4 Work using macro data are, however, less conclusive on a possible association between poverty and access to finance or job opportunities. Beck et al. (2007), Honohan (2004), Jalilian and Kirkpatrick (2002), and Singh and Huang (2011) find that the degree of financial intermediation has a strong positive impact on the income of the poor. Jalilian and Kirkpatrick (2002) use the ratio of bank assets to GDP to measure financial intermediation in a sample of advanced and developing economies. Beck et al. (2007) and Honohan (2004), looking only at developing countries, capture the role of finance by considering private sector credit. Singh and Huang (2011) also use private sector credit, but with a sample covering only Sub-Saharan African countries.

In contrast, Dollar and Kraay (2002) find that financial development does not affect the poor. They examine the relationship between the average income of the poorest quintile in a sample of advanced and developing economies, and measure financial depth using the ratio of commercial bank assets to total bank assets. Kraay (2004) reaches similar results studying the association between the change in absolute poverty and the ratio of M2 to GDP in a sample of developing countries.

Finally, Guillaumont-Jeanneney and Kpodar (2011) suggest that the poor benefit primarily from the ability of the banking system to facilitate transactions and provide savings opportunities rather than reaping the benefit of greater access to credit. Looking at a sample of developing countries, they find a positive relationship between financial development and poverty if financial development is measured by the ratio of M3 to GDP. If private credit is used instead, the association turns out to be statistically insignificant.

# **B.   Banks, markets and the institutional environment**

Financial institutions operate in settings where complete information is often not available. Entrepreneurs seeking financing normally have more information about their projects than their banks do. In this setting, from the viewpoint of a financial institution projects that may have different probabilities of success are indistinguishable. This information asymmetry requires banks to screen applications so as to grant loans only to the most promising projects (Singh, 1992).

The lender cannot rely simply on increasing the interest rate, however. As Stiglitz and Weiss (1981) demonstrated, increases in the interest rate charged on loans may adversely affect the composition of the pool of borrowers. The expected return to the lender depends on the probability of repayment, so the lender would like to be able to identify borrowers who are more likely to repay. Those who are willing to borrow at high interest rates, however, may be riskier: they are willing to borrow at high interest rates because they perceive their probability of repaying the loan to be low. For a given expected return, an increase in interest rates will induce low-risk projects to drop out first, leaving only the riskier ones in the pool.

Lenders could require collateral, which imposes a cost if the entrepreneur defaults. As the probability of failure is greater for high-risk projects, the same amount of collateral will reduce the expected profit of these projects by more than that of less risky ones. Bester (1985) demonstrated that lenders could design attractive contracts adapted to the various qualities of borrowers, leading to perfect sorting.