📈 economics

Understanding Household Financial Behaviour in India: An Empirical Analysis

Using 2019 AIDIS data and a probit regression model, this study analyzes how demographic and socio-economic factors influence Indian household financial behavior, revealing that while bank deposits are the dominant asset, participation in advanced financial products remains limited and is significantly shaped by gender, age, education, occupation, and regional development.

Original authors: Nahid Zehra, Udai Bhan Singh

Published 2026-08-04

📖 7 min read🧠 Deep dive

CC BY 4.0

Original authors: Nahid Zehra, Udai Bhan Singh

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Great Family Money Map

Imagine the economy as a massive, bustling ocean. In this ocean, every family is a small boat trying to navigate safely while carrying their cargo of daily needs. Some boats are sturdy and well-stocked; others are smaller and more fragile. The science of household finance is essentially the study of how these captains (the heads of households) decide to steer their boats. Do they keep their cargo in a simple, safe locker (like a bank deposit)? Do they try to catch a big wave for a huge reward (investing in risky stocks)? Or do they have to borrow a paddle from a neighbor to keep moving (taking a loan)?

For a long time, scientists mostly studied the captains of the biggest, most expensive ships in developed countries. They figured out that things like how old the captain is, how much they know about maps (education), and whether they are a man or a woman change how they steer. But what about the millions of smaller boats in developing countries like India? Do they follow the same rules, or is their ocean different? This question matters because if we want to help families build better futures, we need to understand the specific currents and winds that affect their boats, not just the big ones. We need to know what makes a family in India decide to save, invest, or borrow.

The Study: Charting the Indian Ocean

This paper, titled "Understanding Household Financial Behaviour in India," acts like a giant, high-tech sonar scan of the Indian ocean. The authors, Nahid Zehra and Udai Bhan Singh, didn't just look at one type of boat; they analyzed data from over 116,000 households across India, collected in 2019. They wanted to see how demographic characteristics—basically, who the family is (age, size, education, job, and where they live)—shapes their financial choices.

Think of the family's financial life as a menu with four main sections:

The Safe Vault: Putting money in a bank (deposits).
The Future Plan: Buying insurance or pension funds (safe investments).
The High-Risk Gamble: Buying stocks or bonds (risky assets).
The Borrowing Barrow: Taking loans from formal banks.

The researchers used a mathematical tool called a probit model (think of it as a sophisticated calculator that predicts the chance of a family choosing one option over another) to see which family traits push them toward which part of the menu.

What the Data Revealed

The scan revealed some very clear patterns, like distinct currents in the water.

The "Safe" Crowd vs. The "Risky" Gamblers
The most striking finding is that Indian families are incredibly cautious. Out of every 100 households, 92 have money sitting in a bank deposit. It's the most popular choice, like everyone choosing the same flavor of ice cream because it's safe and familiar. However, when it comes to "risky" assets like stocks or bonds, the number drops to a tiny 1.37%. It's as if almost no one is willing to jump off the dock to try surfing the big waves.

Who is on the Boat?
The study found that the captain's identity matters a lot:

Gender: If the captain is a man, the family is much more likely to take risks. Men are more likely to invest in stocks and borrow money from banks. The data suggests men seem more willing to bet on the future, while women-headed households stick closer to the shore. Interestingly, men and women are equally likely to just put money in a bank; the difference shows up in the riskier choices.
Age: The relationship with age is like a rollercoaster. Younger and middle-aged captains are more likely to take risks and borrow money (perhaps to buy a house or start a business). But as the captain gets older, they become more conservative, preferring safe savings and avoiding risky bets. The paper notes that the "risk-taking" curve goes up and then comes back down as people age.
Education: This is the map-reading skill of the boat. Families where the captain has a university degree are much more likely to invest in both safe and risky assets. Education seems to give people the confidence to understand complex financial products. However, there's a twist: families with primary education sometimes take more risks than those with higher education, perhaps because they are trying to catch a big break to improve their situation, whereas the highly educated might be more strategic.
Family Size: A bigger family is like a boat with more passengers. These families save more in safe accounts (to feed everyone) and borrow more (to meet needs). But, they are very afraid of the risky waves. As the family grows, the chance of investing in stocks actually goes down. They can't afford to lose their cargo.

