sold outside the municipality or through an informal trader (coyote), and type of production (crops, livestock, and crop/livestock subproducts).

For enterprises we include a set of variables relating the personal characteristics of the manager, such as gender, education, and ownership relationship, with the enterprise (main shareholder). Moreover, we include characteristics of the enterprise’s size, such as value of average assets and dummy variables for number of workers (large, medium, and small).<sup>16</sup> Other characteristics of the enterprise include: export, formality indicators (registration with government), social capital (membership with guilds), training, information technology (Internet), and acquisition of new technologies.

For both individual entrepreneurs and enterprises, economic conditions in the municipality (state for enterprises) are captured by log of population, the share of agricultural workers, and the number of different types of lenders (individuals only). These variables control for agrarian conditions, market size, and infrastructure availability. The model also includes a set of region dummy variables to capture regional differences in both climate and infrastructures, both of which affect the demand and supply of loans. The availability of loans, and hence their opportunity cost, may vary with municipality size and other characteristics of the municipality. The number of different types of lenders in the municipality captures proximity to lenders, and financing costs.

Demand identification variables ( _Z_ ). A crucial part of this model is the “identification” assumption—the assumption that some variables influence one equation but not the other. Fortunately, the richness of the survey allows us to make such assumptions. We argue that some of the characteristics of individual entrepreneurs and enterprises that are available in the survey are likely to be correlated with their demand for loans, but unlikely to be observable by lender and therefore would not influence the lender’s decision on granting the loan.<sup>17</sup> Since these characteristics are not influencing the lender’s decision on access, they are not likely to enter in the “access” (or supply) equation.

We grouped the “identification variables” in four categories. Below we discuss each of the variables and the intuition for their selection. In Table 5 we present a univariate t-test for our demand identification variables that helps to separate agents with demand and without demand for loans. Indeed, we find that most demand identification variables are statistically different for agents with demand and those without demand.

The first category captures the _entrepreneurial abilities_ of the individual; those with better abilities will likely to have more demand (for example, for expanding and improving the business). Such variables include (a) an indicator of whether the individual has taken preliminary steps before starting the business (which shows their enthusiasm and the level of preparation); and (b) whether they have inherited a business or a farm (those who did are less likely to have entrepreneurial spirit than those who started the business on their own accord). We find that these two variables increase demand for loans.

The second category captures the _need for funds_ . Farmers and microentrepreneurs may need funds because of adverse situations (shocks), or because the business is expanding. For individual entrepreneurs we include indicators of the incidence of adverse events, such as death or illness of household members, loss of income, or climatic shocks that occurred in the last three years. For enterprises we use indicators

> 16 The survey design stratified enterprises by number of workers, including four categories: large, medium, small, and micro. See INEGI (2002) for details.

> 17 In addition we need to assume that people who expect to be rejected do not apply for loans. This is a reasonable assumption given our observations in the previous section.

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