Institution
Government of Canada
Government•Ottawa, Ontario, Canada•
About: Government of Canada is a government organization based out in Ottawa, Ontario, Canada. It is known for research contribution in the topics: Monetary policy & Debt. The organization has 796 authors who have published 886 publications receiving 21366 citations. The organization is also known as: federal government of Canada & Her Majesty's Government.
Papers published on a yearly basis
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01 Jan 2020TL;DR: In this paper, the contributors sought to re-examine the concept of military professionalism, highlight research from different nations, and present novel thinking and a re-examination on aspects related to military professionalism.
Abstract: The contributors to this volume sought to (1) re-examine the concept of military professionalism, (2) highlight research from different nations, and (3) present novel thinking and a re-examination on aspects related to military professionalism. This concluding chapter attempts to summarize and synthesize the research presented here and to provide suggestions on the usefulness of common theoretical frameworks and future directions in the study and implementation of military professionalism.
1 citations
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TL;DR: This study investigates the efficacy of Collaborative Learning recitation sessions on student outcomes in large introductory microeconomics sections and addresses the selection issues by using a quasi-randomization experiment and measures the treatment effects by programme evaluation methods.
Abstract: The effectiveness of voluntary training programs is inherently difficult to measure due to the issue of selection bias. Random assignment is suggested as a method to ameliorate this bias. We construct our own randomization experiment to gain insight into this question by utilizing a structured platform. In this study, we investigate the efficacy of Collaborative Learning (CL) recitation sessions on student outcomes in large introductory microeconomics sections. We address the selection issues by using a quasi-randomization experiment and measure the treatment effects by programme evaluation methods. We find that the average treatment effect of CL is 4.6-4.9 percent of the final grade or in qualitative terms, a grade change i.e. B to B plus. We also investigate the distributional effects via quantile treatment effects. We find that CL participants at the bottom 40 percentile showed the greatest improvement. Finally, we apply a nonparametric sensitivity analysis and confirm that the sign of the treatment effects is robust to potential violations of the underlying assumptions. Therefore, this voluntary training is effective.
1 citations
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TL;DR: The authors developed and estimated a DSGE model which realistically assumes that many goods in the economy are produced through more than one stage of production and showed that intermediate-stage technology shocks explain most of short-run output fluctuations, whereas final-stage technological shocks only have a small impact.
Abstract: We develop and estimate a DSGE model which realistically assumes that many goods in the economy are produced through more than one stage of production. Firms produce differentiated goods at an intermediate stage and a final stage, post different prices at both stages, and face stage-specific technological change. Wage-setting households are imperfectly competitive with respect to labor skills. Intermediate-stage technology shocks explain most of short-run output fluctuations, whereas final-stage technology shocks only have a small impact. Despite the dominance of technology shocks, the model predicts a near-zero correlation between hours worked and the return to work and mildly procyclical real wages. The factors mainly responsible for these findings are an input-output linkage between firms operating at the different stages and movements in the relative price of goods. We show that, depending the source, a technology improvement may either have a contractionary or expansionary impact on employment.
1 citations
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TL;DR: In this article, the authors study the effect of network structure on technology adoption in the setting of the Python programming language and build a dynamic model of technology adoption where each package makes an irreversible decision to provide support for Python 3.
Abstract: This paper studies the effect of network structure on technology adoption, in the setting of the Python programming language. A major release of Python, Python 3, provides more advanced but backward-incompatible features to Python 2. We model the dynamics of Python 3 adoption made by package developers. Python packages form a hierarchical network through dependency requirements. The adoption decision involves not only updating one’s own source code, but also dealing with dependency packages lacking Python 3 support. We build a dynamic model of technology adoption where each package makes an irreversible decision to provide support for Python 3. The optimal timing of adoption requires a prediction of all future states, for the package itself as well as each of its dependencies. With a complete dataset of package characteristics for all historical releases, we are able to draw the complete hierarchical structure of the network, and simplify the estimation by grouping packages into different layers based on the dependency relationship. We study how individual adoption decisions can propagate along the links in such a hierarchical network. We also test the effectiveness of various counterfactual policies that can promote a faster adoption process.
1 citations
Authors
Showing all 802 results
Name | H-index | Papers | Citations |
---|---|---|---|
Kingston H. G. Mills | 92 | 313 | 29630 |
David W. Schindler | 85 | 217 | 39792 |
Martha C. Anderson | 70 | 340 | 20288 |
Hui Li | 62 | 246 | 14395 |
Lei Zhang | 58 | 146 | 21872 |
Michael J. Vanni | 55 | 124 | 11714 |
Cars Hommes | 54 | 250 | 14984 |
Richard E. Caves | 53 | 115 | 24552 |
John W. M. Rudd | 51 | 70 | 9446 |
Karen A. Kidd | 47 | 163 | 10255 |
Kenneth O. Hill | 43 | 126 | 8842 |
Steven H. Ferguson | 43 | 225 | 6797 |
Derwyn C. Johnson | 41 | 103 | 8208 |
Kevin E. Percy | 40 | 91 | 5167 |
Guy Ampleman | 40 | 128 | 4706 |