Household-welfare and farm surveys: AIDIS, SAS, NFHS, time use and crop cutting

Detail

1. Why these surveys matter

• Sample survey: data is collected from a small, randomly chosen part of the population (the sample). The results are then scaled up to the whole population.
• Census (complete enumeration): every single unit is counted, such as every household, farm or animal.
• The surveys in this section measure household welfare. That means debt, assets, farm income, health and nutrition, and how people spend their time. They also measure farm output through crop yields.
• Most of them are run by the NSO (National Statistical Office, under MoSPI) as rounds of the NSS (National Sample Survey). NFHS is the exception. It is run by the Health Ministry.
• NCERT lists NSS estimates on literacy, school enrolment, morbidity (how often people fall ill), maternity, child care and PDS use.
• Example: the 60th round (January–June 2004) covered morbidity and healthcare.

2. AIDIS — All India Debt and Investment Survey

What it is

• A household survey of three things:
• assets: what a household owns, such as land, buildings, deposits and livestock
• liabilities: what it owes

• capital expenditure: spending on lasting assets, such as building a house or buying farm machinery

• It is decennial (held once every 10 years) since 1961-62.

History

• The RBI ran the rural credit surveys from 1951-52 (the All India Rural Credit Survey).
• The NSS took over in 1992 (48th round).
• Official list of NSS rounds on debt and investment: 26th (1971-72), 37th (1981-82), 48th (1992), 59th (2003), 70th (2013) and 77th (2019) [2].
• The NSO ran the 77th round at the request of the RBI [3].

77th round (January–December 2019): design

• Reference date for assets and debt: 30 June 2018. Capital expenditure was measured for the agricultural year 2018-19 (July–June) [2].
• Two visits to the same households: Visit 1 in January–August 2019, Visit 2 in September–December 2019 [2].
• Sample: 5,940 villages with 69,455 rural households, and 3,995 urban blocks with 47,006 urban households [2].

Key terms

• Incidence of Indebtedness (IOI): the percentage of households that have any debt.

• Formula: IOI = (indebted households ÷ total households) × 100

• Average Amount of Debt (AOD): the average cash debt per household. All households are counted, including those with no debt [2].

• Average Value of Assets (AVA): the average value of all physical and financial assets per household [2].
• Institutional credit: loans from formal lenders such as banks, cooperatives, government and SHG-bank links.
• Non-institutional credit: loans from moneylenders, landlords, traders, relatives and friends. These usually charge higher interest and are not regulated.

77th round: results (as on 30 June 2018)

• Incidence of indebtedness:
• Rural 35.0%: cultivator households 40.3%, non-cultivator households 28.2% [2][3]

• Urban 22.4%: self-employed households 27.5%, other households 20.6% [2][3]

• Where rural households borrowed [2][3]:

• only from institutions: 17.8%
• only from non-institutional sources: 10.2%
• from both: about 7%

• Urban comparison: 14.5% (only institutions) and 4.9% (only non-institutional sources).

• Share of outstanding debt from institutions: rural 66%, urban 87% [2]. So one rupee in every three owed by rural households is still owed to informal lenders.

• Average debt per household (AOD): rural Rs 59,748; urban Rs 1,20,336 [2].
• Average debt per indebted household: rural Rs 1,70,533; urban Rs 5,36,861 [2].
• Average assets per household (AVA): rural Rs 15,92,379; urban Rs 27,17,081 [2].
• How rural wealth is held: land 69%, buildings 22%, deposits 5% [2]. Rural wealth is mostly land, which cannot easily be sold for cash.
• Next round: MoSPI has announced a combined AIDIS and SAS for July 2026 to June 2027 [5].

Worked example: debt-to-asset ratio (rural, 2018)

• Debt-to-asset ratio = AOD ÷ AVA × 100 = 59,748 ÷ 15,92,379 × 100 ≈ 3.75%.
• This means the average rural household owed about Rs 3.75 for every Rs 100 of assets. That is low.
• But the average hides the indebted households. Their debt (Rs 1,70,533) is almost 3 times the average (Rs 59,748). The strain falls on a smaller group.
• Linked note for the credit analysis: financial-inclusion-rural-credit.

3. SAS — Situation Assessment Survey of Agricultural Households (77th round)

• Agricultural household: a rural household that earns a certain amount from farming activities (crops, livestock and similar) and has at least one member self-employed in farming.
• The SAS was run in rural areas only, alongside AIDIS in the 77th round (January–December 2019). It used the agricultural year July 2018 to June 2019 as its reference period [3].
• Average monthly income per agricultural household: Rs 10,218 (2018-19) [4].

• This covers all sources: wages, crop farming, livestock, non-farm business and land leasing.

• About half of agricultural households were in debt.

