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Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index

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  • Seema Jayachandran
  • Monica Biradavolu
  • Jan Cooper

Abstract

We propose a new method to design a short survey measure of a complex concept such as women's agency. The approach combines mixed-methods data collection and machine learning. We select the best survey questions based on how strongly correlated they are with a "gold standard'' measure of the concept derived from qualitative interviews. In our application, we measure agency for 209 women in Haryana, India, first, through a semi-structured interview and, second, through a large set of close-ended questions. We use qualitative coding methods to score each woman's agency based on the interview, which we treat as her true agency. To identify the close-ended questions most predictive of the "truth," we apply statistical algorithms that build on LASSO and random forest but constrain how many variables are selected for the model (five in our case). The resulting five-question index is as strongly correlated with the coded qualitative interview as is an index that uses all of the candidate questions. This approach of selecting survey questions based on their statistical correspondence to coded qualitative interviews could be used to design short survey modules for many other latent constructs.

Suggested Citation

  • Seema Jayachandran & Monica Biradavolu & Jan Cooper, 2021. "Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index," NBER Working Papers 28626, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28626
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    Cited by:

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    5. Caroline Krafft & Ragui Assaad & Isabel Pastoor, 2021. "How Do Gender Norms Shape Education and Domestic Work Outcomes? The Case of Syrian Refugee Adolescents in Jordan," HiCN Working Papers 361, Households in Conflict Network.
    6. Vijayendra Rao, 2023. "Can Economics Become More Reflexive? Exploring the Potential of Mixed Methods," Springer Books, in: Ashwini Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, chapter 14, pages 323-349, Springer.
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    8. Ingvild Almås & Orazio Attanasio & Pamela Jervis, 2024. "Presidential Address: Economics and Measurement: New Measures to Model Decision Making," Econometrica, Econometric Society, vol. 92(4), pages 947-978, July.
    9. Bjorkegren, Dan & Blumenstock, Joshua & Knight, Samsun, 2022. "(Machine) Learning What Policies Value," CEPR Discussion Papers 17364, C.E.P.R. Discussion Papers.
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    More about this item

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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