Quotas and reservations to ensure equity in Nepal
Publication id: shakya-2016-nepal-quotas-reservations-equity
Status: verified
Citation: Shakya, S. & Lama, N. (2016). Possible decision rules to allocate quotas and reservations to ensure equity for Nepalese poor. Economic Journal of Development Issues, 17(1-2):149-162. Download PDF
Facts¶
Policy hook¶
Nepal provides quotas and reservations for disadvantaged groups, but these categories are economically heterogeneous. How can governments design poverty-targeted allocation rules that effectively identify and support those truly below the poverty line?
Main finding¶
Decision tree models using 14 practical questions can predict household poverty in Nepal with 70-94% accuracy across different scenarios, providing policymakers with implementable tools for quota and reservation allocation that target assistance to the poorest households.
Data and setting¶
14,907 household observations from Nepal; classification and regression tree (CART) approach modeling 5 different response scenarios.
Research design (plain language)¶
Machine learning (CART) developed simple decision trees based on answerable questions enumerators could verify. Multiple response scenarios tested rule robustness; out-of-sample accuracy measured.
One caveat¶
Decision rules’ accuracy varies significantly depending on response patterns (70-94%), suggesting real-world performance depends heavily on implementation and response reliability.
PDF or DOI¶
Why it matters¶
Quota and reservation systems aim to reduce inequality, but they often miss the poorest households because program administrators lack reliable identification tools. This paper shows that simple, verifiable questions can accurately target poverty-based benefits. For developing country governments, the practical decision-tree approach is implementable without expensive data collection—a major advantage over statistical matching or means testing that may require documentation many poor households lack. For India, Nepal, and other countries relying on affirmative action via quotas, better targeting improves both program effectiveness and public trust. The finding that accuracy varies across response scenarios also highlights the need for robustness testing when adopting these rules in new settings. By bridging the gap between broad categorical quotas and truly poverty-targeted assistance, the paper offers a middle path between equity goals and administrative feasibility.