This study examines the impact of household debt on consumer spending and disposable personal income in India from 2007 to 2024. The findings indicate that while disposable income has shown a steady increase, household debt initially declined but has been rising since 2020, suggesting growing financial pressures. Consumer spending has also increased, supported by rising incomes, though the recent rise in debt may indicate a growing reliance on credit. The widening gap between disposable income and household debt highlights improved financial stability but necessitates careful financial management. The regression analysis suggests that household debt negatively impacts disposable income (-0.0342) and has a small negative influence on consumer spending (-0.2770), though these relationships are statistically insignificant. Additionally, past consumer spending has a minor negative impact on household debt (-0.0436), implying that increased spending may reduce the need for borrowing. The Wald test confirms that the relationship between household debt, consumer spending, and disposable income is short run in nature, indicating that household debt does not have a significant long-term effect on these economic variables. Overall, the study highlights the need for sustainable borrowing practices and policy measures to maintain financial stability.
Table of Contents
ABSTRACT
1 INTRODUCTION
1.1 Introduction to Topic
1.2 Need for the Study
1.3 Statement of Research Problem
1.4 Objectives of the Study
1.5 Hypothesis of the Study
1.6 Scope of the Study
2 REVIEW OF LITERATURE
2.1 Review of Literature
2.2 Research Gap
3 RESEARCH METHODOLOGY
3.1 Research Design
3.2 Sample Period
3.3 Data Type and Sources
3.4 Variables
3.5 Statistical Tools
4 DATA ANALYSIS AND INTERPRETATION
4.1 Unit Root Test
4.2 Descriptive Statistics
4.3 Trend Analysis
4.4 Vector Autoregression (VAR)
4.5 Ordinary Least Squares (OLS)
4.6 Limitations of the Study.
5 FINDINGS AND CONCLUSION
5.1 Findings
5.2 Suggestions
5.3 Scope for Further Research
5.4 Conclusion
REFERENCE
ABSTRACT
This study examines the impact of household debt on consumer spending and disposable personal income in India from 2007 to 2024. The findings indicate that while disposable income has shown a steady increase, household debt initially declined but has been rising since 2020, suggesting growing financial pressures. Consumer spending has also increased, supported by rising incomes, though the recent rise in debt may indicate a growing reliance on credit. The widening gap between disposable income and household debt highlights improved financial stability but necessitates careful financial management. The regression analysis suggests that household debt negatively impacts disposable income (-0.0342) and has a small negative influence on consumer spending (-0.2770), though these relationships are statistically insignificant. Additionally, past consumer spending has a minor negative impact on household debt (-0.0436), implying that increased spending may reduce the need for borrowing. The Wald test confirms that the relationship between household debt, consumer spending, and disposable income is short run in nature, indicating that household debt does not have a significant long-term effect on these economic variables. Overall, the study highlights the need for sustainable borrowing practices and policy measures to maintain financial stability.
Keywords: Household debt, consumer spending, disposable income, financial stability, borrowing, economic growth, India.
CHAPTER 1 INTRODUCTION
INTRODUCTION
1.1 Introduction to how household debts affect consumer spending and disposable personal income
Household debt encompasses various financial liabilities, including mortgages, personal loans, and credit card balances. It represents a significant portion of a household's financial commitments, influencing overall financial flexibility. As debt obligations rise, households must allocate a portion of their income to repayments, which directly impacts their spending capacity and financial stability. Managing debt effectively is crucial to maintaining economic resilience and ensuring that households do not face financial distress due to excessive borrowing.
Consumer spending, a key driver of economic growth, is directly influenced by the level of household debt. When debt levels are manageable, households can spend on discretionary goods and services, boosting economic activity. However, high debt burdens often lead to reduced consumer spending, as a significant portion of income is diverted toward loan repayments. This reduction in spending can weaken economic growth, as lower consumer demand affects businesses and overall market stability. Additionally, rising debt-service payments can limit purchasing power, making it harder for households to afford essential and non-essential goods.
Disposable personal income (DPI), which refers to the income households retain after paying taxes, plays a critical role in determining financial well-being. Higher debt obligations reduce DPI, as a substantial portion of income is directed toward debt servicing rather than consumption or savings. Households with limited disposable income may struggle to maintain their standard of living, forcing them to cut back on essential expenses such as healthcare, education, and investments. This financial strain can also lead to increased stress and reduced economic participation, further compounding the challenges faced by indebted households.
The impact of household debt on consumer spending varies in the short and long term. In the short term, access to credit can stimulate spending. As households accumulate more debt, a larger portion of their income must be directed toward servicing debt, such as making monthly loan repayments or paying interest on credit balances. This reduces their available income for discretionary spending. Consequently, households may need to cut back on spending in other areas, such as dining out, entertainment, or travel, in order to meet their debt obligations. In cases where debt burdens become excessive, households may face a situation where they are unable to afford even the basic goods and services they need to maintain their standard of living. However, over the long term, high levels of household debt can constrain future consumer spending and economic growth. As households devote a larger portion of their disposable income to debt servicing, they may become financially constrained, unable to spend as freely or invest in their future. High debt burdens can also make households more vulnerable to economic shocks, such as job loss, illness, or rising interest rates, which could make it even harder for them to meet their debt obligations. From an economic perspective, rising household debt can have broader implications for financial stability. If debt levels become unsustainable, the risk of loan defaults increases, potentially leading to financial crises. Policymakers and financial institutions closely monitor household debt trends to ensure economic sustainability.
