monitor village public goods and common property resources. The villages were sampled, following a natural experiment, to be matched by language and ecology across neighboring districts, which belonged to linguistically different states. Prior to 1956 these matched districts were part of the same state. Since the villages matched across the paired districts, spoke the same language, had the same ecological structure, the same caste structure, and had 300 years of shared history, they belonged to the same linguistic community. Everything else equal, they should have, therefore, had the same patterns of public discourse.

However, we found that deliberative meetings were very different in different states – which, because of the natural experiment, we could attribute to state government policy in implementing the 73<sup>rd</sup> amendment. Thus, we were able to show that deliberation – or what we call “oral democracy” to distinguish it from the kinds of deliberation theorized about in western countries – was possible in poor, highly unequal, contexts with low literacy, and that the quality of deliberation was less related to levels of inequality or literacy, but determined by state government policy. Moreover, our discourse analysis found that social distinctions were largely equalized within the context of these meetings because village elites were concerned about losing votes by suppressing voice. Thus, individuals from lower castes were just as likely to speak as those from upper castes, and topics raised closely matched the interests of the median voter in the village (Ban, Jha and Rao, 2012).

What characterizes these three research projects is their relatively small-N, which reflects the difficulty of analyzing narrative data. The transcripts of 300 village meetings in India, for instance, took me and my co-author ten years (with about two years of active work) to analyze; a process that required deep reading, careful coding, reflection, debate, analysis and writing. This trade-off is limiting, and in the next section I will discuss the advantages and disadvantages of machine learning tools, and whether they could help us analyze narrative data at scale.

# C. <u>Machine Learning and Natural Language Processing</u>

The last decade has seen vast advances in using Machine Learning (ML) to analyze textual data, a field which is known as Natural Language Processing (NLP). ML and NLP pervade our everyday lives in ways that are seen and unseen – for instance they underlie how search engines work, target advertising from social media companies, filter spam emails, create virtual assistants to listen and respond to phone calls, and an infinity of other applications. The application of these methods by governments and firms have been shown to discriminate against minorities and women because they draw on training data sets that are largely populated by dominant groups and by men (Caliskan, 2021), which is not an indictment of the methods themselves but of how they have been used.

Researchers across a variety of fields, including Economics, have begun to use these tools to study a variety of questions (e.g. Gentzkow, Kelly and Taddy 2019). NLP vastly expands our ability make sense of high-dimensional textual data which opens whole new areas of research in the social sciences. In particular, it has the potential to revolutionize mixed-methods research by making very large amounts of narrative and textual data amenable to statistical analysis. The question is whether this can bring more reflexivity to economic analysis.

To answer this question, it might be helpful to contrast the “hand-made” analysis of village meetings discussed in the previous section, with a study that I was involved in (Parthasarathy et al, 2019) that used NLP methods to analyze 100 transcripts of village meetings recorded and transcribed in the Indian state of Tamil Nadu in 2014 (and, consequently, took six months rather than ten years to analyze). The NLP paper studies the degree of gender bias in the village meetings and whether having a woman village president can correct gender bias. The transcripts are analyzed using “topic-models,” a method which 12