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22 pages ; International audience ; There is a great deal of literature regarding the asymptotic properties of various approaches to estimating simultaneous space-time panel models, but little attention has been paid to how the model estimates should be interpreted. The motivation for use of space-time panel models is that they can provide us with information not available from cross-sectional spatial regressions. LeSage and Pace (2009) show that cross-sectional simultaneous spatial autoregressive models can be viewed as a limiting outcome of a dynamic space-time autoregressive process. A valuable aspect of dynamic space-time panel data models is that the own- and cross-partial derivatives that relate changes in the explanatory variables to those that arise in the dependent variable are explicit. This allows us to employ parameter estimates from these models to quantify dynamic responses over time and space as well as space-time diffusion impacts. We illustrate our approach using the demand for cigarettes over a 30 year period from 1963-1992, where the motivation for spatial dependence is a bootlegging effect where buyers of cigarettes near state borders purchase in neighboring states if there is a price advantage to doing so. ; La littérature économétrique récente fait une place croissante à l'étude des propriétés asymptotiques des différentes méthodes d'estimation des modèles de données de panel spatio-temporels. Toutefois, force est de constater que peu d'attention est consacrée à l'interprétation économique de tels modèles malgré leur grand intérêt pour la modélisation des phénomènes économiques dans une dimension spatio-temporelle et le rôle qu'ils pourraient jouer dans l'évaluation des politiques économiques dans cette même dimension. Nous montrons dans ce papier que les coefficients estimés de ces modèles permettent d'expliciter non seulement la dynamique temporelle des impacts mais également leur dynamique spatiale et surtout de quantifier la diffusion spatio-temporelle de l'impact d'une variation d'une ...

22 pages ; International audience ; There is a great deal of literature regarding the asymptotic properties of various approaches to estimating simultaneous space-time panel models, but little attention has been paid to how the model estimates should be interpreted. The motivation for use of space-time panel models is that they can provide us with information not available from cross-sectional spatial regressions. LeSage and Pace (2009) show that cross-sectional simultaneous spatial autoregressive models can be viewed as a limiting outcome of a dynamic space-time autoregressive process. A valuable aspect of dynamic space-time panel data models is that the own- and cross-partial derivatives that relate changes in the explanatory variables to those that arise in the dependent variable are explicit. This allows us to employ parameter estimates from these models to quantify dynamic responses over time and space as well as space-time diffusion impacts. We illustrate our approach using the demand for cigarettes over a 30 year period from 1963-1992, where the motivation for spatial dependence is a bootlegging effect where buyers of cigarettes near state borders purchase in neighboring states if there is a price advantage to doing so. ; La littérature économétrique récente fait une place croissante à l'étude des propriétés asymptotiques des différentes méthodes d'estimation des modèles de données de panel spatio-temporels. Toutefois, force est de constater que peu d'attention est consacrée à l'interprétation économique de tels modèles malgré leur grand intérêt pour la modélisation des phénomènes économiques dans une dimension spatio-temporelle et le rôle qu'ils pourraient jouer dans l'évaluation des politiques économiques dans cette même dimension. Nous montrons dans ce papier que les coefficients estimés de ces modèles permettent d'expliciter non seulement la dynamique temporelle des impacts mais également leur dynamique spatiale et surtout de quantifier la diffusion spatio-temporelle de l'impact d'une variation d'une variable explicative. La méthode proposée est illustrée par une étude de la demande de cigarettes dans 46 Etats américains sur la période 1963-1992 en utilisant une base de données bien connue dans la littérature économétrique. La présence d'autocorrélation spatiale est ici motivée par un effet de " contrebande ". Les consommateurs proches des frontières d'un état achèteront en effet leurs cigarettes dans les états voisins si le prix des cigarettes y est inférieur à celui pratiqué dans leur propre Etat.

Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using generalized method of moments estimators. Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification.

The Clean Development Mechanism (CDM) allows emission reduction (or emission removal) projects in developing countries to earn Certified Emission Reduction (CER) credits, each equivalent to one tonne of CO2. These CERs can be traded and sold, and used by industrialized countries to meet a part of their emission reduction targets under the Kyoto Protocol. The mechanism stimulates sustainable development and emission reductions, while giving industrialized countries some flexibility in how they meet their emission reduction limitation targets. Accepted projects must qualify through a rigorous and public registration and issuance process designed to ensure real, measurable and verifiable emission reductions that are additional to what would have occurred without the project. Between November 2004 and May 2009, the mechanism has registered 1,653 projects and is anticipated to currently produce CERs amounting to 303 106 tonnes of CO2 equivalent yearly. The mechanism is extremely interesting since it is the first global, environmental investment and credit scheme of its kind, providing a standardized emissions offset instrument. However the geographical distribution of the CDM projects is revealing very large differences in between developing countries since China, India, Brazil and South-Korea totalise 82 % of the CERs while Africa only represents 3,3% of the total.

