emergence of division of labor through decentralized social sanctioning
<p>Human ecological success relies on our characteristic ability to flexibly self-organize into cooperative social groups, the most successful of which employ substantial specialization and division of labor. Unlike most other animals, humans learn by trial and error during their lives what role to take on. However, when some critical roles are more attractive than others, and individuals are self-interested, then there is a social dilemma: each individual would prefer others take on the critical but unremunerative roles so they may remain free to take one that pays better. But disaster occurs if all act thusly and a critical role goes unfilled. In such situations learning an optimum role distribution may not be possible. Consequently, a fundamental question is: how can division of labor emerge in groups of self-interested lifetime-learning individuals? Here we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labor involving all critical roles. Such social norms work by redistributing rewards within the population to disincentivize antisocial roles while incentivizing prosocial roles that do not intrinsically pay as well as others.</p>
The emergence of division of labor through decentralized social sanctioning
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Data from: Host investment into symbiosis varies among genotypes of the legume Acmispon strigosus, but host sanctions are uniform
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Dataset: Sanctions and international interaction improve cooperation to avert climate change
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Nitrogen fertilization nullifies host sanctions against non-fixing rhizobia and drives divestment from symbiosis in Lotus japonicus
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How do Less-expensive Nitrogen Alternatives Affect Legume Sanctions on Rhizobia?
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Data from: Sanctions, partner recognition, and variation in mutualism
Mutualistic interactions can be stabilized against invasion by noncooperative individuals by putting such "cheaters" at a selective disadvantage. Selection against cheaters should eliminate genetic variation in partner quality—yet such variation is often found in natural populations. One explanation for this paradox is that mutualism outcomes are determined not only by responses to partner performance but also by partner signals. Here, we build a model of coevolution in a symbiotic mutualism, in which hosts' ability to sanction noncooperative symbionts and recognition of symbiont signals are determined by separate loci, as are symbionts' cooperation and expression of signals. In the model, variation persists without destabilizing the interaction, in part because coevolution of symbiont signals and host recognition is altered by the coevolution of sanctions and cooperation, and vice versa. Individual-based simulations incorporating population structure strongly corroborate these results. The dual systems of sanctions and partner recognition converge toward conditions similar to some economic models of mutualistic symbiosis, in which hosts offering the right incentives to potential symbionts can initiate symbiosis without screening for partner quality. These results predict that mutualists can maintain variation in recognition of partner signals or in the ability to sanction noncooperators without destabilizing mutualism, and they reinforce the notion that studies of mutualism should consider communication between partners as well as the exchange of benefits.
Factor Endowments, Economic Integration, Sanctions, and Offshores: Evidence from Inward FDI in Russia
<p>These files reproduce figures and empirical results found in Cieślik, A., Gurshev, O. Factor Endowments, Economic Integration, Sanctions, and Offshores: Evidence from Inward FDI in Russia. <em>Comparative Economic Studies</em> (2022). https://doi.org/10.1057/s41294-022-00202-6 . If you use these files for your own work, please cite the abovementioned article. </p> <p>It includes data, graphs, and econometric analysis performed in the paper. We thank Peter Egger, Ariel Reshef, Matheu Parenti, Anne-Célia Disdier, Richard Frensch, Stefano Bolatto, Sarhad Hamza, one anonymous referee and participants of the graduate workshop hosted by Paris School of Economics for helpful comments on the earlier versions of the paper. Gurshev acknowledges financial assistance from University of Warsaw and host assistance of University of Bologna.</p>
Data for International trade sanctions imposed due to the Russia-Ukraine war may cause unequal distribution of environmental and health impacts
<p>Data for producing the figures in the study "International trade sanctions imposed due to the Russia-Ukraine war may cause unequal distribution of environmental and health impacts".</p>
MERICS Podcast: How China approaches sanctions, with Francesca Ghiretti and Maria Shagina
<p>International sanctions have become a common tool in the relationship between the US, the EU, and China in the last decade. If we add the sanctions on Russia in connection with its full-scale invasion of Ukraine into the mix and the question of Chinese adherence or circumvention of them, it seems high time that we devote some time to talking about Chinese thinking on this tool of international coercion.</p> <p>In this episode of the MERICS China Podcast, we talk to MERICS Analyst <strong>Francesca Ghiretti</strong>, who has recently written a report on the evolution of China’s sanction regime, and IISS Senior Fellow <strong>Maria Shagina</strong>, who among other topics focusses her research on the issues of Russia and international sanctions. Questions by <strong>Johannes Heller-John</strong>.</p> <p>This podcast episode is part of the “Dealing with a Resurgent China” (DWARC) project, which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101061700.</p> <p>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p>
Evaluation of Virtual Course to Increase Knowledge in Sexual Harassment,Prevention and Sanction Policies of a University
ClinicalTrials.gov study NCT04021849. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Sanctions, partner recognition, and variation in mutualism
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Data from: Discriminative host sanction together with relatedness promote the cooperation in fig/fig wasp mutualism
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Evaluating A Drug Testing and Graduated Sanctions Program in Delaware
ClinicalTrials.gov study NCT01187121. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Interventions for Sanctioned Ohio University Students
ClinicalTrials.gov study NCT02603978. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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