Universiẗat, Diss.--Hannover, 1995.

Cette thèse contribue à l'approche économétrique de la croissance pro-pauvre. Elle présente des apports théoriques et empiriques. En premier lieu, elle présente les différentes définitions, indices et politiques de croissance pro-pauvre proposées dans la littérature théorique. Elle examine également les modèles théoriques et empiriques portant sur les interactions entre distribution du revenu et croissance. Elle montre que les mesures traditionnelles, en plus de leurs caractères partiels, peuvent conduire à des résultats contradictoires. Pour contourner ces limites, cette thèse privilégie l'approche alternative qui consiste à utiliser des modèles économétriques. Cette dernière approche, bien qu'elle présente l'avantage d'inclure l'ensemble des dimensions de la pauvreté, souffre de deux types de biais : le biais de sélection et le biais d'endogeneité. Ces derniers s'expliquent par les limitations inhérentes des données : erreurs de mesures, points aberrants. En outre, les résultats obtenus avec cette approche sont sensibles aux formes fonctionnelles choisies. Ainsi, il y'a de bonnes raisons d'utiliser la régression Gini. Malheureusement, les régressions de type Gini n'existaient qu'en coupe instantanée et en séries temporelles. Ainsi, dans un second temps, cette thèse propose d'étendre la réflexion sur la régression Gini en panel. Elle introduit les estimateurs intragroupes, intergroupes, le test d'existence de l'effet individuel et l'estimateur Aitken Gini. Enfin, cette thèse présente des applications empiriques qui illustrent de façon concrète la robustesse de nos estimateurs. Elle s'intéresse particulièrement aux conséquences de la méthode d'estimation et à la section de l'échantillon. Elle conclut que le processus de croissance favorise la réduction de la pauvreté à condition que les inégalités de revenu soient maîtrisées. Mais aussi, que l'impact de la croissance agricole sur la réduction de la pauvreté varie en fonction du niveau de développement du pays. / This thesis contributes to the econometric approach to pro-poor growth. It presents theoretical and empirical contributions. First, it presents the different definitions, indices and the policies of pro-poor growth proposed in the theoretical literature. It also examines the theoretical and empirical models on the interactions between income distribution and growth. It shows that the traditional measures, in addition to their partial characters, can lead to contradictory results. To avoid these limits this thesis emphasizes the alternative approach by using econometric models. The latter approach, although it has the advantage of including all the dimensions of poverty, suffering from two types of bias: selection bias and bias of endogeneity. These are due to the limitations of the data: measurement error, outliers. In addition, the results obtained with this approach are sensitive to selected functional forms. So, There are good reasons to use the Gini regression. Unfortunately, the Gini regressions existed only cross sectional and time series. Thus, in a second time, this thesis proposes to extend the Gini regression on the panel. It introduces within and between estimators, the individual effect test and the Gini Aitken estimator. Finally, this thesis presents empirical applications that illustrate the robustness of our estimators. She is particularly interested in the consequences of the estimation method and the sample section. It concludes that the growth process promotes poverty reduction when income inequalities are overcome. But also, the impact of agricultural growth on poverty reduction varies depending on the country's level of development.

Yes

A key challenge for the insurance industry is to charge each customer an appropriate price for the risk they represent. Risk varies widely from customer to customer, and a deep understanding of different risk factors helps predict the likelihood and cost of insurance claims. The goal of this project is to see how well various statistical methods perform in predicting bodily injury liability Insurance claim payments based on the characteristics of the insured customer’s vehicles for this particular dataset from Allstate Insurance Company.We tried several statistical methods, including logistic regression, Tweedie’s compound gamma-Poisson model, principal component analysis (PCA), response averaging, and regression and decision trees. From all the models we tried, PCA combined with a with a Regression Tree produced the best results. This is somewhat surprising given the widespread use of the Tweedie model for insurance claim prediction problems.

