Essays in political economy

Cette thése relie trois articles sur l'économie politique. Ces articles analysent à la fois théoriquement et empiriquement si, et dans quelle mesure, trois phénomènes politiques différents (les partis politiques, les guerres civiles et les menaces externes), et leur interaction, influent sur les résultats économiques. Le premier chapitre étudie l'impact de la présence au pouvoir des politiciens de nouveaux partis politiques sur la taille du gouvernement. Le chapitre se concentre sur les municipalités colombiennes, où les nouveaux partis politiques ont été nombreux et fructueux au cours des dernières années. Les estimations par régressions sur discontinuité montrent que les dépenses publiques et les recettes fiscales sont significativement plus élevées dans les municipalités gouvernées par un maire d'un nouveau parti politique. En utilisant des informations sur la politique locale et des caractéristiques des nouveaux partis, je soutiens que ce résultat peut être expliqué par le fait qu'il y a moins d'information sur les politiciens de nouveaux partis que les politiciens des partis traditionnels. Le deuxième chapitre développe une nouvelle explication de l'impact des guerres civiles et des conflits interétatiques sur le state-building qui repose sur l'idée que les protagonistes de ces deux types de conflits peuvent avoir un lien (ethnique ou idéologique). Un premier résultat montre que la force de ce lien détermine si les conflits contre des adversaires internes (i.e. guerres civiles) ou des ennemis externes (i.e. conflits interétatiques) sont complémentaires ou se substituent, conduisant à plus ou moins d'investissement en capacité fiscale. La théorie prédit également un rôle non trivial de la stabilité politique dans la relation entre les deux types de conflits et la capacité fiscale: un deuxième résultat montre que, bien que la stabilité politique se traduit par moins de capacité fiscale, plus de stabilité n'implique pas plus de state-building. Leur équivalence dépend du niveau de cohésion des institutions. Un nouveau mécanisme par lequel plus de stabilité politique peut impliquer moins de state-building est proposé. En outre, il est démontré que des corrélations dans les données cross-country sont compatibles avec la théorie. Le troisième chapitre examine la relation entre la probabilité d'occurrence d'un conflit intérieur violent et le risque qu'un tel conflit "s'externalise" (c'est à dire se propage dans un autre pays en devenant un conflit interétatique). Je considère une situation dans laquelle un conflit interne entre un gouvernement et un groupe rebelle peut s'externaliser. Je montre que le risque d'externalisation augmente la probabilité d'un accord de paix, mais seulement si le gouvernement est suffisamment puissant par rapport aux rebelles, et si le risque d'externalisation est suffisamment élevé. Je montre comment ce modèle aide à comprendre les récents pourparlers de paix entre le gouvernement colombien et le groupe le plus puissant des rebelles dans le pays, les FARC.

O crime de drogas e a violência em São Paulo: uma análise a partir da Lei de Drogas

O objetivo desta pesquisa é estudar o efeito da Lei nº 11.343/06 (Lei de Drogas) sobre o crime de tráfico e porte de drogas e a relação entre crimes de drogas e outros crimes. Para isso, são exploradas as variações da Lei de Drogas, através de análises de regressões com descontinuidade e com variável instrumental, além de estimações com dados em painel, em busca de um efeito causal entre drogas e violência. Como resultados, a Lei de Drogas parece não ter efeito negativo significativo sobre crimes de drogas. Por outro lado, crimes de drogas apresentam uma associação negativa sobre crimes de furto e uma relação positiva com crimes de formação de quadrilha. Para cada redução de 100 crimes de drogas (por mil habitantes) associa-se um aumento de 3,6 crimes de furto (por mil habitantes) e uma diminuição de 27 crimes de formação de quadrilha (por mil habitantes). Não são encontrados efeitos robustos sobre roubos, homicídios, latrocínios, estupros, crimes de lesão corporal e porte de arma de fogo.

