This is where randomised evaluation comes handy. To measure the possible impact, we could initially select a set comprising, say, 100 panchayats located in one or a couple of similarly situated districts. These panchayats would then be randomly assigned to two groups — the treatment group and the control group. In the control group, the existing labour:material ratio may be continued whereas the panchayats in the treatment group would be allowed to spend more on the material component if they see fit. It is important to collect baseline data initially and monitor the process so that the integrity of the experiment is not compromised. Then, after a suitable time period, follow up data (on the quality of roads or other assets created under NREGA) would need to be collected for both the groups of panchayats in identical ways. This data can then be used to estimate the impact of the intervention and to assess whether such impact is statistically and practically significant.

A similar experiment could easily be designed for including other basic skills like carpentry, masonry, welding and even data entry in the NREGA domain. For example, data entry could conveniently be dovetailed with the National Rural Health Mission (NRHM) or Sarva Shiksha Abhiyan (SSA) to create local panchayat level databases on immunisation of infants, expecting and lactating mothers (under NRHM) and attendance-cum-performance tracking of school children (under SSA). It is interesting to note that the wages paid by the private sector for basic data entry are similar to the wages paid for unskilled manual labour under NREGA. However, in case of data entry, there is a distinct incentive to upgrade skills to move to the next wage stratum.

... contd.