It should be noted however

It should be noted, however that in the presence of lagged values of the dependent variables fixed effect models also violates the strict exogeneity assumption which states that xit it estrogen receptors is statistically independent of εit, for different time periods. This happens because one component of xit is y itself at an earlier point in time (Allison, 2009). The proposed solution to this problem is to to use the Arellano-Bond (1991) estimation method (denote by AB) which are presented below. For the reasons above, in the analyses that follow attention will paid, preferentially, to the results of FE and AB estimation methods. The generic specification of the model is:where —is the dependent variable in case for município i in year t. The dependent variables considered are, alternatively, the rates of growth of herd size, grazing ratio, cattle specialization ratio, and of the farm area of Brazilian municipalities in the inter-Census periods from 1975 to 1985, 1985 to 1995, and 1995 to 2005. —is the set of explanatory variables referring to the demographic, economic, social, and transport conditions in Brazilian municipalities the initial Census year of the respective growth period, namely, 1975, 1985 and 1995. Tables B1–B4 in Appendix B report the results of estimations of the ordinary leas square (OLS), seemingly unrelated (SURE), fixed effects (FE) and Arrellano Bond (AB) models, respectively. Table 3 below gives a summary presentation of these results listing the dependent variables as well as the acronyms of the estimation method in the top two rows and the explanatory variables in the first left column. Results are qualitatively summarized by indicating the insignificant, positive or negative effect of the variable in the rows by a zero (o), plus (+) or minus (−) signal, respectively, and the significance level of the estimated coefficient by the number of plus or minus signals according to the following rule: a zero signal when the estimated coefficient is not significant at 0.05, that is p>0.05; one minus or plus signal when p<0.05; two minus or plus signals when p<0.01; and three signals if p<0.001.
Policy options for sustainable development This section discusses policy options for a sustainable development of cattle ranching in Brazil. The first lesson to be drawn is that the extensive land use pattern as well as other inefficiencies of cattle raising in Brazil have deep and persistent economic and institutional roots. Land abundance – defined both in terms of relative factor availability and open access to land property – and high transport costs were major historical drivers of the extensive land use patterns of cattle raising in Brazil. These conditions are still pervasive in the Brazilian Amazon and to that extent the expansion of cattle ranching remains, by far, the most important source of deforestation in the region (Reis and Margullis, 1990; Chomitz and Thomas, 2000; Andersen et al., 2002; Chomitz and Thomas, 2003). The structure of incentives provided by the Brazilian institutional context impairs simple policy proposals to bring inefficient cattle raisers to the technological frontier (Schneider et al., 2000; Cohn et al., 2011; Assunção et al., 2013c; Strassbourg, s.d.; Strassbourg, s.d.). The problem becomes even more complex once we recognize the social and equity issues derived from the fact that cattle raising has always been and still is as one of the most traditional channels of economic and social mobility in agrarian economies, particularly for poor and small farmers. For those social segments, wealth or capital accumulation is practically synonym to increase in cattle herd. Furthermore, from and individual perspective, extensive cattle ranching is amply justified by the price incentives provided by cheap land and by the mining of unpaid natural resources (Rebello, 2004; Pacheco, 2009; Pacheco and Poccard-Chapuis, 2012). Fortunately, however, empirical results show that pasture intensification is not driven by factor price signals.