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In this paper, the short-term and long-term effects of macroeconomic variables on socio-economic indicators of agricultural sector, including price index, rural unemployment and gross domestic product (GDP) over the first to six government development programs were investigated. The auto-regressive distributed lag approach (ARDL) was used. The results showed that in the short run, inflation rate with a delay has a significant and negative effect on employment in the agricultural sector and increases the level of rural unemployment. The findings suggest that the effects of liquidity and inflation rate in the short run on the GDP are negative and government spending in the short run has a positive effect on the agricultural price index. In the long run, the effect of these variables on agricultural inflation is similar to the short term, but with the difference that the elasticities were larger. Resumo Neste artigo, foram investigados os efeitos de curto e longo prazo das variáveis macroeconômicas sobre os indicadores socioeconômicos do setor agrícola, incluindo índice de preços, desemprego rural e produto interno bruto (PIB) sobre os primeiros seis programas de desenvolvimento do governo. Foi utilizada a abordagem de atraso distribuído autorregressivo (ARDL). Os resultados mostraram que no curto prazo a taxa de inflação com atraso tem um efeito significativo e negativo sobre o emprego no setor agrícola e aumenta o nível de desemprego rural. Os resultados sugerem que os efeitos da liquidez e da taxa de inflação no curto prazo sobre o PIB são negativos e os gastos do governo no curto prazo têm efeito positivo sobre o índice de preços agrícolas. No longo prazo, o efeito dessas variáveis sobre a inflação agrícola é semelhante ao de curto prazo, mas com a diferença de que as elasticidades foram maiores.

Objetivos: a) Describir la metodología seguida en la construcción de un índice de privación por sección censal en ciudades, que permite identificar las secciones con situaciones socioeconómicas más desfavorables, y b) analizar la relación de este índice con la mortalidad general. Métodos: Se elaboraron diversos indicadores socioeconómicos (Censo 2001) correspondientes a las secciones censales de las ciudades de Barcelona, Bilbao, Madrid, Sevilla y Valencia. Se estudiaron sus correlaciones con la razón estandarizada de mortalidad (1996-2003), así como sus dimensiones conceptuales. Finalmente, mediante el análisis de componentes principales, se agregaron en un índice los indicadores seleccionados, usando como valores de peso las saturaciones correspondientes al primer eje. Resultados: Los indicadores que presentaron mayores correlaciones con la mortalidad general fueron los referidos a trabajo, educación, vivienda-entorno y hogares monoparentales. En el análisis dimensional de los indicadores aparece una primera dimensión que contiene los indicadores relativos a trabajo (desempleo, trabajadores manuales y eventuales) y educación (instrucción insuficiente total y en jóvenes). El índice elaborado con estos 5 indicadores recoge, en todas las ciudades estudiadas, más del 75% de la variabilidad de los indicadores que lo componen. Las correlaciones de este índice con la mortalidad muestran, en general, mayores valores que las obtenidas individualmente con cada indicador. Conclusiones: El índice de privación que se propone puede ser un instrumento útil para la planificación sanitaria al detectar áreas pequeñas de grandes ciudades con una situación socioeconómica desfavorable, que se relaciona con la mortalidad, y puede contribuir al estudio de las desigualdades sociales en salud en España. Objectives: a) To describe the methodology used to construct a deprivation index by census tract in cities, to identify the tracts with the least favorable socioeconomic conditions, and b) to analyze the association between this index and overall mortality. Methods: Several socioeconomic indicators (Census 2001) were defined by the census tracts of the following cities: Barcelona, Bilbao, Madrid, Seville and Valencia. The correlations with the standardized mortality ratio (1996-2003), and the dimensionality of the socioeconomic indicators were studied. Finally, the selected indicators were aggregated in an index, in which the results of the factor loadings from extraction of a factor by principal components were used as weighting values. Results: The indicators with the strongest correlations with overall mortality were those related to work, education, housing conditions and single parent homes. In the analysis of dimensionality, a first dimension appeared that contained indicators related to work (unemployment, manual and eventual workers) and education (insufficient education overall and in young people). In all the cities studied, the index created with these 5 indicators explained more than 75% of their variability. The correlations between this index and mortality generally showed higher values than those obtained with each indicator separately. Conclusions: The deprivation index proposed could be a useful instrument for health planning as it detects small areas of large cities with unfavorable socioeconomic characteristics and is associated with mortality. This index could contribute to the study of social inequalities in health in Spain.