• Policy use: it is the main data point in the doubling farmers' income debate. The target was set in 2016 and aimed at 2022. SAS rounds are the before-and-after measure.
• Next round: combined with AIDIS, July 2026 to June 2027 [5].

4. NFHS — National Family Health Survey

What it is

• A large household survey on population, health and nutrition.
• It is run by the MoHFW (Ministry of Health and Family Welfare). The nodal agency is the IIPS, Mumbai (International Institute for Population Sciences).
• It is India's version of the international DHS (Demographic and Health Surveys). So its results can be compared with other countries'.
• Rounds: NFHS-1 (1992-93), NFHS-2 (1998-99), NFHS-3 (2005-06), NFHS-4 (2015-16), NFHS-5 (2019-21).
• NFHS-4 and NFHS-5 give district-level estimates. This is useful for targeting schemes such as POSHAN Abhiyaan.

Key terms

• TFR (Total Fertility Rate): the average number of children a woman would have in her lifetime at current birth rates.

• Replacement level = 2.1. At this level the population, in the long run, just replaces itself.

• Stunting: a child under 5 who is too short for their age. It is a sign of long-term (chronic) undernutrition.

• Anaemia: too little haemoglobin in the blood, often caused by iron deficiency.

NFHS-5 (2019-21) results

• TFR 2.0. It fell from 2.2 in NFHS-4 [6]. India is now below replacement level.
• Sex ratio: 1,020 women per 1,000 men. This was the first time an NFHS round showed more women than men.
• Child stunting: 35.5%. PIB rounds this as a fall from 38% to 36% since NFHS-4 [6].
• Anaemia in women (15-49): 57%. Anaemia is still a concern: more than half of women and children are anaemic [6].
• NFHS-6: fieldwork was done in 2023-24. PIB has since published a release titled "NFHS-6 Reflects India's Accelerated Progress in Maternal …" [7]. Check the NFHS-6 figures directly before quoting them.

5. Time Use Survey (TUS)

What it is

• The TUS measures how people split the 1,440 minutes of a day across activities:
• paid work
• unpaid domestic work (cooking, cleaning)
• unpaid caregiving (looking after children, the old and the sick)

• learning, leisure and self-care

• It is run by the NSO. TUS 2019 was India's first all-India round. TUS 2024 (January–December 2024) was the second [8].

• Few countries run a national time use survey. MoSPI names Australia, Japan, the Republic of Korea, New Zealand, the USA and China [8].
• Why it matters: it is the basis for valuing unpaid work. GDP leaves out most unpaid household services. The TUS shows how big that hidden work is, and it is mostly done by women.

Design of TUS 2024 [8]

• 1,39,487 households; 4,54,192 persons aged 6 years and above.
• Data was collected by CAPI (Computer-Assisted Personal Interviews). The recall period was 24 hours, from 4:00 AM the day before the interview to 4:00 AM on the interview day.
• The day was split into 30-minute slots. Up to 3 activities per slot were recorded if each lasted 10 minutes or more.

Three indicators [8]

• Participation rate = % of persons who did the activity during the day.
• Average time per participant = average minutes, counting only those who did the activity.
• Average time per person = average minutes, counting everyone (those who did not do the activity count as 0).
• Link between them: time per person ≈ participation rate × time per participant.
• Worked example: if 80% of women do domestic work, and each participant spends 290 minutes, then time per woman ≈ 0.80 × 290 = 232 minutes.

Results of TUS 2024

• Unpaid domestic services, persons aged 6+ (minutes per participant per day): women 289, men 88 [8].

• (NCERT: "women (15-59) spent 289 minutes on unpaid domestic and care work, against 88 for men". The official table gives 289/88 for domestic services only, for persons aged 6 and above.)

• Women aged 15-59 who did unpaid domestic work: 305 minutes in 2024, down from 315 minutes in 2019. MoSPI reads this as a shift from unpaid to paid work [8].

• Caregiving, age 15-59: 41% of women took part against 21.4% of men. Women who did it spent 140 minutes, men 74 minutes [8].
• Employment and related activities, age 15-59: participation was 75% for men (2019: 70.9%) and 25% for women (2019: 21.8%) [8].

6. Crop estimation — the Crop Cutting Experiment (CCE)

What a CCE is

• A crop cutting experiment (CCE) harvests the crop from a small plot of fixed size, chosen at random within a randomly selected field.
• The produce is weighed, and this gives the yield (output per hectare).
• Many CCEs are averaged to get the average yield for an area.

Who runs it

• State governments run CCEs under the General Crop Estimation Surveys (GCES).
• The NSO's Field Operations Division gives technical guidance and supervises.

Formula

• Production = Area × Yield
• Worked example: a district sows 50,000 ha of wheat, and the CCE average yield is 3.2 tonnes/ha. Production = 50,000 × 3.2 = 1,60,000 tonnes.
• District figures are added up into state and national advance estimates of production. These are released by the DES (Directorate of Economics and Statistics, MoA&FW).

