Data submitted is based on farm surveys among 660 farmers in the Siriguppa taluk of Bellary district in the Indian state of Karnataka. The data includes four waves of surveys among same farmers over five years from 2012-13 to 2016-17. The first survey – baseline survey – was carried out in the first year of the project before the implementation of the experimental intervention. The intervention was on providing farmers with crop cultivation information ranging from land preparation to harvesting, information on credit and insurance, the price of inputs and outputs, etc. This intervention was carried out every year except 2014-15. After the baseline survey, two more surveys were carried out apart from the endline survey. These surveys retrospectively record information on several aspects of farming, social network, and household consumption. Abstract Recent years have witnessed renewed appreciation that agriculture could play a significant role in the pursuit of Millennium Development Goals. In this context, the role of information dissemination through information and communication technology (ICT) in improving rural welfare is highlighted. However, some fear that with ICT technological disparity will arise, and existing socio-economic inequality and poverty will be further exacerbated. This study will use randomised experiment and surveys before and after the experiment to investigate the impact of ICT on rural welfare in the Indian state of Karnataka. The two key aims of this project are: (1) to unravel the linkage between information access and agricultural growth, rural development, reduction of poverty, and income and social inequality; (2) to identify the role of ICT as a potential instrument of rural information and empowerment for inclusive growth. The randomised experimental methodology proposed here involves facilitating information access on key agriculture related services to households in some villages and not in others. Combining data from both surveys and the experiment, we investigate the impact of information dissemination on agricultural practices, household incomes, social network, risk coping mechanism and caste disparity. India's development priorities include poverty reduction and faster, more inclusive growth. Due to widespread rural poverty and high population growth, India must increase agricultural productivity. In the current debate among academics and policy makers on inclusive growth in India, there is a growing concern that poor people, especially in rural India, have benefited very little from rapid economic growth. Asymmetric information coupled with poor skill sets are considered the root cause, and inability of the rural poor to take advantage of opportunities in the markets, created by technology advancement and policy changes. Addressing the problem of asymmetric information is expected to empower the rural poor to take advantage of the market opportunities as well as overcome the skill set deficits in the long run, and therefore, enhances inclusiveness. The action research proposed in the current project using experimental methodology does precisely this - benefits the rural community directly, where e-governance facilities installed and access to range of information provided. The information will include both public and private services in the areas of education, health, agriculture, employment, financial inclusion, etc. These services will directly cater to the needs of the village inhabitants, local government as well as business. In recent years, there is a proliferation of government welfare programs for the poor to be delivered in the rural areas. But several of these services have not been delivered due to weak last mile organisational linkage. Proper design and use of the telecentres can help overcome this difficulty to a large extent and effectively reach the rural poor. With public access to information on these services, there can be some scope for transparency and lower corruption. Apart from directly benefiting the rural people, this project will inform the ongoing debate on some of the concerns raised.

For Siruguppa samples we applied the two-stage randomization procedure. From 25 GP in Siruguppa, 12 are randomly chosen and then are split equally into treatment and control. Randomization is done in Excel. A similar condition is applied in random sampling: none of the control and treatment GP should be neighbors. Figure 1 is a schematic depiction of the stages and the GP sample chosen in the process. In Siruguppa, we plan to have only one type of treatment viz. T2. Figure 4 depicts the location of 12 selected GPs in Siruguppa Taluka and their classification in treatment and control groups. Another interesting research question which can be addressed is the magnitude of spillover effect. It has been observed in the field that farmers often collect information from other farmers in the village. Thus, farmers may pass the information provided by e-SAP and our extension agent to others in the village and the recipients may benefit as well. To measure this indirect benefit, it is decided to take some additional farmers in each treatment GP. They will not receive any direct information from the project but they will be surveyed. The subsequent question was how would these additional farmers be chosen? Initially, it was thought that each selected GP, select four or five villages and for each village select farmers randomly. To capture internal (intra-village) spillover effect, for each treated village by selecting 2 additional farmers from the village itself. To capture external (inter-village) spillover effect, for each treated village select two more from outside the village. But, as GP in Siruguppa have a smaller number of villages (sometimes only one village) and they are bigger too. So, it is decided to select farmers randomly from the GP, ignoring his/her village of residence. Ideally, these farmers should be chosen simultaneously with the farmers receiving treatment. However, in practice, a randomly selected and identified farmer may refuse to respond to our survey. Considering that possibility, it was decided that those 10 farmers would be randomly selected after completing the baseline survey in a treatment GP. The data generated was based on recall method from household interviews using structured questionnaires.