The Location and Job Factors

City vs. Country: Families living in cities are better at using formal financial tools like insurance and long-term loans, likely because banks are right around the corner. But surprisingly, city families save less in simple bank deposits than rural families.
The Job: If the captain is a "casual laborer" (working day-to-day with no guaranteed paycheck), they are very risk-averse. They stick to safe assets and avoid stocks. They also borrow less in the long term. In contrast, self-employed people and salaried workers are more active in all areas, including borrowing and investing.
State Development: Families living in states that are more "developed" (with better infrastructure and human development) are much more likely to have a diverse portfolio. They hold a mix of safe and risky assets, whereas families in less developed states stick almost entirely to basic savings.

The "What If" Scenarios

The authors didn't just stop at the general picture. They asked, "Does this change if the family is rich or poor?" or "Does it change if the family is small or huge?"

Rich vs. Poor: Even when they separated the data by how much the family spends (a proxy for income), the main rules held true. Men still took more risks, and education still mattered. However, poor families with only primary education were surprisingly willing to take risks, perhaps seeing it as their only way up. Rich families with primary education, on the other hand, were very cautious, likely because they already had enough to get by.
Small vs. Large Families: Small families were more likely to take risks and borrow short-term money. Large families were more focused on long-term loans and safe savings. A small family might feel they can afford a gamble; a large family knows they need a safety net.

The Bottom Line

The paper concludes that while Indian households are masters of the "Safe Vault" (bank deposits), they are still very hesitant to step into the "High-Risk Gamble" (stocks) or even fully embrace the "Future Plan" (insurance and pensions).

The authors suggest that the reason isn't just a lack of money, but a lack of understanding. They argue that if we want more families to diversify their portfolios and stop relying solely on cash or gold, we need to teach them how these financial tools work. It's like teaching the boat captains how to read the stars and navigate the currents, rather than just telling them to stay in the harbor. By improving financial literacy, especially for women, the less educated, and those in less developed regions, India could help its families steer their boats toward a more secure and prosperous future.

The study doesn't claim to have solved the mystery of human behavior, but it provides a very clear, data-backed map of where Indian families are right now and what factors are steering their financial ships.

Technical Summary: Understanding Household Financial Behaviour in India

Problem Statement and Motivation
Existing literature on household finance is heavily skewed toward developed economies, where households generally possess greater financial sophistication and market access. Consequently, empirical evidence regarding the socio-economic determinants of household financial behavior in developing and emerging economies, particularly India, remains limited and fragmented. While previous studies have often focused on single dimensions of household finance—such as bank account ownership or specific investment choices—there is a lack of comprehensive analysis using nationally representative data that simultaneously examines multiple facets of financial decision-making. This study addresses this gap by investigating how demographic characteristics influence diverse financial behaviors, including savings, investments in safe and risky assets, and borrowing decisions within the Indian context.

Data and Variables
The analysis utilizes unit-level data from the 77th round of the National Sample Survey (NSS), specifically the All-India Debt and Investment Survey (AIDIS) conducted by the National Statistical Office (NSO) between January and December 2019. The dataset comprises 47,006 urban and 69,455 rural households, providing a nationally representative view of assets, liabilities, and capital expenditure.

The study employs six binary dependent variables to capture different dimensions of financial behavior:

Deposits: Presence of bank deposits.
Safe Financial Assets: Ownership of provident funds, pensions, or life insurance.
Risky Assets: Ownership of shares, bonds, or other securities.
Short-term Borrowings: Access to short-term formal loans.
Medium-term Borrowings: Access to medium-term formal loans.
Long-term Borrowings: Access to long-term formal loans.

Key independent variables include demographic and socio-economic characteristics: gender of the household head, age (and age squared), household size, education level (categorized as illiterate, primary, secondary, and graduation), urban/rural location, occupation (self-employed, salaried, casual labor, other), and household consumption expenditure per capita (used as a proxy for income). Additionally, states are classified into low, medium, and high development categories based on the Human Development Index (HDI).

