adhere to administrative processes that would help synchronize the two databases implies that registered deeds, if they exist at all, may be out of date.<sup>8</sup> This would eliminate any transaction cost effect from registry computerization. Third, other policy restrictions, in particular land ownership ceilings, imply that rural land markets are not very liquid and that therefore foreclosure may be associated with high transaction costs. Moreover, a history of often politically mandated agricultural debt cancellations may reduce banks’ confidence in their ability to recover rural debts through foreclosure in case of default.<sup>9</sup> In urban areas on the other hand, absence of land records implies that deeds registries will be the only source of data on land rights, land values will be higher, and better access to SROs will reduce total cost of registration compared to rural ones. Any fixed reduction of registration cost (e.g. due to elimination of informal payments) would thus result in a bigger relative decline of total registration cost, increasing the incentive to register. Interviews with banks suggest that, in light of the fact that underlying information is neither perfect nor conclusive, banks will still need to conduct detailed legal checks on applicants before disbursing credit. Still, online access to ECs provides lenders with low-cost access to information on existing encumbrances, in particular mortgages that can be used as a low-cost pre-screening of loan applications and thus reduce the cost of lending in urban areas.

# **4.2 Computerization as one factor affecting the number of land transactions**

Exploring the effect of computerization on number of transactions allows us to assess the plausibility of the credit supply effects ascertained earlier by looking at mechanisms that might underlie them. However, as transaction data is available only from a subset of SROs, the validity of arguments along this line will depend on the ability to demonstrate that SROs for which data are missing do not systematically differ from the rest in observable attributes so that results will not be drive by selectivity bias. Descriptive data for the two samples of SROs show no statistically significant differences in a range of census-based socio-economic characteristics and credit access between the two groups. In fact, results from repeating the credit-supply regressions from Table 2 for the restricted sample of SROs with transaction data in appendix Table A2 are consistent to those obtained earlier, with point estimates for all the coefficients being very similar. This suggests that selection bias is unlikely to be an issue.

Table 3 reports results, separately for sales in columns 4-6, mortgages in columns 7-9, and total number of transactions (i.e. including sales, mortgages, and non-monetary transfers) in columns 1-3. In line with what was found earlier, we note that, after controlling for time and SRO fixed effects, computerization is estimated to not have had any effect on either of these variables in the aggregate. At the same time, we find significant computerization effects on the number of total transactions in urban areas, with a point

> 8 Having changes recorded at the village level is easier for owners than traveling to _taluk_ headquarters but cases registries are often not updated. 9 While available data from AP do not allow us to empirically discriminate between these explanations, information from other states that have computerized records and registries and may have established village-based kiosks as well, could allow such testing.

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