Some scholars have made the case for co-produced research, where questions are designed with the input of the communities being studied, members of the community are involved in data collection, and research results are then fed back to the community, arguing that this improves the “rigor, relevance and reach of science” (Balazs and Morello-Frosch, 2013). The Centers for Disease Control released a “Tools and Techniques” note outlining a method for a more limited form of participation – where research findings are shared via a process of two-way dialogue with marginalized communities, arguing that this builds trust and improves the dissemination of public health advice (McDavitt et al, 2016). Development economists have made a similar case for presenting results from randomized control trials to communities, though some have cautioned that this could affect the integrity of the research process (McKenzie, 2011).

With all this, the fact remains that it is rarely done. Research in economics – even research that displays a great deal of cognitive empathy - remains largely extractive, and any benefits to the respondents who participate (other than the payments for participating in a survey) is usually indirect; via the long route of research outcomes affecting policy which in turn may have a possible impact on respondents. Recently several projects around the world have been involved in efforts to “democratize data,” by involving people in designing, collecting and analyzing their own data tailored to answer their own needs (Ada Lovelace Institute, 2021). Yuen Yuen Ang (2020), building on Clifford Geertz description of ethnography as “thick description,” has called this “thick data.” In 2014 some colleagues and I co-produced a method, that we called Participatory-Tracking, with tribal communities in South India to facilitate community-level decision making by democratizing the design, collection, and analysis of data (World Bank, 2021). Representatives of over 200 tribal villages engaged in several weeks of deliberations to think about what constituted the good life for them, turned those ideas into indicators measured with survey questions and then tested and adapted those questions in their villages so that they did not take more than 30 minutes to answer. The questionnaires were quite different from those generally used to track poverty with only a 17 percent overlap in questions with the Indian National Sample Survey. We incorporated the community designed questionnaire into a tablet-based electronic survey and used a system of video-based training to train community representatives to use the tablet to conduct surveys of their own villages. In our pilot, which covered one district, we were able to conduct a census of 32,000 households in the district in about six weeks. Once the survey was conducted the data were dispatched directly to a cloud server to prevent anyone from tampering with them. The same exercise was repeated the following year.

The goal was for villagers to use the data for two purposes, to track changes in the quality public services and in their living standards, and to use the data to make better decisions in village meetings ( _gram sabhas_ ). This was challenging because levels of literacy were low (at about 65 percent), and numerical literacy was much lower. Thus, we could not use conventional data analytics and visualizations, and had to develop ways of showing the data that would be intelligible to someone who could not read or write. So, we co-produced data visualizations with the community, iterating a few times till we were confident that they were widely understood. These data were then presented at village planning meetings, including the village meetings described in earlier sections, and we found that they substantially helped improve the quality of discussion by allowing citizens and officials to focus on the issues rather than debate the facts. - (More information is available here <u>https://socialobservatory.worldbank.org/categories/democratizing data.)</u> Efforts such as participatory tracking, which are still in their infancy, harness technology to empower communities to conduct their own research designed to study their own priorities. Professional researchers are largely on the sidelines serving mainly as advisors and facilitators. The rise in data literacy around the world, and technology to facilitate data collection, mapping, and visualization, have made such efforts a promising line of action. In effect, we have come full circle – and returned to Charles Booth and 17