Este trabalho pretende contribuir para a ampliação do debate em torno da trajetória dos ganhos de produtividade no Brasil, examinando o desempenho da produtividade total dos fatores (PTF) agregada no período 1987/2002, nas unidades da federação, através da estimação de intervalos de confiança para o índice de Malmquist e suas componentes variação de eficiência e taxa de progresso tecnológico. Para tanto, aplica-se a metodologia de bootstrap, conforme Simar e Wilson (1999a) aos índices obtidos pela técnica DEAMalmquist, de modo que se os intervalos de confiança – com probabilidade de 90% e 95% - contenham a unidade, o índice em questão não é significativamente diferente de 1 e, portanto, não é possível concluir que existam mudanças na PTF, na eficiência ou na tecnologia. Com a aplicação do bootstrap, a conclusão mais evidente é que se deve ter cautela na análise e comparação entre unidades produtivas através do mero exame dos índices de Malmquist calculados. Em alguns casos, a técnica corrobora os resultados encontrados através do índice, mas em outros se conclui que não se pode afirmar que as variações sejam, de fato, estatisticamente significantes. / This tesis estimates the confidence intervals for output oriented Malmquist indices of productivity and their decompositions – changes in efficiency and changes in technology – in the period 1987/2002 taking into consideration Brazilian States. For this study, a bootstrap algorithm described in Simar and Wilson (1999a) was used, this procedure is a consistent estimation for obtaining confidence intervals for Malmquist indices. So the purpose is to contribute for further debates on total factor productivity performance in Brazil, providing results for both 90% and 95% confidence intervals. They allow assessment of the null hypothesis of no total factor productivity change which indicates that the corresponding measures are not statistically different from unity. The interpretation is straightforward. In the confidence interval case, if it contains the unity, then the corresponding measure is not significantly different from one at the significance level, i.e., it is not possible to conclude that changes occurred in productivity. In contrast, when the interval excludes the unity, one can conclude with confidence that the corresponding measure is significantly different from unity. With the application of bootstrap methodology, it is possible to conclude the necessity of being cautious while analyzing and comparing productive units through the mere exam of the calculated Malmquist indices. In some cases the technique corroborates the results found through the indices, whereas in others it may be concluded that it is not possible to state that variations are, in fact, statistically significant.

Este trabalho visa estudar a evolução da produtividade dos bancos brasileiros ao longo dos anos de 2000 a 2014, analisando o período pré e pós 2003, quando iniciou-se a introdução de reformas institucionais que afetaram o setor bancário, dando base à sustentabilidade do crescimento do crédito na economia e fortalecendo a estrutura das operações tanto à pessoa física quanto à pessoa jurídica. O estudo é introduzido através da estimação de funções de produção e obtenção de medidas de produtividade total dos fatores (PTF) para 86 bancos individuais, empregando as técnicas de Levinsohn e Petrin (2003) e de Ackerberg, Caves e Frazer (2015) para controlar problemas de endogeneidade e colinearidade que surgem por conta da variável não observada (produtividade) e da escolha dos insumos. Os bancos foram divididos em três grupos: (i) intensivos em operações de crédito, utilizando como critério a razão de operações de crédito/total do ativo maior ou igual a 50% no período anterior às reformas; (ii) não intensivos em operações de crédito, reunindo bancos com a razão anteriormente mencionada menor que 10%, tendo como premissa que esse conjunto não é afetado diretamente pelas reformas; e (iii) os cinco maiores bancos do sistema financeiro brasileiro em relação ao ativo total. Os resultados mostram que (i) a produtividade agregada dos bancos oscilava sem direção definida no período anterior às reformas, mas mostraram um crescimento após as reformas; (ii) bancos intensivos em operações de crédito apresentaram ganho de produtividade de 7,0% a.a. após as reformas, totalizando 110,7% no período de 2004 a 2014, o que representa um crescimento bem mais expressivo que os 4,3% a.a. para os bancos não intensivos, totalizando ganho de 58,9% após as reformas; e (iii) maior realocação do produto de bancos menos produtivos para bancos mais produtivos durante a crise financeira mundial de 2008. / This paper aims to study the productivity of Brazilian banks from 2000 to 2014, analyzing the period before and after 2003, when began the introduction of institutional reforms affecting the banking sector, supporting the sustainability of credit growth in the economy and strengthening the structure of operations both to individuals as to legal entities. The study is introduced by estimating production functions and obtaining the total factor productivity (TFP) for 86 individual banks, implementing the methodology proposed by Levinsohn and Petrin (2003) and Ackerberg, Caves and Frazer (2015) to control problems of endogeneity and collinearity that arise due to the unobserved variable (productivity) and the choice of inputs. Banks were divided into three groups: (i) intensive in credit operations, using as criteria the ratio of credit operations/total assets greater than or equal to 50% in the period before the reforms; (ii) non-intensive in credit operations, bringing together banks with aforementioned ratio less than 10%, with the assumption that this group is not directly affected by the reforms; and (iii) the five largest banks in the Brazilian financial system in relation to total assets. The results show that (i) the aggregate productivity of banks fluctuated without a defined direction in the period before the reforms, but showed growth after the reforms; (ii) intensive banks in credit operations showed productivity gains of 7.0% (compound annual growth rate) after the reforms, totaling 110.7% from 2004 to 2014, which represents a much more significant growth than the 4.3% per year for non-intensive banks, totaling a gain of 58.9% after the reforms; and (iii) higher relocation of the product from less productive to more productive banks during the global financial crisis in 2008.

