Targeting errors

Also called: Inclusion error, Exclusion error, Inclusion and exclusion errors · Topic: Government Budget, Fiscal Policy and FRBM · NCERT: Beyond NCERT

Meaning

Targeting errors are the two mistakes a welfare scheme makes when it tries to give benefits only to eligible people (for example, the poor). An inclusion error means the benefit goes to people who are not eligible. An exclusion error means people who are eligible are left out.

• They matter because every rupee lost to inclusion error is public money wasted.
• Every eligible person hit by exclusion error is a poor person denied their rightful welfare.
• Standard measures:
• Inclusion error rate = (Ineligible people covered ÷ Total people covered) × 100
• Exclusion error rate = (Eligible people missed ÷ Total eligible people) × 100

Explanation

How targeting works and where it goes wrong

• Targeting means the scheme sets eligibility rules, such as income, BPL status or land size. Only people who meet the rules should get the benefit.
• No system can identify every person perfectly, so two kinds of error always appear.
• Inclusion error (benefit reaches the wrong people):
• Ghost beneficiaries: names of people who do not exist.
• Duplicate beneficiaries: the same person is listed twice.
• Better-off households holding BPL (Below Poverty Line) cards.

• Effect: public money is wasted, and the subsidy bill grows with no gain for the poor.

• Exclusion error (the right people get left out):

• Aadhaar authentication failures: worn fingerprints (common among manual labourers and the elderly) or poor internet at the ration shop.
• Outdated beneficiary lists: new poor families, migrants or newly married members are not added.
• Effect: the poorest can lose food or cash they are entitled to.

The trade-off between the two errors

• Tighter targeting (stricter rules, more checks):
• fewer ineligible people get through, so inclusion error falls

• but more genuine people fail the checks, so exclusion error rises

• Universal schemes (everyone gets the benefit):

• exclusion error is close to zero, because nobody is left out

• but inclusion error is very high, because the rich are covered too, and the cost goes up

• Policy therefore cannot remove both errors at once. It has to choose which error it can live with.

• Exclusion error hurts the most vulnerable. Many experts therefore see it as the more serious one in food and nutrition schemes.

Worked example

• 100 people are eligible. The scheme reaches 120 people: 90 of them are eligible and 30 are not.
• Inclusion error = 30 ineligible people covered

• Inclusion error rate = (30 ÷ 120) × 100 = 25%

• Exclusion error = 100 − 90 = 10 eligible people missed

• Exclusion error rate = (10 ÷ 100) × 100 = 10%

• If the government tightens checks and removes the 30 ineligible people, it may also wrongly drop some of the 90 eligible ones. Inclusion error falls, but exclusion error rises.

In India

• Where targeting errors show up:
• Targeted Public Distribution System (TPDS) and NFSA: cheap or free foodgrains.
• PMGKAY (Pradhan Mantri Garib Kalyan Anna Yojana): free foodgrains for NFSA beneficiaries, extended for five years from 1 January 2024.

• Food and fertiliser subsidies make up 87% of the Union subsidy bill (2026-27 BE, Budget Estimate). So errors in these two schemes cost the most [3].

• The PEO finding (Planning Commission): its study of TPDS found that the government spent ₹3.65 to deliver ₹1 of food subsidy to the poor. Part of this loss came from diversion to the wrong people.

• DBT and the JAM trinity as a fix for inclusion error:
• DBT (Direct Benefit Transfer) means money is paid straight into the beneficiary's bank account. It was launched on 1 January 2013.
• JAM (Jan Dhan–Aadhaar–Mobile) was proposed in the Economic Survey 2014-15. Aadhaar seeding (linking each beneficiary to their Aadhaar number) removes ghost and duplicate names.
• A BlueKraft Digital Foundation study of 2009-2024 data estimated cumulative savings of ₹3.48 lakh crore from plugging leakages [1].
• The same study found that subsidies fell from 16% to 9% of total government expenditure after DBT [1].

• Cumulative DBT transfers reached about ₹53.26 lakh crore (as of September 2026) [2].

• The flip side: Aadhaar-based authentication can create exclusion errors when fingerprints fail or the internet is down.

• Cash transfer pilots for food: tested in Chandigarh and Puducherry (since September 2015) and in urban Dadra and Nagar Haveli (since March 2016) [4].
• UBI angle: the Economic Survey 2016-17 costed a UBI (Universal Basic Income) of about ₹7,620 a year for 75% of the population, at about 4.9% of GDP.
• It deliberately accepted some inclusion error, and dropped the richest 25% to keep the cost down.
• The aim was to avoid the exclusion errors of narrow targeting.

Don't confuse with

• Leakage: leakage is benefit lost in the delivery chain through theft, diversion, storage or transport losses. Targeting error is about who is on the beneficiary list. Leakage can happen even when the list is perfect.
• Universal scheme / UBI: a universal scheme avoids targeting on purpose. It has almost no exclusion error but high inclusion error. A targeted scheme tries to reduce inclusion error and risks exclusion error.
• Financial inclusion: this means bringing people into the banking system, for example through Jan Dhan. It has nothing to do with "inclusion error", which is a mistake in welfare targeting.
• Inclusion vs exclusion swap (MCQ trap): a ghost or duplicate ration card = inclusion error. An eligible person denied ration because of Aadhaar authentication failure = exclusion error.

Prelims Hooks

• Inclusion error = ineligible people get the benefit (ghost names, duplicates, better-off BPL card holders). Exclusion error = eligible people are left out.
• Aadhaar authentication failure (worn fingerprints, poor internet) is an exclusion error, not an inclusion error.
• Aadhaar seeding under JAM (proposed in the Economic Survey 2014-15, not the Budget speech) mainly cuts inclusion errors by removing ghost and duplicate beneficiaries.
• Trade-off: tighter targeting lowers inclusion error but raises exclusion error. Universal coverage does the opposite.
• PEO (Planning Commission) TPDS study: ₹3.65 spent to deliver ₹1 of food subsidy.
• Economic Survey 2016-17 UBI: 75% coverage, ₹7,620 a year, about 4.9% of GDP. This is "quasi-universal" design that trades some inclusion error for lower exclusion error.

Mains Points

• Technology cuts one error but can create the other:
• DBT and JAM removed ghost beneficiaries. They produced estimated savings of ₹3.48 lakh crore, and subsidies fell from 16% to 9% of expenditure [1].
• But biometric failures and outdated lists can exclude the poorest.

• Safeguards are needed: offline or manual fallbacks, grievance redress, and regular updating of beneficiary lists.

• Which error to tolerate depends on the scheme:

• For food and nutrition, exclusion can mean hunger. So near-universal coverage (as in PMGKAY for NFSA beneficiaries) accepts some inclusion error.

• For costly subsidies such as fertiliser or LPG, tighter targeting saves fiscal space (money freed for other spending) and helps meet FRBM deficit targets.

• Cash vs in-kind and UBI debate: cash through DBT reduces diversion. In-kind food protects against inflation and suits remote areas with weak banking. A hybrid, choice-based model and quasi-universal designs (like the 75% UBI of the Economic Survey 2016-17) try to balance the two targeting errors against the fiscal cost.

Related concepts

• Subsidy
• Cash transfers
• JAM trinity
• Universal Basic Income
• Freebies
• Performance budget
• Outcome budget
• Zero-based budgeting
• Gender budgeting
• Green budgeting

Read more

• Spending better: subsidies, DBT and budgeting innovations