households report that they do not know where to report a corrupt act and 82 percent of the respondents state that not knowing where to report a corrupt act was an important reason for not denouncing.<sup>13</sup> <Table 4.4 around here> In Table 4.5 we provide the distribution of the dependent variable DIS1 by the different values of these governance characteristics. We observe that households that are discouraged are also ranking more institutions as dishonest. In particular, 49 percent of the discouraged households are ranking 17 institutions (the average) or more as dishonest out of 42, compared to 37 percent of households that are not discouraged. Average dishonesty has the same effect: discouraged households have lower average value of the honesty of the institutions. In terms of trust on state institutions, we observe that discouraged households trust less state institutions that households that have not been discouraged. We observe that the proportion of households that knows where to report a corrupt act is larger for discouraged users that for non-discouraged (47 percent versus 39 percent).

<Table 4.5 around here> The analysis at the agency-level uses information only from the public official survey to construct a measure of accessibility to public services – the dependent variable – and the governance indices –the explanatory variables. Table 4.6 provides the summary statistics for all these variables. The accessibility index varies from 17.5 - the lowest level which corresponds to Courts in Cuzco- to the highest accessibility score of 100 taken by 11 agencyregions (Peace Judges in Coronel Portillo, Popular Dining Room in Lima, Schools in Arequipa, etc.). The governance indices also display a large variation across institutions<sup>14</sup> . As for the other control variables, 22 percent of the agencies are municipal and 11 percent judicial. The regional distribution is 33 percent of the agencies in Lima, 21 percent in the jungle, 24 percent in the rest of the coast and 22 percent in the mountain range.

<Table 4.6 around here>

# **5. THE ESTIMATION MODEL**

At the estimation stage we choose to focus on the link between bad governance and access to service, leaving aside the link between bad governance and costs of the public services. To test our hypothesis – bad governance limits access to public services – we proceed in stages. We begin focusing on the user-level analysis to evaluate the effects of household characteristics and governance factors on discouragement to use public services. The unit of observation in this case is the user _i_ located in region _k_ .

> 13 We also check whether the sample is nationally representative: Table 4.4 discusses the summary statistics obtained from the sample compared with the corresponding statistics from the 1993 census data.  Our sample appears to be fairly representative of the Peru population, though it is slightly biased towards young people, while less educated people and single households are underrepresented.  It is worth noting that the survey interviewed households in 14 departments from a total of 25 departments in Peru. As can be seen in Table 4.4, the majority of the households was interviewed in Lima (46 percent), which is consistent with the high percentage of Peruvian households in Lima.

> 14 For a detailed description of the questions used, please see Appendix 1.

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