“Skills, Occupation Inequality and Development” is a theoretical study. There is no general agreement about how income inequality will affect development in the long run. Classic growth models show that income inequality is beneficial to development due to agent’s heterogeneity and marginal propensity to save increasing with wealth. Neoclassical growth models present that income distribution plays no significant role on development assuming representative agents and decreasing marginal returns in investment. New classical growth theory demonstrates that income inequality impedes growth due to credit constraints and indivisibility of investment in human capital. This paper studies income inequality through the channel of complementary skills and occupations in aggregate production. In a new classical model economy with two complementary occupations, CES production technology, skills in utility, and uncertainty of completing high-skilled occupations, we find a continuum of equilibria denoted by a correspondence between aggregate capital stock and the low-skilled population share regardless of the distribution in initial endowments. Aggregate capital stock and aggregate income per capita are non-monotonically related to the low-skilled population share. Aggregate income per capita will be maximized at a certain distribution of occupations on the continuum of equilibria. Therefore, the correlation between development and inequality of occupation distribution can be both positive and negative which depends on the position of occupation division on the continuum of equilibria. The correlation between low skills and occupation inequality is monotonic within a country, but the correlation is opposite between developed and developing economies. The low skills will move up on the continuum of equilibria if the occupation inequality is smaller (larger) in developed (developing) economies. The study also shows that inequality of the occupation distribution plays different effects in developed economies from those in developing economies due to the assumption that skills affect the completion of occupations. Developing economies also present two patterns of equilibria, in which one has higher optimum inequality of occupations, another one has lower optimum inequality of occupations. The cause of two patterns of equilibria for developing economies comes from the assumption of Cobb-Douglas production function. Shifts of equilibrium lead to new levels of development due to a change of inequality in other characteristics of the economy. “Fair Division of Income Distribution, Development and Growth: Evidence from a Panel of Countries” is an empirical exercise. I use an unbalanced panel data to explore the correlation between aggregate income per capita and income inequality. A lot of studies document controversial results using the Gini index or other summary measurements of income inequality. I measure income inequality by the two dimensions of a point on the Lorenz Curve, where the Lorenz curve has unit slope. It is called fair division point, which involves the fair population share and the fair income share. The difference between the fair population share and the fair income share approximates the Gini index of an income distribution. My analysis shows that a country’s low income population relatively decreases (the fair population share drops slightly) as the economy grows; and at the same time, those low income households are relatively worse off (the fair income share falls even though the GDP per capita increases). Inversely, as an economy becomes rich, there are more low income households (the fair population share increases), but those low income households are better off (the fair income share goes up and GDP per capita increases as well). Overall, both the Gini index and the difference between the fair population share and the fair income share have been increasing during the last half century in the panel of countries. Therefore, income inequality increases as an economy is getting richer. The analysis presents strong evidence for optimum income inequality regarding both the aggregate productivity and the growth rate of GDP, where income inequality is measured by either the Gini index or the fair division shares. But no evidence has been found for the Kuznets’ hypothesis. Both high and low inequality of income distribution could harm an economy as we compare with its potential optimum inequality. Also developed economies show different optimum inequality from that in developing economies, and there is the growth-worst fair population share that results in the lowest growth in developed economies. Measurement of income inequality matters on its economic effects for the subsamples of the panel data.