This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a spatial panel data model that controls for the spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important spatial dimension to the discussion, by showing that productivity effects of knowledge spillovers increase with geographic proximity. (authors' abstract)

This thesis explores three important factors that have been central to the pursuit of economic growth, particularly in the developing and emerging economies. These are Outward Foreign Direct Investment, Reverse Technology Spillovers, and Total Factor Productivity. Chapter 2 examines whether India’s Outward Foreign Direct Investment (OFDI) pattern is consistent with Dunning’s Investment Development Path (IDP) sequence using macro data over the period 1980-2010. It tests whether the level of development - proxied by GDP per capita - is the main factor explaining OFDI, and augments the IDP by studying other major determinants such as exports, Inward FDI, human capital, and R&D using the Cointegration and Error Correction Model techniques. The results support the main proposition of the IDP, but also highlight the importance of other factors. We also find that OFDI granger-causes R&D, suggesting a possibility of reverse technology spillover. Chapter 3 analyses the ‘feedback effect’ of Foreign Direct Investment (FDI) on Total Factor Productivity (TFP) growth of emerging economies via technology spillovers across borders. We study the effect of R&D spillovers resulting from Outward FDI flows from 18 emerging economies into 34 OECD countries over the 1990-2010 period, comparing the impact with that of spillovers resulting from Inward FDI flows. The result confirms that FDI enhances productivity growth in the home country; however the impact is much larger when R&D-intensive developed countries invest in the emerging economies than the other way round. The country-specific bilateral elasticities also support this outcome. Finally, Chapter 4 studies twofold stages of OFDI – determinants and effects – at a disaggregated level, using data on OFDI undertaken by 34 countries in 10 major sectors of US during 1990-2010. The main aim of this essay is to provide micro evidence in support of outcomes of Chapter 2 & 3. The first stage concentrates on the driving forces of OFDI to understand its macroeconomic determinants, by distinguishing the factors into 3 broad categories: country specific, sector specific and time specific variables. In the second stage, we then study how the home countries benefit from the OFDI that they undertake in the US, in terms of the impact of induced reverse technology spillovers. This stage entails the creation of a foreign R&D capital term as the weighted average of R&D intensity of US with the OFDI undertaken by the home countries into US. It investigates both direct and interaction effects of such R&D spillovers on the growth of home country’s TFP. The analysis also considers a lag structure to allow for a time lag in the transfer and effect of foreign R&D capital. Results for both the stages confirm the set hypotheses.

This dissertation analyzes productivity changes connected with inflow of foreign direct investment. Specifically indirect effects of foreign direct investment (spillovers) on firms in the Czech Republic in period 2000 -- 2006 are analyzed. As a proxy for productivity spillovers is used total factor productivity, which is retrieved by implementing the Levinsohn -- Petrin technique, which control the simultaneity bias in the production function. Using firm-level data, obtained from Amadeus database, a significant spillovers effects of foreign direct investment on productivity were identified -- a stronger positive impact on upstream sector and weaker negative impact on downstream sector.

Agricultural research is susceptible to research deterioration due to biological, climatic, and economic forces. Research deteriorates as the base conditions it addresses change which leaves the resulting information or technology less effective, efficient, productive, and/or relevant. Maintenance research targets deterioration in an attempt to prevent any loss of previous gains. Maintenance research is in contrast to productivity enhancing research which attempts to increase efficiency or productivity beyond previously attained thresholds. In 1986, Adusei and Norton conducted a survey of agricultural scientists across the United States to measure the amount of commodity based agricultural research devoted to maintenance research (1990). They discovered roughly 35% of all agricultural research related to commodities was spent on maintenance research. A follow-up survey was conducted in 2008 to see if the proportion of maintenance research engaged in agricultural research had risen. In this survey, the amount of maintenance research in non-commodity based agricultural research was also measured. The percentage of agricultural commodity research engaged in maintenance research was found to have risen to roughly 41%. In contrast, the percentage of maintenance research in agricultural non-commodity research was found to be roughly 29%. An empirical model was developed to explain maintenance research expenditures. Agricultural research funding, climatic conditions, land degradation, pest and pathogen control, and agricultural production were thought to influence maintenance research expenditures. From these five categories, seven representative variables were included in the model. The model found each category except land degradation to have a statistically significant impact on maintenance research expenditures. / Master of Science

