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Abstract(s)
Este Trabalho Final de Mestrado teve como objetivo proceder a uma análise da produtividade na produção de hortícolas dos países da União Europeia 27 (UE-27) no período 2004-2012.
A metodologia aplicada teve por base o cálculo, em folha Excel, de índices TFP no tempo e no espaço e fez uso da base de dados FADN e do Eurostat. O índice TFP é uma medida de produtividade marginal. No tempo, medem-se flutuações de um ano para o outro. No espaço, medem-se flutuações de cada país em relação à média da UE. A análise foi seguida de uma análise Cluster feita em SPSS, onde se distinguiram grupos de países de acordo com os índices TFPt e TFPs médios obtidos. Por fim foi corrida uma regressão em STATA, de forma a perceber que variáveis influenciam as variações de TFP.
Os resultados mostram que os países com índices TFPt maiores e/ou maior evolução são a Hungria, a Letónia e a República Checa. Os países com índices TFPs maiores e/ou maior evolução do TFP são a Itália, a Espanha e a Lituânia.
Com a análise K-means Cluster foi possível identificar quatro grupos de países que se distinguem pelo índice TFPs. Sendo estes por ordem decrescente o grupo 1 (Grécia, Espanha, Itália e Lituânia), 2 (Bélgica, Hungria, Polónia e Reino Unido), 3 (Bulgária, Dinamarca, Alemanha, França, Letónia, Holanda, Portugal e Suécia) e 4 (República Checa, Malta, Roménia e Finlândia).
Com a regressão foi possível constatar que para o índice TFPt as variáveis significantes são a crise, a percentagem de SAU em estufa, a intensidade da produção em capital e o tempo sendo que apenas esta última afecta de forma positiva. No índice TFPs, todas as variáveis significativas (intensidade da produção em capital, trabalho familiar e tempo) afectam negativamente.
Palavras-chave: Benchmarking, Índice TFP, Produtividade.
The objective of this Master’s final paper was to analyze the productivity in terms of vegetable production in the European Union 27 (EU-27) countries during the years between 2004 and 2012. The methodology adopted consisted in using FADN and Eurostat databases to compute a Total Factor Productivity (TFP) index in terms of time and space. The TFP index is a measure of marginal productivity. In time, it measures fluctuations in relation to the previous year, while in space, it measures fluctuations of each country in relation to the EU average. Subsequently, the analysis was carried out using the SPSS software through a Cluster analysis that allowed to group the different countries given their average TPFt and TPFs indexes. Lastly, STATA was used to run a truncated regression to understand which variables influence the TFP variations. Results showed that the countries with greater TFPt indexes and/or higher levels of development are Hungary, Latvia and Czech Republic, whilst for TFPs these countries are Italy, Spain and Lithuania. Using the K-means Cluster analysis it was possible to identify four groups of countries that distinguish themselves by their TFPs indexes. These are, by descending order, Group 1 (Greece, Spain, Italy and Lithuania), Group 2 (Belgium, Hungary, Poland and the United Kingdom), Group 3 (Bulgaria, Denmark, Germany, France, Latvia, Netherlands, Portugal and Sweden) and Group 4 (Czech Republic, Malta, Romania and Finland). According to the regression it was possible to conclude that for the TFPt index, the significant variables that explain its variation are the crisis, the percentage of SAU in greenhouses, the capital and the time, being the last the only one that affects positively the index. In what concerns the TFPs, the most significant variables are capital, family work ratio and time and they all affect negatively the index.
The objective of this Master’s final paper was to analyze the productivity in terms of vegetable production in the European Union 27 (EU-27) countries during the years between 2004 and 2012. The methodology adopted consisted in using FADN and Eurostat databases to compute a Total Factor Productivity (TFP) index in terms of time and space. The TFP index is a measure of marginal productivity. In time, it measures fluctuations in relation to the previous year, while in space, it measures fluctuations of each country in relation to the EU average. Subsequently, the analysis was carried out using the SPSS software through a Cluster analysis that allowed to group the different countries given their average TPFt and TPFs indexes. Lastly, STATA was used to run a truncated regression to understand which variables influence the TFP variations. Results showed that the countries with greater TFPt indexes and/or higher levels of development are Hungary, Latvia and Czech Republic, whilst for TFPs these countries are Italy, Spain and Lithuania. Using the K-means Cluster analysis it was possible to identify four groups of countries that distinguish themselves by their TFPs indexes. These are, by descending order, Group 1 (Greece, Spain, Italy and Lithuania), Group 2 (Belgium, Hungary, Poland and the United Kingdom), Group 3 (Bulgaria, Denmark, Germany, France, Latvia, Netherlands, Portugal and Sweden) and Group 4 (Czech Republic, Malta, Romania and Finland). According to the regression it was possible to conclude that for the TFPt index, the significant variables that explain its variation are the crisis, the percentage of SAU in greenhouses, the capital and the time, being the last the only one that affects positively the index. In what concerns the TFPs, the most significant variables are capital, family work ratio and time and they all affect negatively the index.
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Keywords
Benchmarking TFP índex Productivity Índice TFP Produtividade