expansion. Similarly, the modest upward trend of sales observed before 2002 that was associated with an increase in the number of transactions from 0.4 to 0.5 mn., accelerated significantly thereafter and in particular after 2005, reaching almost 1 mn. in 2007. This translates into an increase from about 0.5 to 1.3 transactions per 1,000 people and year.

A summary of average amounts for the main transaction categories by SRO, separately for before and after computerization based on SRO specific computerization dates, is presented in Table 1. Amounts of credit vary between urban and rural areas with Rs. 2 bn. and 0.08 bn., respectively on average. After computerization, credit is estimated to have doubled in both; from Rs. 1 bn. to Rs. 2.37 bn. in urban and from Rs. 0.05 bn. to Rs. 0.11 bn. in rural areas. Evidence on registered transactions is consistent with this in terms of much higher numbers in urban than in rural areas that are driven by sales but very little movement in mortgages. Registered sales almost doubled after computerization, from 3,922 to 5,636 in urban and from 1,203 to 2,390 in rural areas. By contrast, there is little change over time in the number of registered mortgages between rural and urban areas (637 vs. 697) and a slight increase, from 672 to 796, that appears to remain confined to urban environments.

# **3.2 Estimation strategy**

While the above changes are large, they may have been due to macro-economic conditions or locationspecific factors that were uncorrelated with CARD. In fact, influential observers have argued that, without more rigorous checks on data quality or process re-engineering, mere digitization of what may be largely outdated data from a very old system will have no economic benefit (Saxena 2005). Rigorous data on impacts of this intervention remains limited (Ahuja and Singh 2006) and whatever evidence is available focuses on rural land records rather than registration (Lobo and Balakrishnan 2002). To explore whether observed increases on credit supply are a causal effect that can be attributed to computerization, we exploit variation in the timing of this intervention across _taluks_ . The estimating equation is where _Cit_ is the log of credit disbursed in _taluk i_ at quarter _t_ ,<sup>7</sup> _γi_ s represent a full set of _taluk_ fixed effects to control for unobserved time invariant heterogeneity such as differences in _taluk_ -level endowments or economic structure that may affect the volume of recorded land transactions or credit disbursement, _θt_ represents quarter*year fixed effects to control for time varying factors such as common demand or policy shocks that may affect disbursement of credit or land transactions across all _taluks_ of the state. Standard errors are clustered at _taluk_ level throughout. We interact computerization with indicator variables for the amount of time passed since the intervention to explore whether potential

> 7 As the value of credit disbursed is likely to change proportionally we use logs. Value of credit also appears to be positively skewed whereas the log transformation is much more symmetrically distributed. Results are qualitatively similar when estimated in levels.

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