Quasi-experimental estimates of fiscal spillovers and spatial interactions among Brazilian cities

Nós usamos a metodologia de Regressões em Descontinuidade (RDD) para estimar o efeito causal do Fundo de Participação dos Municípios (FPM) recebido por um município sobre características dos municípios vizinhos, considerando uma variedade de temas: finanças públicas, educação, saúde e resultados eleitorais. Nós exploramos a regra que gera uma variação exógena da transferência em munícipios próximos às descontinuidades no repasse do fundo de acordo com faixas de população. Nossa principal contribuição é estimar separadamente e em conjunto o efeito spillover e o efeito direto do FPM, considerando ambos municípios vizinhos ou apenas um deles próximos às mudanças de faixa. Dessa forma, conseguimos entender melhor a interação entre municípios vizinhos quando há uma correlação na probabilidade de receber uma transferência federal. Nós mostramos que a estimativa do efeito direto do FPM sobre os gastos locais diminui em cerca de 20% quando controlamos pelo spillover do vizinho, que em geral é positivo, com exceção dos gastos em saúde e saneamento. Nós estimamos um efeito positivo da transferência sobre notas na prova Brasil e taxas de aprovação escolares em municípios vizinhos e na rede estadual do ensino fundamental. Por outro lado, o recebimento de FPM por municípios vizinhos de pequena população reduz o provimento de bens e serviços de saúde em cidades próximas e maiores, o que pode ocorrer devido à redução da demanda por serviços de saúde. A piora de alguns indicadores globais de saúde é um indício, no entanto, de que podem existir problemas de coordenação para os prefeitos reterem seus gastos em saúde. De fato, quando controlamos pela margem de vitória nas eleições municipais e consideramos apenas cidades vizinhas com prefeitos de partido diferentes, o efeito spillover é maior em magnitude, o que indica que incentivos políticos são importantes para explicar a subprovisão de serviços em saúde, por um lado, e o aumento da provisão de bens em educação, por outro. Nós também constatamos um efeito positivo do FPM sobre votos para o partido do governo federal nas eleições municipais e nacionais, e grande parte desse efeito é explicado pelo spillover do FPM de cidades vizinhas, mostrando que cidades com dependência econômica do governo federal se tornam a base de sustentação e apoio político desse governo. Por fim, nós encontramos um efeito ambíguo do aumento de receita devido ao FPM sobre a competição eleitoral nas eleições municipais, com uma queda da margem de vitória do primeiro colocado e uma redução do número de candidatos, o que pode ser explicado pelo aumento do custo fixo das campanhas locais.

Essays on Unemployment Insurance

Can the potential availability of unemployment insurance (UI) affect the behavior of employed workers and the duration of their employment spells? After discussing few straightforward reasons why UI may affect employment duration, I apply a regression kink design (RKD) to address this question using linked employer-employee data from the Brazilian labor market. Exploiting the UI schedule, I find that potential benefit level significantly affects the duration of employment spells. This effect is local to low skilled workers and, surprisingly, indicates that a 1\% increase in unemployment benefits increases job duration by around 0.3\%. Such result is driven by the fact that higher UI decreases the probability of job quits, which are not covered by UI in Brazil. These estimates are robust to permutation tests and a number of falsification tests. I develop a reduced-form welfare formula to assess the economic relevance of this result. Based on that, I show that the positive effect on employment duration implies in a higher optimal benefit level. Moreover, the formula shows that the elasticity of employment duration impacts welfare just with the same weight as the well-known elasticity of unemployment duration to benefit level.

Phase 1 study of HPV16-specific immunotherapy with E6E7 fusion protein and ISCOMATRIX (TM) adjuvant in women with cervical intraepithelial neoplasia

Purpose: Persistent infection of cervical epithelium with high risk human papillomavirus (HPV) results in cervical intraepithelial neoplasia (CIN) from which squamous cancer of the cervix can arise. A study was undertaken to evaluate the safety and immunogenicity of an HPV 16 immunotherapeutic consisting of a mixture of HPV16 E6E7 fusion protein and ISCOMATRIX(TM) adjuvant (HPV16 Immunotherapeutic) for patients with CIN. Experimental design: Patients with CIN (n = 3 1) were recruited to a randomised blinded placebo controlled dose ranging study of immunotherapy. Results: Immunotherapy was well tolerated. Immunised subjects developed HPV16 E6E7 specific immunity. Antibody, delayed type hypersensitivity, in vitro cytokine release, and CD8 T cell responses to E6 and E7 proteins were each significantly greater in the immunised subjects than in placebo recipients. Loss of HPV16 DNA from the cervix was observed in some vaccine and placebo recipients. Conclusions : The HPV16 Immunotherapeutic comprising HPV16E6E7 fusion protein and ISCOMATRIX(TM) adjuvant is safe and induces vaccine antigen specific cell mediated immunity. (C) 2004 Elsevier Ltd. All rights reserved.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Essays in Long-run Development and Spatial Economics