By implementing responsible lending practices and promoting financial literacy, Governments and financial institutions play a key role in managing household debt levels. By promoting responsible lending practices, such as assessing borrowers’ ability to repay loans before extending credit, policymakers can reduce the risk of excessive borrowing and the financial stress that often follows. Additionally, promoting financial literacy and encouraging savings can help households manage their finances better and reduce reliance on debt. Educating consumers about the long-term impacts of debt and how to make informed financial decisions can empower households to avoid falling into excessive debt. Moreover, governments may also implement policies that directly address the debt burden, such as debt relief programs, stimulus measures, or adjustments to interest rates. These interventions aim to reduce the financial strain on households and stimulate consumer spending, which in turn supports economic growth.
1.2 Need for the study
Understanding the impact of household debt on consumer spending and disposable income in India is crucial for various stakeholders, particularly policymakers, financial institutions, and researchers. For policymakers, these insights are essential in crafting strategies that promote sustainable borrowing and responsible spending habits. With household debt on the rise, it becomes imperative to ensure that borrowing does not lead to long-term financial stress or reduced economic activity. By closely examining the relationship between debt and consumption, policymakers can design targeted interventions such as financial literacy programs, income support measures, or regulatory frameworks to maintain economic stability and support the well-being of citizens.
Financial institutions, meanwhile, rely on this understanding to manage credit risk and create borrower-friendly financial products. Knowing how household debt influences spending and disposable income helps banks and NBFCs tailor their lending practices to ensure responsible borrowing and reduce the chances of default. It enables them to adjust interest rates, repayment terms, and loan structures in line with consumers’ financial capacities. For researchers, this area of study opens up valuable insights into household behaviour, economic resilience, and financial inclusion, offering a foundation for evidence-based policy recommendations. Together, these perspectives help build a more robust and responsive financial system in the Indian context.
1.3 Statement of Research problem
Household debt has been steadily rising, influencing consumer spending patterns and disposable personal income. While debt can facilitate economic growth by enabling consumption and investment, excessive household debt can lead to financial distress and reduced spending capacity. Understanding how household debt impacts consumer behaviour is crucial for policymakers and financial institutions to ensure economic stability.
This study aims to analyse the relationship between household debt, consumer spending, and disposable personal income in India, identifying key trends and factors that drive financial decision-making among households.
1.4 Objectives of the study
1. To analyze the cross-trend between household debt, consumer spending, and disposable income in India.
2. To examine the relationship between household debt, consumer spending, and disposable income in India.
3. To assess the impact of household debt on consumer spending and disposable income in India.
1.5 Hypothesis (Null Hypothesis)
Null Hypothesis (H02): There is no significant relationship between household debt, consumer spending, and disposable income in India.
Null Hypothesis (H03): Household debt has no significant impact on consumer spending and disposable income in India.
1.6 Scope of the study
The present study focuses on “How Household Debt Affects Consumer Spending and Disposable Personal Income” in the context of India, covering a study period from 2007 to 2024. By examining data over these 17 years. This focused scope provides valuable insights into the economic behavior of Indian households and contributes to understanding how debt levels influence consumer spending and income stability over time, offering relevant information for policymakers, financial institutions, and researchers.
CHAPTER 2 REVIEW OF LITERATURE
2.1 REVIEW OF LITERATURE
1. Glenn B. Canner, Arthur B. Kennickell, and Charles A. Luckett (1995) This examined the growth of household debt from 1983 to 1994, focusing on its implications for economic vulnerability and spending sustainability. It highlighted that household debt rose significantly relative to income during the 1983–1989 economic expansion, followed by slower accumulation during the 1990–1992 recession. Using data from the Federal Reserve's Survey of Consumer Finances, the research revealed that debt was concentrated among higher-income households and those with greater net worth. It observed a decline in the share of debt held by households with high debt-to-income ratios from 1989 to 1992, suggesting improved financial conditions. The study concluded that household debt statistics should consider economic and demographic factors for a better understanding of debt trends and their economic impact.
2. María Pía Olivero and Mikheil Dvalishvili (2022) This examined the impact of fiscal stimulus packages on household debt during crises using data from the Consumer Expenditure Survey. They found that government-issued stimulus checks reduced outstanding liabilities, particularly for households with incomes below the median and those employed during the crisis. The findings emphasized the importance of fiscal policies in alleviating financial burdens and highlighted the role of targeted interventions in stabilizing household finances. The study concluded that stimulus packages effectively reduce liabilities and improve economic resilience during challenging times.
3. Francesca Eugeni (1993) examined the importance of consumer debt ratios in evaluating households' financial conditions and forecasting consumer spending. It highlighted that personal consumption is a major driver of U.S. economic growth. The research observed a decline in the consumer instalment credit-to-income ratio since 1990, driven by shifts in borrowing behaviour such as increased reliance on home equity loans and auto leasing. It also noted the limitations of traditional debt measures in capturing evolving financial practices. The study concluded by emphasizing the need for comprehensive debt-to-income ratios to better reflect changing borrowing trends and improve predictions of economic impacts.
4. Serhan Cevik (2022) This examined the effects of the COVID-19 pandemic and government interventions on consumer spending in the Baltics using daily card transaction data. It found that rising COVID-19 infections and public health restrictions had a negative impact on household spending, particularly in sectors affected by lockdowns. However, government economic support measures stimulated spending, especially on goods rather than services. The study highlighted changes in spending patterns and the significant role of policy interventions in mitigating the economic shock of the pandemic. It concluded that understanding such shifts is essential for effective policymaking during crises.
5. Manavi Gupta and Avinash Kishore (2020) This examined the effects of unemployment on household consumption in rural and urban India using panel data. They highlighted that job loss significantly reduced household spending, with rural households being more vulnerable due to limited income sources. The research emphasized the importance of safety net programs in maintaining consumption levels and mitigating the adverse effects of unemployment. The study concluded that targeted interventions are crucial for supporting households during economic downturns and ensuring resilience in the face of employment shocks.