Considerando as micorregiões do Censo Agropecuário de 1995-1996 e dos Censos Demográficos de 1991 e 2000, este trabalho estima, primeiramente, uma função de produção Cobb-Douglas. Para tal, controla-se o efeito dos preços regionais sobre o valor da produção. São levados em conta fatores tradicionais (mão-de-obra, terra, insumos químicos e capital) e o capital humano (nível de escolaridade formal e conhecimento de mercado). A estimativa foi obtida através do método de mínimos quadrados em três estágios – MQ3E. O método se justifica pelo modelo econômico proposto ser um sistema de equações (estima-se também a demanda por capital e o custo relativo da utilização deste fator) e pela forte presença de heteroscedasticia na estimação em mínimos quadrados ordinários – MQO. Em seguida, estima-se a Produtividade Total dos Fatores – PTF por estado, verificando-se a contribuição relativa do capital humano. / Using data from the Agricultural Census of 1995-96 and the Demographic Censuses of 1991 and 2000, this dissertation first estimate a Cobb-Douglas production function. The value of production is calculated in order to control the effect of regional prices. Traditional factors (labor, land, fertilizers and capital) and human capital (years of schooling and market knowledge) are used as explanatory variables in the production function. The economic model is a simultaneous equations system that considers both decisions to produce and maximize profits. Results of the proposed economic model are obtained by using the three-stage least squares (3-SLS) method due to presence of heteroskedasticity. Afterwards, total productivity factor (TFP) is estimated to each of the Brazilian states and the relative contribution of human capital is analyzed.

Made available in DSpace on 2010-04-20T21:00:16Z (GMT). No. of bitstreams: 3 marcelofatioturma2004.pdf.jpg: 16086 bytes, checksum: 5090b863d17b5ef434673e8759006297 (MD5) marcelofatioturma2004.pdf: 214712 bytes, checksum: 0f0a15eb1acc24638d6ff471a67fc55b (MD5) marcelofatioturma2004.pdf.txt: 66846 bytes, checksum: 685e8939ce9226930820f666f977e5bb (MD5) Previous issue date: 2007-03-13T00:00:00Z / O presente trabalho utiliza a 'Contabilidade do crescimento' para analisar e explicar as diferenças nas taxas de crescimento do PIB per capita dos países Brasil, Chile, China, Índia e Coréia no período compreendido entre os anos 1960 e 2000. Descrevendo os quatro fatos estilizados do crescimento econômico, a 'Contabilidade do crescimento de Solow', bem como a função de produção Cobb-Douglas, buscou-se dar o embasamento teórico para o modelo utilizado de fato no presente trabalho, que decompôs o crescimento dos diferentes países para identificar qual fator mais contribuiu ou quais fatores de produção mais contribuíram para os diferentes níveis de crescimento econômico dos países analisados. A metodologia utilizada no trabalho baseia-se em pesquisas bibliográficas, que visam primordialmente a fundamentação conceitual e teórica de alguns conceitos utilizados e em pesquisas às diferentes bases de dados históricos referentes aos países e variáveis analisadas. Pode-se afirmar que as principais fontes de consulta foram a 'Penn World Table' da Universidade da Pensilvânia e o Banco Mundial. O estudo irá demonstrar, além dos diferentes níveis de cada um dos fatores (capital humano, físico e progresso tecnológico ou 'TFP – Total Factor Productivity' ) nos países, como cada um desses fatores evoluiu ao longo dos anos e qual a contribuição de cada um nas taxas de crescimento do PIB per capita de cada um dos países analisados. É feito um estudo da variância do crescimento do PIB per capita, onde ficará claro que boa parte das diferenças apresentadas nas taxas de crescimento dos países vem do progresso tecnológico ou da covariância dos fatores, que são progresso tecnológico e o agrupamento do capital físico e humano. Também verificou-se a correlação existente entre a variação do PIB per capita e as variáveis que o compõe, permitindo a visualização do alto grau de correlação existente, principalmente com o progresso tecnológico ou 'TFP'. / This study applies the “Growth Accounting” to Brazil, Chile, China, India and Korea, intending to explain the differences in the GDP per capita growth rates of these countries from 1960 to 2000. Describing the four “Stylized Facts” of Economic Growth, the “Solow Growth Accounting” and also the “Cobb-Douglas” Production Function, a theoretical support for the model was used in the present study, which segmented the different growth rates for the focused countries in order to identify which factor or which factors have contributed more for their different levels of economic growth. The methodology is based on bibliographic research that provided the theoretical support for the analysis and also on historical data research for the countries that are part of this study. The main sources of data were the World Bank and the “Penn World Table” provided by the University of Pennsylvania. The study will demonstrate the different levels of each production factor (human and physical capital and technological progress or “TFP – Total Factor Productivity”) country by country, the evolution of these factors in the period and also how each factor has contributed for the growth of the GDP per capita of the focused countries. Through a variance analysis of the rate of GDP per capita growth, it can be shown that the technological progress and the covariance between technological progress and a composition of physical and human capital, are the main sources which explain the differences in the rates of economic growth. Furthermore, the correlation among the changes in the GDP per capita and the variables which compose that figure that were analyzed, allow for the perception of a high degree of correlation, especially between the GDP per capita growth rate and the “TFP – Total Factor Productivity”.

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. We provide an econometric derivation of the relationship and show that the presence of latent/unobservable regional knowledge capital leads to a model relationship that includes both spatial and technological dependence. This model specification accounts for both spatial and technological dependence between regions, which allows us to quantify spillover impacts arising from both types of interaction. Sample data on 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)