Runsinarith found significant impacts of mobile telephones e

Runsinarith (2008) found significant impacts of mobile telephones, electricity, irrigation and roads on poverty incidence through the application of quantile regressions in Cambodia for the year 2006. He concluded that mobile technology was the infrastructure with the highest impact on poverty reduction, followed by electricity, roads and irrigation. With the development of two infrastructure indexes (physical and social) created through the method of main components, Roy (2009) detected a strong negative correlation between the human poverty index and physical infrastructure (roads, electricity, irrigation projects, etc.) and social infrastructure (hospitals, schools, etc.) in India for the bcl-2 inhibitor 1981–2001. On the other hand, Ogun (2010) based on data related to the 1970–2005 period found that the development in social and physical infrastructure significantly reduced poverty in urban areas of Nigeria. Likewise, Datt and Ravallion (2002) estimated the determinants for differences in poverty reduction rates among Indian states for the period 1960–1994. One of the main facts is that State government expenses aimed at economic development exerted a great impact on poverty reduction, even when controlled by agricultural and non-agricultural productivity changes in a temporary tendency. In a more detailed study, Datt and Ravallion (1998) proved that the Indian states with the best infrastructures and human resources boasted significantly higher poverty reduction levels. Ghosh and De (2000), evaluated physical infrastructures in south Asian countries in the eighties and nineties, demonstrating that differentiated amounts of physical infrastructure were responsible for the growing regional disparity in southern Asia. By using a non-balanced panel of 121 countries from 1960 to 2000, Calderon and Serven (2004) applied quantitative infrastructure indexes and quality indicators that demonstrated positive and significant infrastructure stock effects on the income level and economic growth of these countries. These authors sustain that the development of infrastructure favors a better income distribution and consequently, a reduction in poverty levels. The national literature on the importance of infrastructure in the reduction of poverty is very rich. We can highlight the works of Cruz et al. (2010), who based on data obtained from 1980 to 2007 concluded that federal and state public expenses on education, health and physical capital (roads and energy) are extremely relevant for the income generation and productivity growth, which somehow allow to reduce poverty levels. In a multidimensional analysis of poverty in Brazil for the period from 1990 to 2004, using data from the National Research per Sample Domiciles (PNADs) Kageyama and Hoffmann (2006) verified that there was an improvement tendency in infrastructure conditions, being this trend largely responsible for the poverty reduction. Bertussi and Ellery (2012) investigated the impact of public expenses in transport based on the economic growth of Brazilian states between 1986 and 2007, using panel data. These authors verified that the public investment in the transport sector provoked a positive and statistically significant effect on the long-term economic performance of Brazilian states. Besides, this investment potentially contributed to the reduction of income inequality among different states. When analyzing the effects of infrastructure on Brazilian productivity from 1950 to 1995 from an empirical perspective, Ferreira and Malliagros (1998) estimated product and productivity elasticity with regards to the capital and the infrastructure investment. They estimated the unbundled impact of infrastructure expenses in five sectors (electricity, telecommunications, railroads, highways and ports) on the GDP and the productivity of private factors. Among the main results obtained, they verified a strong relation between infrastructure and the long term product, also corroborating that the total productivity of factors is not Granger-caused by productivity, but rather, the other way round.