6. Robert G. Murphy (1999) This examined the predictive power of the debt-service-to-income ratio in forecasting consumer spending growth. He found that higher debt burdens lead to tightened lending standards, disproportionately affecting spending on durable goods and services. The findings showed that borrowing-constrained households reduce discretionary spending when faced with increased debt obligations. The study concluded that the debt-service ratio is a vital tool for understanding consumer behavior and emphasized its utility in economic forecasting and policymaking.
7. Fiona Price, Benjamin Beckers, and Gianni La Cava (2019) This examined the relationship between mortgage debt and consumer spending in Australian households, identifying a "debt overhang effect" where high mortgage debt reduced spending, even when net wealth remained constant. They showed that this effect was pervasive across all owner-occupier households and was not limited to financially constrained ones. Additionally, the findings revealed that indebted households reduced spending more during economic shocks, such as the global financial crisis. The study concluded that changes in household debt composition significantly influence spending behavior, challenging traditional macroeconomic models.
8. Katya Kartashova, Xiaoqing Zhou (2020) The study investigates the causal effect of mortgage rate resets on consumer spending, debt repayment, and defaults in Canada, focusing on short-term fixed-rate mortgages, the dominant mortgage type in the country. The study exploits the exogenous variation in mortgage rate resets to examine how changes in rates affect consumer behavior during expansionary and contractionary monetary policy periods. Findings reveal asymmetric responses in durable spending, debt reduction, and defaults, which can be explained by the cash-flow effect and consumer expectations about future interest rates. The study contributes to understanding how households respond to interest rate changes, especially in countries with prevalent short-term fixed-rate mortgages.
9. Yunchao Cai, Selamah Abdullah Yusof, Ruzita Mohd Amin, Mohd Nahar Mohd Arshad (2020) This study examines the impact of household debt on consumption behavior in urban households in Malaysia, particularly in the Klang Valley. Results indicate that debt does not broadly affect consumption, with the exception of vacation expenditures, which are reduced for debt-laden households. Financial wellness is found to be a more significant determinant of consumption decisions, with poorer financial wellness leading to more frequent cuts in essential and non-essential expenses. The study highlights the need for monitoring household debt to prevent negative impacts on household well-being.
10. D. Rooj, Anurag Banerjee, R. Sengupta (2024) The study investigates the relationship between economic policy uncertainty (EPU) and consumer confidence in India, using data from the Reserve Bank of India's Consumer Confidence Survey. The findings show that both India-specific and global EPU negatively affect consumer confidence, with global EPU having a stronger impact. A disaggregated analysis reveals that EPU also influences various consumer confidence indicators, such as outlook on income, spending, employment, and prices. The study concludes that economic policy uncertainty shocks significantly affect household sentiment and perceptions of the economy
11. Apostolos Fasianos, R. Lydon (2021) This research examines how household debt influences consumer spending responses to income and wealth changes. By tracking spending on non-durables from 1993 to 2017, the study finds that indebted households significantly reduce consumption when income falls, but respond less drastically when income rises. The effects of wealth shocks are less consistent. These findings highlight that households with higher debt-to-income ratios are more sensitive to income fluctuations, emphasizing the role of debt in amplifying consumption adjustments during economic changes.
12. N. Tejmani Singh, Nripendra Singh (2012) This study focuses on retail store categorization and patronage behavior in rural India. As rural consumption patterns evolve, the study develops a classification system for retail stores in rural markets and explores how various segments of the rural population prefer different retail formats. The findings offer insights into the changing preferences and behaviors of rural consumers, which can help retailers and policymakers better understand and address the retail needs of rural India. The study sets a foundation for further research into rural retail formats and their patronage dynamics.
13. Birgitta Jansson (2020) This study examines changes in disposable personal income and the gender income gap in Sweden from 1983 to 2010, analyzing three income positions: the 10th percentile, the median, and the 99th percentile. The research reveals that women in the 10th percentile saw significant income gains, primarily due to increased female labor force participation, narrowing the gender gap in low-income households. However, the gender gap at the median widened between 2000 and 2010 for individuals under 50, and at the top of the income distribution, the gap persisted despite substantial income growth. The study highlights the feminization of low-income groups and the ongoing challenge of gender inequality in high-income brackets.
14. Ma. Jesusa Bato, Edilberto Viray (2024) This paper examines the effects of high national debt on household consumption, foreign direct investments (FDI), tax revenues, and economic health. The authors analyze how national debt impacts these variables in the short and long term using the Autoregressive Distributed Lag (ARDL) cointegration technique. They find that high national debt negatively affects household consumption by limiting disposable income and reducing foreign investments due to higher taxes and fiscal uncertainty. The study concludes that effective fiscal and monetary policies are crucial in managing national debt levels to minimize adverse effects on household consumption and investments. Furthermore, the analysis reveals that the growing national debt, along with economic health and FDI, significantly influences household final consumption expenditure both in the short and long run.
15. Jonathan A. Parker, J. Schild, Laura Erhard, David S. Johnson (2022) This study examines the relationship between household consumption and income volatility, particularly focusing on how consumers adjust spending in response to income fluctuations. The authors find that increased income volatility leads to a reduction in household consumption, with consumers opting to save more to buffer against future income uncertainty. The research suggests that households facing more volatile income streams may prioritize saving overspending, even when current income levels are stable. The study concludes that income volatility plays a significant role in determining consumption patterns, and policies aimed at reducing income uncertainty could help stabilize household spending
16. Karen E. Dynan, Wendy Edelberg, (2013) This study explores the relationship between household leverage and spending behavior, using data from the 2007-2009 Survey of Consumer Finances. The authors find that higher levels of household debt leads to reduced consumer spending, particularly when households are more leveraged. The research suggests that households with greater debt burdens tend to cut back on consumption in order to service their debts, especially in the face of economic uncertainty. The study concludes that leveraging, especially in the context of financial shocks, plays a crucial role in shaping household spending patterns, and reducing household debt may be key to stimulating consumer demand in the economy.
