How can official statistical surveys such as the Time Use Survey strengthen evidence-based policymaking in India? Illustrate with examples.

Evidence-based policymaking means designing and revising schemes on measured outcomes rather than assumptions. Official surveys make hidden realities measurable and let policies be tested. One example is the NSO's Time Use Survey (TUS), which records how about 4.5 lakh persons spend every half-hour of a day [1].

How official surveys strengthen policymaking

• Making unpaid work visible: In TUS 2024, women aged 15–59 spent about 305 minutes a day on unpaid domestic work. Also, 41% of women gave household care, against 21.4% of men [2]. This supports investment in care infrastructure and gender budgeting.
• Tracking change over time: Because the 2019 and 2024 rounds can be compared, they show women's participation in employment rising from 21.8% to 25% [2]. This gives a baseline for targets.
• Diagnosing root causes: The data show that time poverty, not just a lack of jobs, keeps women out of paid work.
• Independent evaluation: Nationally representative microdata let researchers test State schemes without relying on what implementing agencies say about themselves.

Illustration: fare-free bus travel for women

• In Punjab, Tamil Nadu, Karnataka and Telangana, the share of women making at least one trip a day rose from 38.7% to 45.3% (2019–2024). That is +6.6 percentage points (pp), against +4.4 pp in 12 other large States [1].
• Natural experiment: Telangana's Mahalakshmi scheme began in December 2023, and women's mobility there rose 10.9 pp. Andhra Pradesh had no scheme until August 2025, and its figure rose only 0.8 pp [1].
• This moves the "freebie vs welfare" debate from rhetoric to results.
• Pairing with audits: The CAG found that the Delhi Transport Corporation's (DTC's) bus fleet shrank from 4,344 to 3,937 buses [3]. Free rides need parallel investment in buses.

Making surveys more useful

• Association, not causation: Two time points cannot isolate a scheme's effect. TUS data should be linked with PLFS to test whether more trips lead to more jobs.
• More frequent rounds with State-level samples would allow before-and-after checks, e.g., testing Andhra Pradesh's scheme in the next TUS [1].

Official surveys turn welfare debates into measurable questions: who benefits, by how much, and at what cost. Regular and detailed data, openly shared and read alongside audits and administrative records, can make Indian schemes self-correcting. This would advance SDG 5 and the welfare-State vision of Article 38.