Table of Contents

Module 01 — Campaign-scale RTI design

RTI for Activists & NGOs Module 01

Goal: Design RTI campaigns that produce statewide or national-scale evidence.

When one RTI isn't enough

Single RTI = one data point. Multi-RTI campaigns = pattern. To prove systemic problems (not isolated incidents), you need: same questions, same period, scaled across geographies / departments / years.

Examples:

  1. 'PMAY housing allotment irregularities' — needs 50+ district RTIs
  2. 'Mid-day meal scheme leakage' — needs 100+ school + 30+ DEO RTIs
  3. 'CAG audit follow-up' — needs ministry + state-cell RTIs

The 4-axis grid

Design every campaign on 4 axes:

  1. Geographic axis: districts/states (X RTIs)
  2. Time axis: years (FY 2020-21 to 2025-26 = 6 years)
  3. Department axis: cross-cutting (revenue + welfare + audit)
  4. Question axis: identical sub-questions across all RTIs (so responses can be tabulated)

A tight 4-axis grid produces a publication-ready dataset on receipt.

Identical-question rigor

For tabulation to work, every RTI must have literally identical sub-questions:

  1. (a) Number of beneficiaries enrolled in [scheme] in FY [year], your district.
  2. (b) Number of beneficiaries who received the FIRST instalment.
  3. © Number who received the SECOND.
  4. (d) Total funds disbursed (₹ lakh).
  5. (e) Number of complaints received about non-disbursal.
  6. (f) Number of complaints resolved.

If any RTI varies the question, that data point drops out of the analysis.

Volunteer-scale logistics

For a 100+ RTI campaign:

  1. Use a central spreadsheet with columns: state, district, PIO, ref no., filing date, response status, response excerpt.
  2. Recruit state coordinators to file from local addresses (some states require local addresses).
  3. Pre-stamp + pre-address envelopes at one central location, then ship to volunteers.
  4. Consolidated First Appeal pack ready when ~30% of RTIs return refusals.

Pattern recognition

Once 60-70% of RTIs return:

  1. Compare similar districts for variance — large variance = local irregularity worth investigating.
  2. Compare year-on-year within a district — sudden change = signal.
  3. Compute disclosure rate by state/department — low disclosure rate = system opacity (a story in itself).
  4. Identify outliers: 1-2 PIOs who give exemplary responses (use as 'compliance benchmark' in your report).

✅ Quiz

Quiz available from your course dashboard.

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Last reviewed: 24 April 2026.

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