17. Hainnur Aqma Rahim, Salimah Yahaya, Khalijah Mohd Nor, Amirah Hazimah Borhanordin (2021) This research examines consumer perceptions of debt in the context of Islamic principles, focusing on how Islamic teachings shape attitudes toward borrowing and financial obligations. The authors find that many consumers perceive debt in Islam as a negative concept, especially when it involves interest, as it conflicts with Islamic principles of financial ethics. However, the study also reveals that there is a growing awareness of the alternatives available, such as Islamic financing methods that avoid interest-based transactions. The research concludes that consumer attitudes toward debt are significantly influenced by Islamic financial principles, and promoting awareness of Sharia-compliant financial products could help align borrowing practices with these values.
18. D. N. Mamyrova, (2024) This paper explores the impact of ceremonial spending on household debt in Kazakhstan. The author investigates how expenditures on cultural and ceremonial events influence household financial behavior, particularly debt accumulation. The study finds that households with higher ceremonial spending are more likely to take on debt, as these expenditures often exceed their disposable income. The research concludes that while ceremonial spending is an important cultural practice, it has a significant impact on household finances, contributing to increased debt levels. The study emphasizes the need for financial literacy programs that address the implications of such spending on personal debt.
19. Peter Ganong & Pascal Noel (2018) This study examines the influence of mortgage modifications on borrower behavior during the Great Recession, focusing on wealth and liquidity changes. Using administrative data and employing regression discontinuity and difference-in-differences methodologies, the research demonstrates that principal reductions, which increase housing wealth without altering liquidity, have no impact on default or consumption. Conversely, maturity extensions, which enhance liquidity without affecting long-term obligations, significantly reduce default rates and boost consumption. The findings underscore the critical role of liquidity in shaping borrower decisions and propose redesigning distressed debt restructuring policies to maximize benefits for borrowers, lenders, and taxpayers.
20. Marina Emiris & François Koulischer (2021) This research investigates the impact of declining interest rates on household borrowing patterns and debt distribution. Using a model and Belgian loan-level data, the study reveals that households with greater pre-existing housing wealth are more responsive to lower interest rates, increasing their borrowing significantly compared to constrained, first-time borrowers. A 1% reduction in interest rates is associated with a 15% rise in household debt, demonstrating high borrowing elasticity and highlighting a shift in debt distribution toward older, wealthier households.
21. Alison Johnston, Gregory W. Fuller & Aidan Regan (2020) This paper analyzes the interaction between mortgage and labor markets in Europe and their role in rising household debt, particularly during the global financial crisis of 2007-2008. The study highlights how liberalized mortgage markets, combined with labor market conditions, exacerbated systemic risks. The authors argue that the convergence of these markets underscores the need for coordinated policy interventions to mitigate the financial vulnerabilities linked to household debt.
22. Andrew F. Haughwout, Donghoon Lee, Joelle Scally, Lauren Thomas & Wilbert van der Klaauw (2019) This study examines household debt trends from the 2000s housing bubble through the Great Recession and recovery. The findings reveal a historic surge in debt driven by housing, followed by widespread mortgage defaults during the crisis. Post-recession, households rapidly reduced debt before borrowing increased again in 2013, albeit at a slower pace under stricter lending standards. The analysis shows significant changes in the composition and characteristics of household debt, offering a nuanced understanding of evolving borrowing behaviors.
2.2 Research gap
The interrelationship between household debt, consumer spending, and disposable income is critical in understanding economic stability and growth. However, existing research largely focuses on developed economies, where income levels, debt structures, and consumer behavior differ markedly from those in emerging markets like India. In the Indian context, limited studies have thoroughly examined how household debt interacts with consumer spending and disposable income. Additionally, studies that conduct a cross-trend analysis of these variables over time, capturing the effects of economic cycles, are notably scarce. Addressing this gap, this study aims to provide a comprehensive analysis of the trends, relationships, and impact of household debt on consumer spending and disposable income within India.
CHAPTER 3 RESEARCH METHODOLOGY
RESEARCH METHODOLOGY
This study adopts a descriptive and quantitative research approach to analyse how household debt influences consumer spending and disposable personal income in India over a 17-year period (2007–2024). The descriptive aspect focuses on identifying historical trends and patterns in economic indicators. Meanwhile, the quantitative component applies statistical techniques to measure the strength and significance of relationships between household debt and financial behaviour. By integrating trend analysis and econometric modelling, the study provides both a broad overview and an in-depth assessment of economic dynamics. This dual approach ensures a comprehensive evaluation of debt’s impact on consumer decision-making and income stability
3.1 Research Design
A descriptive design is employed to capture and present historical data trends for household debt, consumer spending, and disposable income, providing a clear overview of how these variables have evolved over the study period. The quantitative component involves statistical analyses, such as regression and correlation tests, to evaluate the strength and significance of the relationships between household debt and the dependent variables—consumer spending and disposable income. This mixed approach allows the study to provide both a broad understanding of trends and an in-depth analysis of relationships.
3.2 Sample Period
The study covers a 17-year sample period from 2007 to 2024, a timeframe that includes various economic cycles, policy changes, and global economic events. This extended period helps to capture both short-term fluctuations and long-term trends, providing a robust basis for understanding how household debt interacts with consumer behavior and income levels over time.
3.3 Data Collection
This study relies on secondary data sourced from “Trading Economics”, which provides access to reliable and comprehensive economic indicators. This data source offers historical data on household debt, consumer spending, and disposable income, ensuring that the analysis is based on high-quality and consistent information across the study period.
