This paper provides a methodological bridge leading from the well-developed theory of credit rationing to the less developed territory of empirically identifying credit constraints. We begin by developing a simple model showing that credit constraints may take three forms: quantity rationing, transaction cost rationing, and risk rationing. Each form of non-price rationing adversely affects household resource allocation and thus should be accounted for in empirical analyses of credit market performance. We then outline a survey strategy to directly classify households as credit unconstrained or constrained and, if constrained, to further identify which of the three non-price rationing mechanisms is at play. We discuss several practical issues that arise due to the use of a combination of “factual” and “interpretative” survey questions. Finally, using a data set from northern Peru, we demonstrate the importance of accounting for all three forms of credit constraints by estimating the increase in farm production that would result from relaxing credit constraints. The inclusion of transaction- and risk-rationed households in the constrained group results in an estimated impact that is twice as large as the impact when only quantity rationed households are considered constrained.

By shrinking the available menu of loan contracts, asymmetric information can result in two types of nonprice rationing in credit markets. The first is conventional quantity rationing. The second is 'risk rationing.' Risk rationed agents are able to borrow, but only under relatively high collateral contracts that offer them lower expected well-being than a safe, reservation rental activity. Like quantity rationed agents, credit markets do not perform well for the risk rationed. While the incidence of conventional quantity rationing is straightforward (low wealth agents who cannot meet minimum endogenous collateral requirements are quantity rationed), the incidence of risk rationing is less straightforward. Increases in financial wealth, holding productive wealth constant, counter intuitively result in the poor becoming entrepreneurs and the wealthy becoming workers. While this counterintuitive puzzle has been found in the literature on wealth effects in principal-agent models, we show that a more intuitive pattern of risk rationing results if we consider increases in productive wealth. Empirical evidence drawn from four country studies corroborates the implications of the analysis, showing that agents with low levels of productive wealth are risk rationed, and that their input and output levels mimic those of low productivity quantity rationed firms.