This thesis tries to further our understanding for why some countries today are more prosperous than others. It establishes that part of today's observed variation in several proxies such as income or gender inequality have been determined in the distant past. Chapter one shows that 450 years of (Catholic) Portuguese colonisation had a long-lasting impact in India when it comes to education and female emancipation. Furthermore I use a historical quasi-experiment that happened 250 years ago in order to show that different outcomes have different degrees of persitence over time. Educational gaps between males and females seemingly wash out a few decades after the public provision of schools. The male biased sex-ratios on the other hand stay virtually unchanged despite governmental efforts. This provides evidence that deep rooted son preferences are much harder to overcome, suggesting that a differential approach is needed to tackle sex-selective abortion and female neglect. The second chapter proposes improvements for the execution of Spatial Regression Discontinuity Designs. These suggestions are accompanied by a full-fledged spatial statistical package written in R. Chapter three introduces a quantitative economic geography model in order to study the peculiar evolution of the European urban system on its way to the Industrial Revolution. It can explain the shift of economic gravity from the Mediterranean towards the North-Sea ("little divergence"). The framework provides novel insights on the importance of agricultural trade costs and the peculiar geography of Europe with its extended coastline and dense network of navigable rivers.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

Optimization of wood flour acetylation by factorial design and partial least squares regression

Acetylation was performed to reduce the polarity of wood and increase its compatibility with polymer matrices for the production of composites. These reactions were performed first as a function of acetic acid and anhydride concentration in a mixture catalyzed by sulfuric acid. A concentration of 50%/50% (v/v) of acetic acid and anhydride was found to produced the highest conversion rate between the functional groups. After these reactions, the kinetics were investigated by varying times and temperatures using a 3² factorial design, and showed time was the most relevant parameter in determining the conversion of hydroxyl into carbonyl groups.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Study Of The Homogeneity Of Drug Loaded In Polymeric Films Using Near-infrared Chemical Imaging And Split-plot Design.

Split-plot design (SPD) and near-infrared chemical imaging were used to study the homogeneity of the drug paracetamol loaded in films and prepared from mixtures of the biocompatible polymers hydroxypropyl methylcellulose, polyvinylpyrrolidone, and polyethyleneglycol. The study was split into two parts: a partial least-squares (PLS) model was developed for a pixel-to-pixel quantification of the drug loaded into films. Afterwards, a SPD was developed to study the influence of the polymeric composition of films and the two process conditions related to their preparation (percentage of the drug in the formulations and curing temperature) on the homogeneity of the drug dispersed in the polymeric matrix. Chemical images of each formulation of the SPD were obtained by pixel-to-pixel predictions of the drug using the PLS model of the first part, and macropixel analyses were performed for each image to obtain the y-responses (homogeneity parameter). The design was modeled using PLS regression, allowing only the most relevant factors to remain in the final model. The interpretation of the SPD was enhanced by utilizing the orthogonal PLS algorithm, where the y-orthogonal variations in the design were separated from the y-correlated variation.

Reliability-based evaluation of design code provisions for circular concrete-filled steel columns

This paper presents an investigation of design code provisions for steel-concrete composite columns. The study covers the national building codes of United States, Canada and Brazil, and the transnational EUROCODE. The study is based on experimental results of 93 axially loaded concrete-filled tubular steel columns. This includes 36 unpublished, full scale experimental results by the authors and 57 results from the literature. The error of resistance models is determined by comparing experimental results for ultimate loads with code-predicted column resistances. Regression analysis is used to describe the variation of model error with column slenderness and to describe model uncertainty. The paper shows that Canadian and European codes are able to predict mean column resistance, since resistance models of these codes present detailed formulations for concrete confinement by a steel tube. ANSI/AISC and Brazilian codes have limited allowance for concrete confinement, and become very conservative for short columns. Reliability analysis is used to evaluate the safety level of code provisions. Reliability analysis includes model error and other random problem parameters like steel and concrete strengths, and dead and live loads. Design code provisions are evaluated in terms of sufficient and uniform reliability criteria. Results show that the four design codes studied provide uniform reliability, with the Canadian code being best in achieving this goal. This is a result of a well balanced code, both in terms of load combinations and resistance model. The European code is less successful in providing uniform reliability, a consequence of the partial factors used in load combinations. The paper also shows that reliability indexes of columns designed according to European code can be as low as 2.2, which is quite below target reliability levels of EUROCODE. (C) 2009 Elsevier Ltd. All rights reserved.