SECC 2011 — Socio Economic and Caste Census¶
India's most granular poverty dataset — household-level deprivation data for 179 million rural households, down to the village level.
What Is SECC 2011?¶
The Socio Economic and Caste Census 2011 is unlike any other Indian survey. Rather than a sample, it is a complete enumeration — every single household in India was visited and recorded. Its purpose was to identify poor and marginalised households for targeting government welfare schemes.
SECC provides:
- Rural component: 179.3 million rural households across all villages
- Urban component: 31.4 million urban households
- Caste data: State-wise caste/sub-caste enumerations (released separately)
SECC is Not a Census of Poverty
SECC does not directly measure income or consumption. Instead, it uses deprivation and exclusion criteria — observable household characteristics that proxy for poverty. A household is identified as "deprived" if it lacks basic assets or has specific vulnerability factors.
How SECC Classifies Households¶
Rural — Automatic Exclusion (NOT Eligible for Benefits)¶
Households are automatically excluded from welfare schemes if they have any one of:
| Exclusion Criterion | Rationale |
|---|---|
| Motorised 2/3/4 wheeler or fishing boat | Asset ownership = not poor |
| Mechanised farm equipment | |
| Kisan Credit Card with ₹50,000+ limit | |
| Government employee in household | Regular income |
| Non-agricultural enterprise registered with government | |
| Any member earning ≥₹10,000/month | |
| Income tax payer | |
| Refrigerator | Asset indicator |
| Landline phone | |
| ≥2.5 acres irrigated land or ≥5 acres unirrigated | |
| 3 or more rooms with brick/concrete walls and roof |
Rural — Automatic Inclusion (Eligible for Benefits)¶
Households are automatically included if they have any one of:
| Inclusion Criterion |
|---|
| Scheduled Caste / Scheduled Tribe household |
| Household with no adult member aged 16–59 years |
| Female-headed household with no adult male member 16–59 |
| Household with disabled member and no able-bodied adult |
| Manual scavenger household |
| Primitive Tribal Group (PTG) |
| Legally released bonded labour |
Rural — Deprivation Scoring (for remaining households)¶
For households that are neither auto-excluded nor auto-included, SECC measures 5 deprivation indicators:
- Only one room with kutcha (mud/thatch) walls and roof
- No adult male member aged 16–59
- Female-headed household
- No literate adult above 25 years
- Scheduled Caste or Scheduled Tribe household (also auto-included)
- No able-bodied adult, with disabled member (also auto-included)
- Landless household deriving income mainly from manual casual labour
The more indicators a household has, the higher its deprivation rank.
How to Access SECC Data¶
Method 1: State-level Summaries from secc.gov.in 🟢¶
- Go to secc.gov.in
- Click "Rural" or "Urban" tab
- Select your state and district from the dropdown
- View block and village-level summary tables online
- Use browser tools to copy/export the table data
Method 2: Structured Tables from data.gov.in 🟢¶
For analysis-ready CSV:
- Go to data.gov.in
- Search:
"SECC 2011 rural"or"socio economic census" - Download CSV — includes district/block/village level deprivation summaries
Method 3: SHRUG Village Dataset 🟡¶
The best structured source for SECC village-level data is SHRUG (Sub-national Harmonised Rural & Urban Geography):
- Go to shrug.io
- Register (free, academic)
- Download the "SECC" module — has village-level deprivation counts joined to a consistent village ID
Method 4: Unit-Level Household Data 🔴¶
For actual household records (not just summaries), researchers can apply through: - secc.gov.in → "Data Request" form - Approval required from MoRD — typically for academic research
Key Variables in SECC Summary Tables¶
| Variable | Description |
|---|---|
Total_HH |
Total households in village/block |
Auto_Excluded |
Households not eligible for benefits |
Auto_Included |
Automatically eligible households |
Dep_0 |
Households with 0 deprivation indicators |
Dep_1 to Dep_5 |
Households with 1–5 deprivation indicators |
SC_HH |
Scheduled Caste households |
ST_HH |
Scheduled Tribe households |
Female_HH |
Female-headed households |
No_Adult_Male |
Households with no adult male (16–59) |
Python: Deprivation Analysis¶
import pandas as pd
import matplotlib.pyplot as plt
# Load SECC district summary (from data.gov.in or SHRUG)
secc = pd.read_csv("secc_district_summary.csv")
# Calculate deprivation rate (% HH with at least 1 deprivation)
secc['Deprived_HH'] = secc['Dep_1'] + secc['Dep_2'] + secc['Dep_3'] + \
secc['Dep_4'] + secc['Dep_5']
secc['Deprivation_Rate'] = (secc['Deprived_HH'] / secc['Total_HH']) * 100
# Filter for Maharashtra
mh = secc[secc['State'] == 'Maharashtra'].copy()
mh = mh.sort_values('Deprivation_Rate', ascending=False)
# Plot top 15 most deprived districts
fig, ax = plt.subplots(figsize=(10, 8))
mh.head(15).plot(kind='barh', x='District', y='Deprivation_Rate',
ax=ax, color='crimson', alpha=0.8, legend=False)
ax.set_xlabel('% Households with ≥1 Deprivation (SECC 2011)')
ax.set_title('Top 15 Most Deprived Districts — Maharashtra (SECC 2011)')
plt.tight_layout()
plt.savefig('secc_deprivation_maharashtra.png', dpi=150)
plt.show()
✏️ Practice Exercise¶
Exercise 2.3 — Find the Most Deprived Block in Your District
Goal: Use SECC data to rank blocks in your district by deprivation.
Time needed: 20 minutes
- Go to secc.gov.in → Select Rural
- Select your State → Select your District
- The portal shows a table of blocks with deprivation counts
- Note the values for each block:
- Total Households
- Auto-excluded (not deprived)
- Auto-included (most deprived)
- Deprived categories (Dep 0 to Dep 5)
Answer these questions: - [ ] Which block in your district has the highest share of auto-included households? - [ ] Which block has the most female-headed households? - [ ] What % of your district's total households are auto-excluded (relatively well-off)?
SECC and government schemes: The SECC list is used to determine who gets ration cards (NFSA), PMAY (housing), and other welfare benefits. Understanding it helps you understand India's targeting system.
Related Datasets¶
- Census of India — Same geographic units, complementary indicators
- NFHS — Health outcomes for deprived populations
- MGNREGA — Employment data for the same households
- SHRUG — SECC + Census + election data combined at village level
Next Dataset: NSS & PLFS Surveys →