Analyse causale et méthodes quantitatives : une introduction avec R, Stata et SPSS

L'analyse causale est une des tâches principales du scientifique. Un criminologue évalue l'effet d'une sentence sur la probabilité qu'un condamné récidive. Une économiste mesure l'effet de la discrimination raciale sur les perspectives d'emploi d'un immigrant. Un politologue étudie l'effet des médias sociaux sur la popularité des partis d'extrême droite. Une spécialiste du marketing jauge l'effet d'une campagne publicitaire sur les choix des consommateurs. Malheureusement, démontrer l'existence de telles relations est difficile, puisque de nombreux phénomènes sociaux ou physiques sont fortement associés, sans être liés par une relation de cause à effet. La distinction entre association et causalité est une des pierres d'assise de la démarche scientifique. Pourtant, cette distinction est souvent ignorée dans la vie de tous les jours, quand des arguments causaux sont défendus sur la base de simples observations descriptives. Cette différence est aussi passée sous silence dans la formation méthodologique que plusieurs étudiants reçoivent à l'université. Trop souvent, les manuels de méthodes quantitatives ignorent la question causale, ou recommandent d'interpréter les résultats d'un modèle statistique en termes causaux, alors qu'ils sont corrélationnels. Pour remédier à ce problème, ce livre offre une introduction intégrée aux méthodes quantitatives et à l'analyse causale. En plus de présenter les outils nécessaires pour exécuter des analyses statistiques, il offre un cadre théorique simple et rigoureux pour interpréter les résultats de ces analyses. Ce cadre théorique permet d'identifier les conditions qui doivent être réunies afin que l'interprétation causale de nos résultats soit justifiée

The Limits of Foreign Aid Diplomacy: How Bureaucratic Design Shapes Aid Distribution

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113691/1/isqu12191-sup-0001-appendixS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/113691/2/isqu12191.pd

Tax Treaty Norms Among Lower- Income Countries and the Role of the UN Model: Past, Present and Potential

Bilateral tax treaties draw heavily from model conventions published by international organisations. In this paper we investigate the influence of the UN model on tax treaties signed by lower-income countries, as well as the potential for specific model provisions to be mainstreamed in a greater number of treaties. Despite its focus on the interests of lower-income countries, the UN model is often assumed to play a minor role compared to the OECD model. Drawing from an updated version of the ICTD Tax Treaties Explorer dataset, we find that a subset of UN model provisions can already be considered as the norm in treaties concluded by lower-income countries. Among the provisions now uniquely found in the UN model, these are the inclusion of ‘supervisory activities’ in article 5(3)a, and the whole of articles 5(3)(b) and 14. The prevalence of many UN articles is increasing, suggesting that more provisions could join these three. The influence of the UN model becomes even more apparent when we focus on the amount of bilateral investment into lower-income countries that is taxed according to UN model provisions. To assess the avenues for further change we study countries’ reservations to model conventions, as well as their recent negotiation history. This allows us to identify those provisions that are most likely to be strong priorities for lower-income countries, and acceptable to a large number of partner countries. In particular, UN articles 5(4)(b), 5(6) and 21(3) are all increasing in prevalence, have strong support from lower-income countries expressed as observations on the OECD model treaty, and show significant renegotiation potential from recent country-level precedent. Overall, we find that there is significant scope for lower-income countries to renegotiate treaties with a view to obtain more rights to tax income at source.Foreign, Commonwealth & Development OfficeBill & Melinda Gates FoundationNorwegian Agency for Development Cooperatio

Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

modelsummary: Data and Model Summaries in R

modelsummary is a package to summarize data and statistical models in R. It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. It makes it easy to execute common tasks such as computing robust standard errors, adding significance stars, and manipulating coefficient and model labels. Beyond model summaries, the package also includes a suite of tools to produce highly flexible data summary tables, such as dataset overviews, correlation matrices, (multi-level) cross-tabulations, and balance tables (also known as "Table 1"). The appearance of the tables produced by modelsummary can be customized using external packages such as kableExtra, gt, flextable, or huxtable; the plots can be customized using ggplot2. Tables can be exported to many output formats, including HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, Excel, RTF, PDF, and image files. Tables and plots can be embedded seamlessly in rmarkdown, knitr, or Sweave dynamic documents. The modelsummary package is designed to be simple, robust, modular, and extensible

Three Essays on the Political-Economy of International Taxation and Investment.

This dissertation has three parts. (1) The political determinants of foreign direct investment: A machine learning approach. (2) Network externalities and interdependent policymaking: The case of international withholding taxes. (3) Individual-specific uncertainty, political institutions, and treaty-making.PhDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/109034/1/varel_1.pd

Explaining the proliferation and design of international investment agreements

This Master's thesis aims to answer two questions: (1) Why do states sign international investment agreements (IIA)?; (2) What determines the substantive strength of these agreements? I use an event history analysis and an ordered logit model, respectively, to answer these questions. I find partial support for the hypothesis according to which the interests of capital-exporting states determine the pattern of IIA diffusion. While the results of my second test are somewhat inconclusive, they allow me to draw a number of interesting lessons for future research.Ce mémoire de maîtrise a pour objectif de répondre à deux questions: (1) Pourquoi les États signent-ils des accords internationaux d'investissement (AII)?; (2) Qu'est-ce qui détermine la force de ces accords? J'utilise un modèle de survie et une régression logistique ordonnée, respectivement, pour répondre à ces deux questions. Les résultats de mon analyse supportent l'idée selon laquelle l'intérêt des pays exportateurs de capital est un déterminant important de la diffusion d'AIIs. Bien que les résultats de mon second test ne soient pas aussi co ncluants, ils indiquent clairement la route que devraient emprunter les travaux futurs sur le thème des IIAs

The Political Determinants of Foreign Direct Investment: A Firm-Level Analysis

Many large-N cross-national studies claim to show that political institutions and phenomena determine where foreign direct investment (FDI) flows. In this article, I argue that these studies tend to overemphasize statistical significance and often neglect to assess the explanatory or predictive power of their theories. To illustrate the problem, I estimate variations of a statistical model published in an influential article on “Political Risk, Institutions, and FDI.” I find that none of the political variables that the authors consider accounts for much of the variation in aggregate FDI inflows. To ensure that this underwhelming result is not driven by misspecification or measurement error, I leverage a large firm-level data set on the investment location decisions of thousands of multinational firms. Using nonparametric machine-learning techniques and out-of-sample tests, I show that gravity variables can help us develop very accurate expectations about firm behavior but that none of the 31 “political determinants” of FDI that I consider can do much to improve our expectations. These findings have important implications because they suggest that governments retain some room to move in the face of economic globalization

When Can Multiple Imputation Improve Regression Estimates?

These materials serve to replicate the simulations and empirical analyses in "When Can Multiple Imputation Improve Regression Estimates?

khroma: Colour Schemes for Scientific Data Visualization

Color blindness affects a large number of individuals. When communicating scientific results colour palettes must therefore be carefully chosen to be accessible to all readers. This R package provides an implementation of Paul Tol’s colour schemes. These schemes are ready for each type of data (qualitative, diverging or sequential), with colours that are distinct for all people, including colour-blind readers. This package also provides tools to simulate colour-blindness and to test how well the colours of any palette are identifiable. To simulate colour-blindness in production-ready R figures you may also be interested in the 'colorblindr' package. For specific uses, several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) are implemented, but these colour schemes may not be colour-blind safe. All these colour schemes are implemented for use with base R 'graphics' or 'ggplot2'