problems affecting the activity, and future plans. In addition, it contains questions about household characteristics (availability of services, other sources of income, wealth indicators, and dwelling features), land markets (farmers only), and incidence of adverse events (systemic and idiosyncratic). As for the enterprise survey, the questionnaire incorporates questions about formality and social capital indicators, exporting activities, employment, manager characteristics, purchases and sale of fixed assets, marketing, suppliers, training received, problems affecting the enterprise, future plans, and simplified financial statements. By combining household, business, and personal characteristics in a single survey, the data allows for a unique opportunity to examine the determinants of the demand and supply of credit, controlling for all those factors.

# **3. Classification of formal and informal lenders**

In the analysis that follows we differentiate between formal and informal lenders. Informal financial markets are prevalent in Mexico. For example, the World Bank (1995) finds that informal credit markets account for 87 percent of all credit transactions received by farmers and microentrepreneurs in three rural areas of Mexico. Moreover, formal and informal loans differ in important ways that have significant implications for demand and supply.

Table 2 summarizes the classification of the providers of loans that we adopt for this paper. Formal lenders include private banks, development banks, and regulated and unregulated nonbanks that have been allowed to operate under the auspices of a particular piece of legislation. Informal lenders comprise companies and individual entrepreneurs that offer loans outside the purview of regulations. They comprise moneylenders, input suppliers, traders, and agrobusinesses that provide loans with a profit motive. Furthermore, loans from friends and relatives or from partners are also classified as informal in this paper.

# **4. Measuring credit constraints**

Despite difficulties involved in measuring credit constraints without directly observing them, two main approaches have emerged to identify credit constraints. The first approach, named “indirect method,” attempts to evaluate the extent of credit constraints by estimating disequilibrium models of demand and supply of loans. These models face numerous difficulties in distinguishing supply from demand equations. In addition, empirical estimation of these models relies on complex estimation procedures. The second approach, dubbed “direct method,” relies on survey data with detailed questions about loan applications and the results of such applications. While this avoids the problems associated with disequilibrium models, working with qualitative responses implies another type of challenge. The level of credit constraints (by how much) is not easily measurable. Survey data allows for estimates of the proportion of individual entrepreneurs with a self-reported demand for loans, but not necessarily whether those individual entrepreneurs would be considered creditworthy clients for certain types of lenders. In this regard, the incidence of credit constraints would provide an upper-bound measure of the extent of true credit constraints because not all credit-constrained agents will be considered creditworthy by lenders.

The approach of this paper is to use the latter approach and infer the extent of credit constraints from the survey data. One of the advantages of the household and enterprise surveys collected in the current study is that they allow us to measure potential demand for loans and the extent of credit constraints by 6