is particularly salient when looking at the limitations faced by small landholders. These limitations include: fragmented rural markets, imperfect information, the lack of production credit, price fluctuations, and factors that reduce competitiveness such as obsolete technology, risk exposure, lower human capital resources and management skills, and transaction costs associated with coordinating quality control or the processing of perishables (Carter and Barham 1996). In Ecuador the constraints indigenous peoples face when engaging in economic activity, in particular in terms of access to complementary assets such as education and credit, and when they work as farmers or entrepreneurs in the rural agricultural sector, are significant (Borja-Vega and Lunde 2006).

If one considers the fact that over the last decade there was little improvement in indigenous peoples’ living standards despite considerable increases in human capital levels, it appears that <u>discrimination</u> may be playing a role in lowering the returns to the assets of indigenous. In Hall and Patrinos (2006) it was shown that the unexplained portion of the earnings gap between indigenous and non-indigenous peoples was about 50 percent – but with considerable range between the five Latin American countries studied. This unexplained portion represents an estimate of potential discrimination, but is considered an _upper_ bound in the literature, as well as in Hall and Patrinos (2006), because there could be other unobserved factors, as well as prior or pre-market factors, that account for the differences in earnings.

For instance, we know that in the case of indigenous peoples in Latin America, indigenous students receive an inferior education and this is reflected in differential test scores that favor non-indigenous students. On average, indigenous children learn only about as much as three-fourths of what non-indigenous learn at the same grade level. Therefore, one more year of schooling (quantity) for indigenous peoples will not have the same impact on increasing earnings given the poor quality. Quality of education then, surely makes up some of the upper bound estimate.

Other factors that these studies – Hall and Patrinos (2006) included – are not able to control for, include unequal access to credit and finance, which we know are less accessible by indigenous peoples yet key for converting human capital into increased earnings (Patrinos and Skoufias 2007). Moreover, since most indigenous peoples are concentrated in agricultural work, then the poor quality and inferior size of their land holdings would serve to reduce returns to labor on such a poor asset. This is more or less confirmed in Hall and Patrinos (2006) since in the case of Bolivia they present a decomposition of earnings differentials for urban areas, and the estimate of discrimination (upper bound) is about half what is found for other countries. Finally, typical household surveys do not tell us anything about the motivation of indigenous workers. Are they earning less because of unequal pay, or are they choosing to work less for lower pay in order to satisfy other needs (for example, target worker theory)?

Yet, while it may be safe to conclude that an unexplained earnings gap would remain even if one were to be able to include all the factors mentioned above – some of them subject to pre-market or prior discrimination themselves, but that would require experimental research to confirm – it would almost certainly not be as high as 50 percent (or even 25 percent in the case of urban Bolivia). That is, there is still a lot that can be done to improve outcomes focused solely on the “explained” portion of the earnings differential. Clearly, human capital is more important than previously thought. Not only is it important on its own for productivity improvements, but it also makes other assets more productive. Again, the evidence points to a 14