Dataset and Method
What the data is, how it was collected, and how the 553-respondent sample was selected.
Dataset introduction
What this data is, who published it, and which of its two workbooks this study actually uses.
Official source: sebi.gov.in — Investor Survey 2025
Survey year vs. publication date
The fielding is named “Investor Survey 2025”; the publisher's own URL path is dated “jan-2026” (report/publication listing). This repository does not have a separately documented data-collection period distinct from the survey's name — the exact fielding dates are not established from the files available here.
What it investigates
A national household survey of individual investment behaviour: awareness, current holdings, past investment, and — for the subset asked — reported barriers and encouragement factors across securities-market products (mutual funds, ETFs, stocks, F&O, REITs/InvITs, corporate bonds, AIFs).
What a respondent record is
A respondent record is one person who took part in the survey — not an “automatically completed survey,” and every column is a workbook field, not automatically a “question” (see below).
This is a SEBI national survey, not INDmoney app-usage or conversion telemetry. It cannot establish anything about INDmoney's own users or product funnel.
| Workbook | Records | Columns | Used here? | What it covers |
|---|---|---|---|---|
| Respondent Data.XLSX sheet “RLD” | 1,09,430 | 448 | Used | One row per individual survey respondent. Used for this entire study. |
| Intermediary Data.XLSX sheet “T1” | 1,313 | 70 | Kept separate | Surveys market intermediaries (RIAs, distributors, brokers) about their category, tenure, customer base, and their own perceived view of investors — a different unit of analysis (intermediary opinion, not respondent self-report). Its identifier column (INTNR) has 7 duplicate values out of 1,313 rows, noted but not investigated. Kept entirely separate from this study. |
The research question is about what individual (would-be) investors themselves report — their occupation, income, product consideration, holdings, and barriers. Only the respondent workbook asks respondents about themselves; the intermediary workbook records what intermediaries perceive about investors in general, a different population and a different question entirely.
Throughout this page, workbook rows are called records and workbook fields are called columns — not “automatically completed surveys” or “questions.” Section 3 explains which columns are genuine survey questions versus metadata or derived fields.
Survey participation
Every respondent went through an initial screening stage (Listing); a subset then completed the detailed Mains section this study relies on.
QFL (Investor / Non-Investor) is a separate classification decided at the Listing stage. It is not the same split as Listing-only vs. Main-survey completion, and this study never substitutes one for the other.
Not every Main-survey respondent answered every Main-survey question — routing narrows further within the Mains section (see the field catalogue and the coverage section below).
What respondents were asked
The respondent workbook has 448 columns — not 448 questions. Many are metadata, derived fields, or per-product grid slots. Search or filter the full catalogue below to see what each column actually is.
How we checked and interpreted the data
Eight checks applied before any filtering or analysis — each one exists to prevent a specific, concrete misreading of this workbook.
How we narrowed the dataset
Each step keeps the respondents relevant to our research question.
Research selection — not investment drop-offs.
1,09,430
Total respondent records
56,073 left out
Listing-only records
28,781 left out
Outside the Gen Z age band
18,892 left out
Documented non-salaried category
2,901 left out
Didn’t select mutual funds
Schematic widths — counts show actual sample sizes. Tap a funnel stage for why that filter is applied.
Selected respondents — salaried Gen Z who considered mutual funds and do not currently hold them.
Confirmed by a final consistency check: all 553 of 553 have an interpretable, MF-free holdings answer (0 excluded, 0 unknown — this step doesn't narrow the group further).
Separately from this funnel: within the broader group (n = 4,346), 3 respondents have an unknown mutual-fund holding status (blank or explicit “Not Answered” on Q22A_All). This is a distinct measure — the current-MF-holding-share calculation for the broader group — not a step in the funnel above, since the focused group (step 5) is itself defined by requiring an interpretable Q22A_All.
| Status | n | % of known status |
|---|---|---|
| holds | 1,427 | 32.86% |
| does not hold | 2,916 | 67.14% |
| unknown | 3 | — |
How much information is available within the selected group
Being selected into the focused group (n = 553) does not mean answering every question — each field has its own denominator.
'None of the above' on Q24A — no prior investment in any of the 7 securities-market products Q24A asks about (does not rule out FDs, insurance, EPF, gold, etc.)
Yes — reported previous MF investment
Invested previously in another securities-market product, not MF
What this dataset allows us to study
The research question this data can answer, and the boundaries around it.
What the data supports
Unweighted, self-reported shares within a defined SEBI respondent sample: awareness, consideration, current holding, past securities-market investment, and — for the subset asked — selected barriers and encouragement factors, each with its own denominator. It also supports descriptive differences in those shares across income or previous-investment subgroups, reported as observed patterns in this specific sample.
Why current non-holders include previous investors
The focused group (n = 553) is defined by current non-holding of mutual funds only — it is not a “never invested” or “first-time SIP” sample. 136 of 553 (24.6%) report previous mutual-fund investment; 382 (69.1%) report none of 7 specific securities-market products; 35 (6.3%) report prior investment in another securities-market product but not MF.
Why results are unweighted sample descriptions
No survey weight (WeightMainM2, Weight_to_Sample) is applied anywhere in this study. Every share describes this specific unweighted sample, not a population-representative estimate — a deliberate, standing design choice for this stage's academic analysis.
Why MF+ETF barriers cannot be presented as MF-only
Every barrier and encouragement field (A11_D11–A15_D15, AA1_DD1–AA4_DD4) is asked only at the combined mutual-fund-plus-ETF level. No field in this workbook reports barriers or encouragement for mutual funds in isolation from ETFs/Gold ETFs.
What this dataset cannot establish
It cannot establish INDmoney conversion, any observed INDmoney app journey, or the cause of any reported barrier. This is a SEBI national household survey, with no link to INDmoney's own users, product funnel, or telemetry — it supports hypotheses worth investigating in INDmoney's own product research, not conclusions about INDmoney itself.