emerge in these motifs are more likely to reflect contemporary realities. They find “a striking consistency between values derived from folklore and contemporary attitudes related to trust, risk-taking, and gender norms.” Thus, they can relate a society’s “ancestral” cultural heritage to its current norms and behaviors showing that narratives can demonstrate powerful path-dependency.

Another recent paper by Jayachandran, Biradavolu and Collins (2021) explicitly compares a reflexive, open-ended qualitative study on gender norms in the north Indian state of Haryana, to information collected using a structured questionnaire from the same communities. The main goal of the paper is to understand which questions on gender norms and women’s agency, widely used in the literature, best capture the lived reality of women. For these authors, qualitative work is the “gold standard” for understanding women’s agency so they begin with a careful qualitative study, where a team of researchers conduct open-ended interviews, and code these interviews to score 209 households across several domains by the degree to which women have independent agency. The authors treat this as a woman’s “true agency.” They then relate this score, using ML methods (Lasso and random forest) to find the best match between the qualitative score and outcomes measured in the structured questions to find an index of five measures of agency. This method builds on previous work by Blattman et al (2016) who also use open-ended interviews to build a validation method to understand the extent of measurement error in quantitative self-reported measures of sensitive questions about crime.<sup>6</sup> There is a greater element of reflexivity in this work, and the Folklore paper, because of their attempts to use ML and NLP to bring reflexively collected data (qualitative interviews in the case of the gender paper, and a close anthropological reading of folklore narratives in the Folklore paper) into conversation with other kinds of more conventional quantitative data, and thereby enrich our understanding of important phenomena. The degree of reflexivity is even more acute in two projects that are underway that are conducting open-ended interviews at scale and then analyzing using them ML and NLP.

The first study, which I am involved with, piggybacks on an ongoing panel survey of Rohingya refugees and their Bangladeshi hosts in Cox’s Bazaar in Bangladesh. We have been conducting 30-minute openended interviews with 2,000 respondents equally divided between hosts and refugees covering three domains – aspirations, well-being and “belongingness” to understand how the hosts perceive the refugees and vice versa. In the first paper out of this project we develop a method to extend a sub-sample of 400 human coded interviews to a much larger sample of documents using NLP (Ashwin et al 2022), thus attempt to circumvent the small sample bias associated with qualitative analysis. We apply the method to study parental aspirations for children, bringing in ideas from philosophy and anthropology to distinguish between “aspirations" ( a process of reversing a “core value" that results in a “change in the self") and “ambition" (“a specic goal that can be fully grasped in advance of achieving it"), and to include the concept of the “capacity to aspire" which is a cultural and cognitive resource that allows people to navigate their way to a better future. We find that there are interesting and policy-relevant differences between general and educational ambitions, and secular and religious aspirations. We also find that while poorer and less educated parents (more prevalent among the Rohingya refugees) say that budget constraints limit their ability to help their children achieve their hopes and aspirations for them, refugees are more able to express clearly articulated navigational capacity than hosts which we think may due to the selection and learning effects associated with surviving violence and finding their way to the refugee camp.

The second study, “The National Poverty Study” (Alexander et al, 2017) is based in the United States and is conducting a “qualitative census of urban and rural poverty” by interviewing 5,000 poor and near-poor respondents, sampled to be representative of poor households in in urban and rural settings in the United

> 6 See also this interesting effort to use video and audio images to visualize poverty in India: https://onehundredhomes.in/.

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