Methodology
Given the binary nature of the outcome variables (participation vs. non-participation), the study employs a Probit regression model. The model estimates the probability ( $P_i$ ) that a household participates in a specific financial activity ( $Y_i = 1$ ) as a function of a vector of socio-economic characteristics ( $X$ ):

$P_i = \text{Prob}(Y_i = 1 | X) = \Phi(X'\beta)$

Where $\Phi$ represents the cumulative distribution function of the standard normal variable, and $\beta$ is the vector of parameters indicating the effect of changes in $X$ on the probability of participation.

To ensure robustness and uncover heterogeneity, the authors conduct sub-sample analyses controlling for:

Household Economic Status: Splitting the sample into "low" and "high" consumption expenditure groups.
Household Size: Splitting the sample into "small" and "large" households based on the average size.

Key Results
The empirical findings reveal distinct patterns in how demographic factors shape financial behavior:

Gender: Male-headed households are significantly more likely to invest in both safe assets (PF, pension, insurance) and risky assets compared to female-headed households. They also exhibit a higher propensity to borrow across all loan durations. However, gender does not significantly influence the decision to hold bank deposits.
Age: The relationship between age and financial decisions follows an inverted U-shape. The linear age term is positive, while the squared term is negative, indicating that participation in financial markets and borrowing increases with age initially but declines in later years as households become more risk-averse and accumulate wealth.
Household Size: Larger households show a positive association with deposits, safe assets, and borrowing, likely driven by increased financial needs. Conversely, household size has a negative relationship with investment in risky assets, suggesting that larger families are more risk-averse.
Education: Higher education levels (secondary and graduation) are positively associated with participation in safe and risky assets. Interestingly, households with graduation-level education show a shift from short/medium-term loans to long-term credit, indicating a more long-term financial orientation.
Urban vs. Rural: Urban households are more likely to invest in safe financial assets and secure medium- and long-term loans, likely due to better access to formal financial infrastructure. However, they are less likely to hold deposits or engage in short-term borrowing compared to rural counterparts.
Occupation: Self-employed and salaried workers demonstrate higher participation in safe and risky assets compared to casual laborers. Casual laborers, facing irregular income, exhibit higher risk aversion, investing less in risky assets and showing a negative relationship with long-term borrowing.
Regional Development: Households in medium- and high-developed states are more likely to hold diversified portfolios (safe and risky assets) and access formal credit compared to those in low-developed states.

Sub-sample Findings

Economic Status: When controlling for consumption expenditure, the determinants for poor and rich households show variations. For instance, among high-consumption households, primary education has a negative impact on risky asset investment, whereas it is positive for low-consumption households. This suggests that wealthier households with lower education may rely on other income sources and avoid risk, while poorer households may take risks to augment income.
Household Size: Small-sized households in developed states borrow at higher rates, potentially due to negative savings, whereas large-sized households in these regions are more inclined to invest in risky assets to seek higher returns.

Significance and Contributions
The paper claims to make a modest but significant contribution to the literature on household finance in developing economies. Its primary contributions include:

Comprehensive Scope: Unlike prior studies focusing on single financial instruments, this analysis simultaneously examines deposits, safe assets, risky assets, and various borrowing horizons.
Contextual Relevance: It provides empirical evidence specific to India, a large emerging economy with substantial socio-economic heterogeneity, challenging the generalizability of findings from developed nations.
Policy Implications: The findings suggest that while bank deposits are the dominant financial asset, participation in advanced financial products (insurance, provident funds, risky assets) remains limited. The authors argue that policy interventions should focus on financial literacy programs to help households understand complex financial products, thereby fostering greater participation in formal financial markets and reducing reliance on informal credit.

The study concludes that demographic characteristics are robust determinants of household financial behavior in India, and understanding these nuances is essential for designing policies aimed at improving financial inclusion and strengthening household financial resilience.

The Great Family Money Map

The Study: Charting the Indian Ocean

What the Data Revealed

The "What If" Scenarios

The Bottom Line

More like this