Tarptautinių atsiskaitymų banko (BIS) Bazelio II susitarimo nuostatos dėl bankų minimalaus kapitalo apibrėžia reikalavimus kredito rizikos skaičiavimui. Kredito rizikos vertinimo metodai leidžia naudoti vidines įmonių reitingavimo sistemas. Vienas svarbiausių reitingavimo modelio uždavinių – į modelį parinkti tokius finansinius ar nefinansinius rodiklius, kurie geriausiai klasifikuotų įmones pagal jų finansinio pajėgumo lygį. Populiariausias statistinis atrankos rodiklis yra tikslumo koeficientas dar vadinamas Gini indeksu. Tradicinis Gini indeksas buvo apibrėžtas 1914 m. ir iki šiol yra naudojamas pajamų nelygybei populiacijoje apskaičiuoti. 1995 m. Mosleris ir Koševojus pristatė k-matį Gini indekso analogą kaip zonoido tūrį. Šio darbo tikslas – naudojantis zonoidų teorijos idėjomis sukonstruoti apibendrintą reitingavimo modelių Gini indeksą. Pirmoje darbo dalyje pateiktos tradicinės Lorenco kreivės bei Gini indekso sąvokos ir Gini indekso apibendrinimai. Antroje darbo dalyje pagal BIS naudojamas reitingavimo modelio galios sąvokas, apibrėžtas reitingavimo modelio Gini indeksas. Be to, apibrėžti Lorenco kreivės apatinės ir viršutinės aproksimacijų Gini indeksai bei sudaryti šių indeksų apibendrinimai – normos bei tūrio daugiamačiai Gini indeksai. Pabaigoje analizuojamas atskirų finansinių rodiklių Gini indeksų stabilumas bei bendras Gini indeksų – vienamčio, normos ir tūrio – stabilumas ir pateikiamos išvados. / Bank for International Settlements (BIS) Basel II resolutions on banks regulatory capital include requirements for credit risk calculation. Credit risk evaluation methods define the possibility of using the internal rating system. One of the main tasks to build the powerful scoring model is to select financial and non-financial factors that appropriately classify companies according to their financial situation. The most popular statistical measure used for discriminatory analysis is the accuracy ratio or Gini index. General Gini index presented in 1914 is still widely applied to measure income inequality in the population. The k-dimensional analogue of Gini index as volume of zonoid was defined only in 1995 by Mosler and Koshevoy. The main purpose of this paper is to build the generalized Gini index of scoring model following the theory of zonoids. In the first part of the paper the usual Lorenz curve, traditional Gini index and its summary measures are presented. The second part presents the definition of the scoring models Gini index according to scoring model power measures applied in BIS resolutions. Furthermore the Gini indexes of Lorenz curve bottom and top approximations are defined and two its summary measures – norm and volume Gini indexes are constructed. Finally the stability of separate financial ratios Gini indexes and the general stability of univariate, norm and volume Gini indexes are analysed and final conclusions are presented.

Thesis (Ph. D.)--West Virginia University, 1999. / Title from document title page. Document formatted into pages; contains ix, 189 p. : ill. Includes abstract. Includes bibliographical references (p. 131-140).

The effect of income inequality has been controversial issue for decades, which researchers have concluded conflicting results. Many researchers have found that income inequality is conducive on economic growth, while others found harmful effect. Hence, this paper investigates the impact of income inequality on economic growth by using the cross sectional analysis. The averaged data from periods of 2002-2006 were used and observations from 90 developed and developing countries were also used. We find that income inequality is negatively associated in economic growth.

No description available.

Made available in DSpace on 2014-06-12T17:20:48Z (GMT). No. of bitstreams: 2 arquivo5942_1.pdf: 672563 bytes, checksum: 0f2745fcc7720dbf3b1c0c15f9e888cb (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / O objetivo desta dissertação é o de analisar a evolução do grau de desigualdade de renda em período recente da história, no qual houve um grande desenvolvimento e difusão da Tecnologia da Informação e Comunicação. Utilizando-se a metodologia de cálculo do coeficiente de Gini e considerando-se o Produto Interno Bruto - PIB e a população agregada de uma amostra com dezesseis países (nove membros da Organization for Economic Co-Operation and Development (OECD) e sete países da América Latina) comparam-se dois períodos da história: o anterior e o posterior a 1980. Analisou-se, criticamente, a literatura sobre crescimento econômico, inovação tecnológica e distribuição de renda e procurou-se demonstrar, por cálculo, que há uma correlação positiva entre o PIB dos países e o grau de utilização, por eles, dos recursos da tecnologia da informação. O estudo foi realizado a partir de duas bases de dados de fontes distintas, sendo a primeira com periodicidade trienal e abrangência entre 1950 e 1998, e a segunda com periodicidade qüinqüenal e abrangência entre 1970 e 2000, com extrapolação para 2002. Os dados relativos a estes dois períodos históricos permitiram a composição de duas combinações diferentes entre PIB agregado e população agregada em cada ano das amostras. Os resultados dos cálculos dos coeficientes de Gini, a partir dessas bases de dados, coincidiram no sentido de indicar uma mesma tendência ao aumento da concentração de renda no grupo de países analisados, a partir de 1980, período histórico em que a Tecnologia da Informação e Comunicação mais se desenvolveu e popularizou no mundo