3.4 Variables:
Dependent Variables:
Consumer Spending: This variable represents the total expenditures by households on goods and services, a critical measure of economic activity and household financial health.
Disposable Personal Income : This is the amount of income remaining after taxes that households have available for spending or saving, reflecting their capacity to manage both spending and debt levels.
Independent Variable:
Household Debt: This variable reflects the total debt held by households, including loans, credit, and other financial obligations. It is the primary factor of interest in assessing its potential impact on consumer spending and disposable income.
3.5 Statistical Tools
Unit Root Test: Unit root tests, such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, are used to assess whether a time series is stationary or non-stationary. Stationarity implies that the statistical properties of the series—like mean, variance, and autocorrelation are consistent over time. A non-stationary series, on the other hand, displays trends, seasonal patterns, or other structures. ADF and PP tests are generally used to check for the presence of a unit root, where the null hypothesis is that the series is non-stationary. If the null hypothesis is rejected, the series is stationary. The KPSS test complements this by checking for stationarity as the null hypothesis, where rejection indicates non-stationarity. Ensuring stationarity is crucial for many time series analyses as it affects the accuracy and interpretability of models applied to the data.
Descriptive Statistics: Descriptive statistics summarize the basic features of the dataset, offering insights into the data's central tendency, variability, and distribution shape. Common measures include the mean, median, and mode for central tendency, and standard deviation, variance, and range for variability. Skewness and kurtosis can also be analyzed to understand asymmetry and tail heaviness. By summarizing the dataset, descriptive statistics provide a foundation for understanding the characteristics of the data, enabling further model selection and testing in the context of time series or regression analysis.
Trend analysis: Trend analysis is the practice of examining historical data to identify consistent patterns or trends over time. In time series data, trends can reflect long-term upward or downward movements in values, driven by underlying factors such as economic growth, technological advancements, or seasonal shifts. By identifying trends, analysts can better understand the general direction of a dataset, distinguish between long-term tendencies and short-term fluctuations, and make more informed predictions about future behavior. Trend analysis is essential for decision-making in fields like finance, economics, and business strategy.
Vector Error Correction Model (VECM): The Vector Error Correction Model (VECM) is an econometric model designed for use with non-stationary, cointegrated series. VECM combines both short-term adjustments and long-term equilibrium relationships between dependent and independent variables. It captures short-term dynamics while ensuring that the variables converge towards a long-term equilibrium over time. The error correction term shows how much of the deviation from the long-term relationship is corrected in each time period, making VECM effective for studying cointegrated data where equilibrium adjustments are of interest.
Ordinary Least Squares (OLS): Ordinary Least Squares (OLS) regression is a fundamental technique used to determine the linear relationship between an independent variable and a dependent variable. In OLS, coefficients are estimated to minimize the sum of the squared differences between observed and predicted values, thereby providing a measure of the independent variable's impact on the dependent variable. OLS assumptions, such as linearity, independence, and homoscedasticity, are critical to producing unbiased and consistent estimates, making OLS a common choice for simple linear regressions.
CHAPTER 4 DATA ANALYSIS AND INTERPRETATION
DATA ANALYSIS AND INTERPRETATION
4.1 Unit root test
Introduction
Unit root tests like the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) help determine whether a time series is stationary or non-stationary. ADF and PP tests check for the presence of a unit root, with the null hypothesis assuming non-stationarity, and rejection indicating a stationary series. The KPSS test, in contrast, assumes stationarity as the null hypothesis, and rejection suggests non-stationarity. Ensuring stationarity is essential for accurate time series modelling, as it impacts the reliability and interpretability of analytical results.
Unit Root Test of Disposable Income
The following is the hypothesis:
· Null Hypothesis: Disposable income data is not stationary.
· Alternative Hypothesis: Disposable income data is stationary.
Table 4.1.1
Unit Root Test of Disposable Income
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The table represents the unit root test of disposable income. The t-statistic calculated value is -3.751348, which is less than the 5% critical value (-3.144920), and the p-value (0.0184) is less than 0.05. Using the ADF test, the study rejects the null hypothesis and accept the alternative hypothesis. Thus, disposable income data is stationary.
Unit Root Test of Household Debt
The following is the hypothesis:
· Null Hypothesis: Household debt data is not stationary.
· Alternative Hypothesis: Household debt data is stationary.
Table 4.1.2
Unit Root Test of Household Debt
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
This table presents the results of the Augmented Dickey-Fuller (ADF) unit root test conducted on household debt data to determine its stationarity. The t-statistic value of -6.604725 is significantly lower than the 5% critical value of -3.081002, and the corresponding p-value is 0.0001, which is well below the standard significance level of 0.05. These results provide strong statistical evidence to reject the null hypothesis, which states that the data has a unit root and is non-stationary. By rejecting the null and accepting the alternative hypothesis, the analysis concludes that the household debt data is stationary. This means that the statistical properties of the series, such as mean and variance, remain constant over time, making it suitable for further econometric modeling and analysis.
Unit Root Test of Consumer Income
The following is the hypothesis:
· Null Hypothesis: Consumer income data is not stationary.
· Alternative Hypothesis: Consumer income data is stationary.
Table 4.1.3
Unit Root Test of Consumer Income
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The Augmented Dickey-Fuller (ADF) unit root test is a fundamental statistical tool used to assess the stationarity of a time series a critical concept in time series analysis. Stationarity implies that the series has a consistent mean and variance over time, making it predictable and suitable for modeling. When a series is non-stationary, it often exhibits trends, seasonality, or structural breaks, which can distort statistical inferences and lead to unreliable forecasts.