Link to crop insurance (PMFBY)

• The same CCE yields settle PMFBY (Pradhan Mantri Fasal Bima Yojana) claims:
• if the actual yield in an insurance unit falls below the guaranteed (threshold) yield → farmers there get paid in proportion to the shortfall

• so a wrong or late CCE → a wrong or late claim payment

• Illustration (general shortfall method, not taken from the retrieved sources): threshold yield 40 q/ha, actual yield 30 q/ha, sum insured Rs 50,000. Claim = (40 − 30) ÷ 40 × 50,000 = Rs 12,500.

Technology in crop estimation

• CCE-Agri app: field staff record and upload CCE yield data digitally to the NCIP (National Crop Insurance Portal). This data is used to work out the actual yield and the claims due. Insurance companies can also watch CCEs, which makes the process more transparent [9].
• YES-TECH (Yield Estimation System based on Technology): it estimates yields using satellite and remote-sensing data. It started for paddy and wheat from Kharif 2023 [10].
• Digital General Crop Estimation Survey (DGCES): this runs under the Digital Agriculture Mission. It will be used for CCEs to give more accurate yield estimates [11].
• Digital Crop Survey: it records crop area plot by plot. Check its current status before quoting it.

7. Complete enumerations run alongside the sample surveys

• Agriculture Census (11th round, 2021-22, MoA&FW): counts operational holdings (land farmed as one unit, whether owned or leased) and their size, tenure and land use. It is held every 5 years.
• Livestock Census (21st round, 2024-25, DAHD): a full count of livestock and poultry. DAHD is the Department of Animal Husbandry and Dairying.
• How they work with the sample surveys:
• a census gives the frame (the full list to sample from) and the benchmark totals
• sample surveys (SAS, CCE) give more detail and more frequent estimates in between

Prelims Hooks

• AIDIS is decennial. The RBI ran rural credit surveys from 1951-52. The NSS took over in the 48th round (1992). The latest completed round is the 77th (2019), run at the RBI's request [3].
• AIDIS 77th round: incidence of indebtedness was 35.0% rural and 22.4% urban, measured as on 30 June 2018. Institutions held 66% of rural debt [2].
• SAS 77th round: Rs 10,218 average monthly income per agricultural household, for agricultural year 2018-19. It covered rural areas only.
• NFHS = MoHFW, with IIPS Mumbai as nodal agency; it is India's DHS. Trap: it is not run by the NSO/MoSPI.
• NFHS-5 (2019-21): TFR 2.0 (below replacement level of 2.1); sex ratio 1,020; stunting 35.5%; anaemia in women 15-49 57%.
• TUS 2024 is India's second time use survey (the first was 2019). It used 30-minute slots, a 24-hour recall from 4 AM to 4 AM, and covered persons aged 6+ [8].
• Per-participant minutes count only those who did the activity. Per-person minutes count everyone. This is a common trap.
• CCE → yield; Area × Yield = Production → DES advance estimates. The same yields settle PMFBY claims.
• YES-TECH began with paddy and wheat, Kharif 2023 [10]. The CCE-Agri app uploads data to the NCIP [9].
• Census vs sample: Agriculture Census (11th, 2021-22, MoA&FW) and Livestock Census (21st, 2024-25, DAHD) count every unit. AIDIS, SAS, NFHS and TUS use samples.

Mains Points

• Rural credit is still partly informal (GS-III):
• one-third of rural debt still comes from non-institutional lenders (AIDIS 2019) [2]
• informal loans cost more and are not regulated → debt traps and farmer distress

• so, more KCC, SHG-bank linkage and cooperative credit reach is needed, including for tenant farmers who have no land records to offer as security.

• Measuring farm income:

• Rs 10,218 a month (2018-19) was the SAS baseline for judging the doubling farmers' income target
• a 7-year gap between SAS rounds makes it hard to track the target in time

• so the 2026-27 AIDIS-SAS round [5] and more frequent farm income data are needed for evidence-based policy.

• Unpaid work and gender (GS-I/GS-III):

• TUS shows women carry most domestic and care work: 289 vs 88 minutes per participant [8]
• this unpaid work is left out of GDP → women's economic contribution is undercounted

• arguments follow for a care economy push (crèches, elder care) and satellite accounts (side accounts that value unpaid work without changing GDP). The rise in female participation in paid work, from 21.8% to 25% [8], shows time moving from unpaid to paid work.

• Better crop data means fairer insurance:

• CCEs are slow, few in number and open to manipulation → claim delays and disputes under PMFBY
• fixes: YES-TECH, the CCE-Agri app and DGCES [9][10][11]
• the trade-off: satellite estimates need ground checks and must work for small plots.