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Data Science for Public Policy Case of Aspirational Districts Program S ( Subu ) V Subramanian, PhD Professor of Population Health and Geography Harvard University A SIAN V ENTURE P HILANTHROPY N ETWORK (AVPN) I NDIA S UMMIT (2018) December 5-6,


  1. Data Science for Public Policy Case of Aspirational Districts Program S ( Subu ) V Subramanian, PhD Professor of Population Health and Geography Harvard University A SIAN V ENTURE P HILANTHROPY N ETWORK (AVPN) I NDIA S UMMIT (2018) December 5-6, 2018, New Delhi, India

  2. To accelerate progress in human • development indicators, focus on most “lagging” districts is critical Aspirational Districts were identified • based on a composite score of 49 indicators pertaining to 5 domains: Health/Nutrition • Education • Agriculture/Water Resources • Financial Inclusion/Skill Development • Infrastructure •

  3. Evidence- Based Perspective High Subject Dimension Domains Data DATA SCIENCE Statistical Computing Advances Sciences

  4. Transforming Aspirational Districts: A Data Science Lens Is there a need for fu further prioritization among Aspirational Districts, and by domains?

  5. “better-off” Aspirational Districts are improving at a much faster rate than the “worse-off” Aspirational Districts

  6. substantial overlaps between Aspirational Districts and Other Districts

  7. Message #1 There is a wide variation among Aspirational Districts that § may also be domain-specific, requiring additional prioritization of certain Aspirational Districts over others. Given, the considerable overlaps between Aspirational § Districts and Other Districts should there be a course- correction in the priority list?

  8. Transforming Aspirational Districts: A Data Science Lens How homogeneous are development markers within Aspirational District? wi

  9. Vizianagaram, Andhra Pradesh Mewat, Haryana

  10. Message #2 There is wide variation between-Villages, within- § Aspirational Districts , necessitating a greater degree of targeting and precision to program interventions.

  11. Transforming Aspirational Districts: A Data Science Lens ers are critically important for What new new d data la layer strengthening governance frameworks for development?

  12. Child Sex Ratio in India: A Village Level View

  13. “ India does not live in its towns but in its villages ” (1931)

  14. Syngeries: Parliamentary Constituencies and Aspirational Districts

  15. Child Stunting in India: A Parliamentary Constituency View

  16. Message #3 Units with direct representation (such as Villages, § Parliamentary/Assembly Constituencies) needs to be integrated into the policy discourse (and research) that thus far remains concentrated around States and Districts.

  17. Take Home Message Heterogeneity between and within Aspirational Districts § needs to be considered for policies to be efficient and equitable. Routine data collection, monitoring and synthesis related § to Gram Panchayat, Assembly and Parliamentary Constituencies are necessary for effective governance and accountability. Data Science approaches can be usefully applied to: § monitor and assess on-going programs § discover new insights that may necessitate a partial or § complete course correction

  18. Acknowledgments William Joe, Sunil Rajpal, Rakesh Kumar, Rockli Kim, Akshay Swaminathan, Shalini Rudra, Menaka Narayanan, Alok Kumar, R Venkataramanan, and Tata Trusts. Thank You svsubram@hsph.harvard.edu

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