In this case, the ADF test was conducted on consumer income data to evaluate whether the series is stationary. The results yielded a t-statistic of -3.751348, which is more negative than the 5% critical value of -3.144920. This means that the test statistic falls within the rejection region of the null hypothesis, which assumes the presence of a unit root—i.e., that the data is non-stationary. Furthermore, the p-value associated with the test is 0.0184, which is below the commonly used significance level of 0.05. This low p-value provides additional evidence against the null hypothesis. Together, these results strongly indicate that the consumer income series does not have a unit root and is thus stationary.
4.2 Descriptive Statistics
Introduction
Descriptive statistics summarize key features of a dataset, including central tendency (mean, median, mode) and variability (standard deviation, variance, range). They also assess skewness and kurtosis to understand distribution shape. These insights help in data interpretation, model selection, and further analysis in time series or regression.
Table 4.2.1
Descriptive statistics of consumer spending, disposable income and household debts
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The descriptive statistics table summarizes 18 observations of consumer spending, disposable income, and household debt. Disposable income shows the highest mean (159.48) and variability (Std. Dev. = 81.12), while consumer spending and household debt have lower means (15.72 and 37.69) and standard deviations (6.22 and 3.59). Skewness values indicate that consumer spending is slightly left-skewed, while the other two variables are moderately right-skewed. All kurtosis values are below 3, suggesting flat distributions. Jarque-Bera test results (p > 0.05) confirm normality for all variables, supporting their use in further analysis.
4.3 Trend analysis
Table 4.3.1
Trend analysis of disposable income, consumer spending and household debts
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Table 4.3.2
Trend analysis of disposable income and household debts
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Figure 4.3.1
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The trend analysis of household debt and disposable income from 2007 to 2024 reveals contrasting patterns. upward Disposable income shows a consistent trend, increasing from ₹50.5 in 2007 to ₹305 in 2024, reflecting significant economic growth and rising earnings. In contrast, household debt initially declined from ₹44.23 in 2007 to ₹33.125 in 2016, indicating reduced borrowing or improved debt repayment, before gradually increasing to ₹42.25 in 2024. The year-on-year growth in disposable income has been substantial, especially from 2016 onward, with major jumps in 2021 (₹35 increase) and 2022 (₹37 increase), suggesting improved financial conditions. Meanwhile, household debt remained relatively stable after 2016 but began increasing from 2020 onwards, reaching ₹42.25 in 2024, possibly due to rising financial needs or increased borrowing. The gap between disposable income and household debt has widened significantly, indicating better financial stability, though the recent rise in debt warrants attention for sustainable economic growth.
Table 4.3.3
Trend analysis of consumer spending and household debts
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Figure 4.3.2
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The trend analysis of consumer spending and household debt from 2007 to 2024 highlights a steady rise in consumer spending, increasing from ₹6.02 in 2007 to ₹25.02 in 2024, reflecting improved purchasing power and economic growth. Household debt, on the other hand, initially declined from ₹44.23 in 2007 to ₹33.12 in 2016, indicating reduced borrowing, before rising again to ₹42.25 in 2024, suggesting increased financial obligations. The year-on-year growth in consumer spending has been consistent, with a significant rise after 2010, particularly in 2011 (₹3.37 increase), 2012 (₹1.71 increase), and 2022 (₹1.72 increase). In contrast, household debt remained stable from 2016 to 2020 but started increasing from 2021 onwards, reaching ₹42.25 in 2024, possibly due to higher borrowing needs. The widening gap between consumer spending and household debt suggests that while spending capacity has improved, the recent rise in debt may indicate increasing reliance on credit, requiring careful financial management for long-term stability.
4.4 VECM Between Disposable Income and Household
Vector Error Correction Model (VECM): The Vector Error Correction Model (VECM) is an econometric model designed for use with non-stationary, cointegrated series. VECM combines both short-term adjustments and long-term equilibrium relationships between dependent and independent variables. It captures short-term dynamics while ensuring that the variables converge towards a long-term equilibrium over time. The error correction term shows how much of the deviation from the long-term relationship is corrected in each time period, making VECM effective for studying cointegrated data where equilibrium adjustments are of interest.
STEP-1: VAR Lag Order Selection
Table 4.4.1
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Author
Interpretation
The VAR Lag Order Selection Criteria table helps determine the optimal number of lags for analyzing the relationship between household debt and disposable income. Various criteria such as the Likelihood Ratio (LR) test, Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn (HQ) Criterion are used for selection. The presence of stars (*) in the table indicates the best lag order according to each criterion. Here, Lag 1 is selected as the optimal lag since it has the lowest values for FPE, AIC, SC, and HQ, and the LR test also confirms its significance. This suggests that a one-period lag effectively captures the relationship between disposable income and household debt, making it the most suitable choice for further analysis.
Step-2: Vector Error Correction
Table 4.4.2
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
System Equation
D(DISPOSABLE_INCOME)=C(1)*(DISPOSABLE_INCOME(-1) - 7.80016218488*HOUSEDEBT(-1) + 131.412250841 ) + C(2)*D(DISPOSABLE_INCOME(-1)) + C(3)*D(HOUSEDEBT(-1)) + C(4)
C(1)=c(3)=0
Interpretation
The table presents the relationship between household debt (independent variable) and disposable income (dependent variable). The coefficient of D(HOUSEDEBT) is -0.0342, indicating a negative relationship between the two variables. This suggests that a one-unit increase in household debt leads to a 0.0342-unit decrease in disposable income. This inverse relationship implies that as households take on more debt, their disposable income decreases, likely due to higher debt repayments, interest obligations, and reduced financial flexibility. This trend highlights the potential burden of debt on household finances, limiting available income for savings and consumption.
Step-3: Wald Test
Table 4.4.3
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The Wald test is a statistical technique commonly used to evaluate the joint significance of coefficients in a regression model. In the context of this study, the Wald test is employed to examine whether there is a long-run relationship between disposable income and household debt. Specifically, it tests the null hypothesis that the coefficients of the variables in the long-run equation are jointly equal to zero, indicating no significant relationship between them over the long term.