The Clean Development Mechanism (CDM) offers abatement cost savings under the Kyoto Protocol by allowing credits for emission reductions obtained in signatory developing countries. The paper argues that technology transfers can improve incentives for cost-effective emission reductions under bilateral CDM contracts when there is asymmetric information between the investor and the host party.JEL classification: Q20; D82

The task of this work is to discuss issues conceming the specification, estimation, inference and forecasting in multivariate dynamic heterogeneous panel data models from a Bayesian perspective. Three essays linked by a few conraion ideas compose the work. Multivariate dynamic models (mainly VARs) based on micro or macro panel data sets have become increasingly popular in macroeconomics, especially to study the transmission of real and monetary shocks across economies. This great use of the panel VAR approach is largely justified by the fact that it allows the docimientation of the dynamic impact of shocks on key macroeconomic variables in a framework that simultaneously considers shocks emanating from the global enviromnent (world interest rate, terms of trade, common monetary shock) and those of domestic origin (supply shocks, fiscal and monetary policy, etc.). Despite this empirical interest, the theory for panel VAR is somewhat underdeveloped. The aim of the thesis is to shed more light on the possible applications of the Bayesian framework in discussing estimation, inference, and forecasting using multivariate dynamic models where, beside the time series dimensión we can also use the information contained in the cross sectional dimensión. The Bayesian point of view provides a natural environment for the models dlscussed in this work, due to its flexibility in combining diíferent sources of information. Moreover, it has been recently shown that Bayes estimates of hierachical dynamic panel data models have a reduced small sample bias, and help in improving the forecasting performance of these models.

AbstractThis paper examines the ethics of the Clean Development Mechanism (CDM) in its architecture, processes and outcomes and its potential to allocate resources to the poor as 'ethical development'. Two specific examples of CDM projects help us to explore some of the quandaries that seem to be quickly defining operating procedure for the CDM in its efforts to bring entitlementsto the poor. The paper concludes with reflections on the normative and social complications of the CDM and closes with three key areas of further investigation. Copyright © 2011 John Wiley & Sons, Ltd.

PurposeThis paper aims to assess whether official development assistance (ODA) or foreign aid has been effective in reducing extreme poverty; test whether the type and source of aid matter; and examine whether political or economic freedom enhances aid effectiveness in developing countries.Design/methodology/approachThe study uses recent dynamic panel estimation techniques (system generalised method of moments), including those methods which deal with endogeneity by controlling for simultaneity and unobserved heterogeneity.FindingsThe main findings of the study are: firstly, foreign aid does have a statistically significant poverty reduction effect and the results are consistent across all the three extreme poverty proxies. Secondly, the disaggregation of aid by source and type shows that total aid, grant and bilateral aid are more likely to reduce poverty. Thirdly, political freedom might not be an effective channel through which aid impacts extreme poverty, but aid is more effective in an environment where there is respect for freedom of enterprise.Research limitations/implicationsAs with most cross-country aid–growth–poverty dynamic panel data studies, the challenges of establishing robust causality and accounting for the unobserved country-specific heterogeneity remain apparent. However, given the data availability constraints, generalised method of moments is, to the best of the authors' knowledge, the most robust empirical strategy when T < N. Future research could explore possibilities of individual country analysis, disaggregating countries by income and also examining the direction of causality between foreign aid, poverty and democracy.Practical implicationsThe policy implications are that the development partners should continue to focus on poverty reduction as the main objective for ODA; aid allocation should be focused on channels which have more poverty-reduction effect, such as per capita income and economic freedom; and aid recipient countries should also focus on reducing inequality.Social implicationsThe main social implications from this study is that it is possible to reduce poverty through ODA. Second, to enhance the effectiveness of foreign aid, ODA allocation should be focussed on channels, which have more poverty-reduction effect, and the host countries should have economic freedom.Originality/valueThis paper makes a further contribution to the aid effectiveness literature, especially the channels through which foreign aid affects poverty.

Non-linearities and dynamic interactions between state variables are characteristic of complex social systems and processes. In this thesis, we present a new methodology to model these non-linearities and interactions from the large panel datasets available for some of these systems. We build macro-level statistical models that can verify theoretical predictions, and use polynomial basis functions so that each term in the model represents a specific mechanism. This bridges the existing gap between macro-level theories supported by statistical models and micro-level mechanistic models supported by behavioural evidence. We apply this methodology to two important problems in the social sciences, the demographic transition and the transition to democracy. The demographic transition is an important problem for economists and development scientists. Research has shown that economic growth reduces mortality and fertility rates, which reduction in turn results in faster economic growth. We build a non-linear dynamic model and show how this data-driven model extends existing mechanistic models. We also show policy applications for our models, especially in setting development targets for the Millennium Development Goals or the Sustainable Development Goals. The transition to democracy is an important problem for political scientists and sociologists. Research has shown that economic growth and overall human development transforms socio-cultural values and drives political institutions towards democracy. We model the interactions between the state variables and find that changes in institutional freedoms precedes changes in socio-cultural values. We show applications of our models in studying development traps. This thesis comprises the comprehensive summary and seven papers. Papers I and II describe two similar but complementary methodologies to build non-linear dynamic models from panel datasets. Papers III and IV deal with the demographic transition and policy applications. Papers V and VI describe the transition to democracy and applications. Paper VII describes an application to sustainable development.