In this case, the Wald test produced a Chi-square value of 0.6244 and a corresponding p-value of 0.7319. Since this p-value is considerably higher than the standard significance level of 0.05, there is insufficient evidence to reject the null hypothesis. This outcome suggests that disposable income and household debt do not share a statistically significant long-run equilibrium relationship. In simpler terms, changes in household debt do not appear to have a lasting or predictable effect on disposable income, and vice versa, over an extended period. The lack of a strong long-term association implies that the interaction between these variables may be more relevant in the short run, possibly influenced by temporary economic conditions, policy shifts, or consumer behaviour, rather than a stable long-term trend.
VECM for consumer spending and household debt
Step:1 VAR Lag Order Selection
Table 4.4.4
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The VAR Lag Order Selection Criteria table determines the optimal lag length for analyzing the
relationship between consumer spending and household debt. The presence of multiple asterisks (*) at lag 1 across different selection criteria—Likelihood Ratio (LR) test, Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn Criterion (HQ)—indicates that lag 1 is the most suitable choice for the model. This suggests that the previous year's values of consumer spending and household debt significantly influence their current values, making a one-period lag the optimal selection for capturing the dynamics between these variables.
Step:2: Vector Error Correction
Table 4.4.5
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
System Equation
D(CONSUMER_SPENDING)=C(1)*(CONSUMER_SPENDING(-1) - 0.474879946922*HOUSEDEBT(-1) + 1.82852678611 ) + C(2)*D(CONSUMER_SPENDING(-1)) + C(3)*D(HOUSEDEBT(-1)) + C(4) C(1)=c(3)=0
Interpretation
The given table presents the relationship between household debt (independent variable) and consumer spending (dependent variable), with the coefficient of D(CONSUMER_SPENDING(-1)) being -0.043641. This negative coefficient suggests that a 1-unit increase in lagged consumer spending is associated with a 0.0436 unit decrease in household debt, indicating an inverse relationship. However, the magnitude of the impact is relatively small, implying that past consumer spending has a minimal but negative effect on household debt in the following period. This could suggest that as consumers spend more, they may rely less on debt, or other economic factors might be influencing the relationship.
Step:3 Wald Test
Table 4.4.6
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The Wald test results indicate a Chi-square value of 0.485284 with 2 degrees of freedom and a p-value of 0.7846. Since the p-value is greater than 0.05, we fail to reject the null hypothesis, suggesting that there is no significant long-run relationship between consumer spending and household debt. This implies that changes in household debt do not have a substantial long-term impact on consumer spending. The results indicate that any relationship between the two variables is likely short-term or influenced by other external factors rather than a strong long-run dependence.
4.5 Ordinary Least Squares (OLS)
Introduction
Ordinary Least Squares (OLS): Ordinary Least Squares (OLS) regression is a fundamental technique used to determine the linear relationship between an independent variable and a dependent variable. In OLS, coefficients are estimated to minimize the sum of the squared differences between observed and predicted values, thereby providing a measure of the independent variable's impact on the dependent variable. OLS assumptions, such as linearity, independence, and homoscedasticity, are critical to producing unbiased and consistent estimates, making OLS a common choice for simple linear regressions.
Ordinary Least Squares (OLS) for Consumer spending and household debt
Table 4.5.1
Consumer spending and household debt
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The Ordinary Least Squares (OLS) regression analysis is conducted to explore the relationship between household debt and consumer spending in India, with household debt serving as the independent variable and consumer spending as the dependent variable. The regression output reveals a coefficient of -0.2770 for household debt, indicating a negative relationship. This suggests that an increase in household debt is associated with a decrease in consumer spending. Economically, this could imply that as households take on more debt, they may divert a larger share of their income toward debt repayment, leaving less disposable income available for consumption.
However, the statistical significance of this relationship is limited. The t-statistic of -0.6486 and the corresponding p-value of 0.5258 are well above the conventional significance threshold of 0.05, indicating that the negative relationship observed is not statistically significant. In other words, the data does not provide strong enough evidence to confidently conclude that household debt has a meaningful effect on consumer spending during the period under study. Furthermore, the R-squared value of 0.0256 reveals that only 2.56% of the variation in consumer spending is explained by household debt. This relatively low explanatory power suggests that other variables such as income levels, inflation, interest rates, employment conditions, and consumer confidence may play a more substantial role in influencing consumer spending in India. Overall, these results indicate that while household debt may have some influence, it is not a dominant driver of consumption trends, at least in the short run covered by this analysis.
Ordinary Least Squares (OLS) for Disposable income and household
Table 4.5.2
Disposable income and household
Illustrations are not included in the reading sample
Source: Data analysed and compiled by Authors
Interpretation
The Ordinary Least Squares (OLS) regression analysis evaluates the impact of household debt (independent variable) on disposable income (dependent variable) in India. The coefficient of household debt is 0.8504, suggesting a positive relationship, meaning that an increase in household debt is associated with a slight increase in disposable income. However, the t-statistic of 0.1507 and the p-value of 0.8821 indicate that this relationship is statistically insignificant. Additionally, the R-squared value is 0.0014, implying that household debt explains only 0.14% of the variation in disposable income, meaning other economic factors likely play a more dominant role. Given these findings, household debt does not have a meaningful impact on disposable income in the analyzed period, suggesting that factors such as wages, employment levels, and government policies may be more influential in determining disposable income in India
4.6 Limitations of the study
1. Household debt, consumer spending, and disposable income are influenced by various external factors, such as inflation, interest rates, government policies, and global economic conditions were not focused.
2. The analysis may not fully account for sudden or structural changes in the economy or in household borrowing behaviors, such as shifts due to new financial regulations, technological advancements in lending, or changes in household consumption patterns.
3. The study relies on secondary data, which may be subject to reporting errors, inconsistencies, or revisions that could affect the accuracy and reliability of the findings.
4. The sample size is limited (18 observations), which may restrict the generalizability of the results and reduce the statistical power of the analysis.
5. The study primarily uses quantitative methods and may overlook qualitative aspects, such as consumer sentiment, behavioural factors, or cultural influences on spending and borrowing habits.
CHAPTER 5 FINDINGS AND CONCLUSION
FINDINGS AND CONCLUSION
5.1 Findings of the study
1. The study shows that disposable income has consistently increased from ₹50.5 in 2007 to ₹305 in 2024, while household debt initially declined but began rising again from 2020. This indicates improved financial stability but also a recent increase in borrowing, indicating potential financial pressures.
2. The study also highlights that consumer spending rose significantly from ₹6.02 in 2007 to ₹25.02 in 2024, while household debt showed a fluctuating pattern, decreasing until 2016 and then rising again. This implies that rising income levels have supported spending, but the recent debt increase could indicate growing reliance on credit.
3. The study reveals that the gap between disposable income and household debt has widened, with income growing at a faster pace. However, debt has been rising since 2020. This indicates improved purchasing power and financial stability, but the recent increase in debt highlights the need for careful financial management.
4. The study indicates that household debt negatively related with disposable income, as shown by the coefficient (-0.0342), indicating that higher debt leads to lower disposable income. This highlights the financial burden of debt, reducing available income for savings and consumption.
5. The study also indicates that past consumer spending has a small negative impact on household debt, with a coefficient of -0.0436, indicating that higher spending may reduce the need for borrowing. This indicates that increased consumer spending could be supported by income rather than debt.
6. The Wald test indicates that the relationship between household debt, disposable income, and consumer spending is short-run in nature. This indicates that changes in household debt do not have a significant long-term impact on disposable income or consumer spending.
5.2 Suggestions
1. Behavioural and Psychological Factors:
While the study focuses on numerical trends, incorporating consumer psychology can provide a deeper understanding of spending habits. Factors like financial literacy, risk tolerance, and economic optimism/pessimism influence how households manage debt and spending. Including these perspectives can strengthen the analysis.
2. Debt Composition and Spending Patterns:
Not all household debt has the same impact on consumer spending. Secured loans (such as home loans) might encourage long-term financial planning, while unsecured debt (such as credit card debt) could lead to impulsive spending. A breakdown of these debt types can help explain variations in spending behaviour and disposable income.
3. Impact of Major Economic Events:
Significant financial disruptions like the 2008 global financial crisis, 2016 demonetization, and the COVID-19 pandemic have shaped household debt patterns and consumer spending in India. Highlighting these events can provide context for fluctuations in the data and improve the interpretation of long-term trends.
4. Justification of Econometric Models:
The use of statistical models like OLS regression, VECM, and unit root tests is essential for reliability. A brief explanation of why these specific models were chosen can enhance clarity. For example, explaining that VECM is used because household debt and consumer spending may have a long-term equilibrium relationship makes the methodology more robust.
5. Policy and Practical Implications:
The findings of this study can inform financial policies on debt management, household savings, and responsible lending. Discussing how governments and financial institutions could use these insights to regulate consumer credit, financial education, and promote responsible borrowing.
5.3 Further Scope for Research
· A larger and more diverse sample should be considered to better understand household debt trends across different income groups, professions, and family structures.
· Analyzing the impact of financial literacy on debt management and savings behavior can provide insights into how education influences financial decisions.
· Studying regional variations in debt and spending patterns across urban and rural households would help identify location-specific financial challenges.
· The role of digital lending platforms and fintech innovations in shaping consumer debt behavior should be explored to assess their long-term impact on financial stability.
· A focused study on post-pandemic financial recovery trends can provide a clearer picture of how households are managing debt, savings, and expenditures in the new economic landscape.
5.4 CONCLUSION
This study explores the impact of household debt on consumer spending and disposable personal income in India from 2007 to 2024, using a combination of descriptive and quantitative research methods. The findings indicate that while disposable income has steadily increased, household debt has shown a fluctuating trend, with a notable rise since 2020. Consumer spending has also grown significantly, supported by rising income levels, but the increasing reliance on credit suggests potential financial vulnerabilities. The study highlights the widening gap between income and household debt, emphasizing the need for effective debt management strategies to sustain financial stability.
The regression analysis suggests a negative relationship between household debt and disposable income, indicating that rising debt levels reduce available income for consumption and savings. However, the study finds that past consumer spending has a small negative impact on household debt, implying that higher spending may reduce the need for borrowing. Furthermore, the Wald test results confirm that the relationship between household debt, disposable income, and consumer spending is short-run in nature, indicating that household debt does not have a significant long-term impact on these variables.
Overall, the findings suggest that while household debt plays a role in shaping economic behaviour. It is not the primary determinant of consumer spending and disposable income. Other economic factors, such as wages, employment, and government policies, are likely more influential. Given the rising trend in household debt, policymakers should focus on promoting responsible borrowing, enhancing financial literacy, and implementing measures to mitigate financial risks for households. Effective debt management policies will be crucial in ensuring long-term economic stability and sustainable consumption patterns in India.
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- P. Y. Radhika (Author), G. Manasa (Author), Pallavi Kencha (Author), Kruthika Manigandla (Author), M. Veera Swamy (Author), M. Arul Jothi (Author), 2024, The Short-Run Impact of Household Debt on Consumer Spending and Disposable Income in India (2007–2024), Munich, GRIN Verlag, https://www.grin.com/document/1577600
