Inflation is a critical economic indicator that not only reflects the general increase in prices of goods and services over time but also profoundly influences consumer behaviour and economic decision making.Hyderabad, historically a centre of trade and commerce, has witnessed rapid urbanization and economic diversification in recent decades.Theoretical perspectives on consumer behaviour suggest that perceptions of inflation can lead to varied adaptive strategies, impacting overall economic stability. Beyond its objective measurement lies the subjective perception of inflation, where how individuals perceive price changes can significantly shape their financial strategies and consumption patterns. The study focuses on exploring the impact of perceived inflation on household budget allocation in Hyderabad, India. Hyderabad, known for its cultural diversity and economic vibrancy, provides an ideal setting to examine these dynamics within the context of an emerging economy. The city's demographic diversity—from affluent urban neighbourhoods to growing middleclass suburbs—offers a diverse range of perspectives on economic issues such as inflation. Understanding how households perceive inflation and allocate their budgets in response is crucial for grasping consumer behaviour in urban India.The gap between perceived and actual inflation can lead to misinformed financial decisions, highlighting the necessity of understanding this phenomenon.

NEED OF THESTUDY:

Understanding how households perceive and respond to inflation is essential for several reasons. The study provides insights into the economic realities faced by urban populations, particularly in rapidly growing cities like Hyderabad, India.Insight into inflation perception can help in designing targeted financial education programs to mitigate adverse consumer reactions.By examining how inflation perceptions influence budget allocation between essentials (such as food and utilities) and nonessentials (including entertainment and luxury items), the study offers practical implications for policymakers and businesses. These insights can inform strategies aimed at enhancing economic stability, consumer welfare, and sustainable development in urban areas.

SCOPE OF THE STUDY:

This study focuses on exploring how perceived inflation influences household budget allocation in the urban context of Hyderabad, India. It aims to offer insights that can inform policymakers, businesses, and financial institutions on strategies to enhance economic stability and consumer welfare amidst fluctuating inflation rates in urban India. By focusing on the practical implications of inflation perceptions, the study contributes to a deeper understanding of economic behaviour and decision making at the household level in Hyderabad.Insights drawn from this study can be instrumental in crafting urban-specific economic policies and interventions.

OBJECTIVES OF THE STUDY:

1. To identify demographic factors (age, income, education level) that affect inflation perceptions and budget allocation.
2. To analyse how these perceptions influence their budget allocation for essentials (food, utilities) versus nonessentials (entertainment, luxury items).
3. To understand how households in Hyderabad, India, perceive inflation.

Zaheeruddin, M (2018) in their article titled “Inflationary Trends and Its Impact on Consumption of Essential Commodities in Hyderabad City”examines how inflationary trends influence the consumption patterns of essential commodities among urban households. By analysing inflationary trends over a period, the dissertation highlights how rising prices impact household budgets and consumption choices. The research employs both quantitative and qualitative methods to gather data, providing a robust framework for understanding the economic implications of inflation on urban consumers.

Zaheeruddin.M, and Ahmed M.O.(2018) in their article titled “Inflationary trends of selected essential commodities with special reference to city of Hyderabad”, focuses on analysing how inflation impacts the pricing and consumption patterns of essential goods within the urban context of Hyderabad. The study provides valuable insights into economic dynamics and consumer behaviour, offering empirical data to understand the challenges and implications of inflation on household budgets and market trends in the city.

Yadav, s., & Shankar(2016), in their article titled “Inflation expectations and consumer spending in India: evidence from the Consumer Confidence survey”explores how consumer perceptions of future inflation influence their spending behaviour, providing empirical evidence on the interplay between inflation expectations and economic decisions at the household level. By analysing survey data, the study contributes to understanding consumer behaviour in response to inflationary expectations, offering insights that are crucial for economic policy formulation and market forecasting in India.

Pervar, P. (1993). “Effects of landuse policies on land prices in middle income housing, Hyderabad, India”, focuses on understanding how regulatory frameworks and urban planning policies influence land values and housing affordability in the city. By analysing these dynamics, the author provides insights into the socioeconomic implications of landuse regulations on housing markets, highlighting the complexities of urban development and policy interventions in Hyderabad, India.

Pothula, J. D. (1992). In their study titled “Residential location of Low-Income households in Hyderabad, India”,focuses on identifying factors influencing the residential choices of low-income families, including economic constraints, accessibility to amenities, and sociodemographic characteristics. By analysing these factors, the author contributes to understanding the socioeconomic dynamics of urban poverty and housing in Hyderabad, shedding light on the challenges and implications for urban planning and social policy in India.

Tiwari, P., & Hingorani, P.(2014) article “An Institutional Analysis of Housing and Basic Infrastructure Services for All: The Case of Urban India”examines the institutional frameworks governing housing and basic infrastructure services in urban India. The study investigates the challenges and gaps in providing equitable access to housing and essential services across urban areas in India, focusing on institutional barriers, policy interventions, and governance issues. By analysing these aspects, the study offers insights into the complexities of urban development, highlighting the need for inclusive policies and effective governance to address housing and infrastructure deficits in rapidly urbanizing regions like India.

Bhatia, R. (1988) article titled “Energy Pricing and Household Energy Consumption in India”, examines how pricing mechanisms influence energy consumption behaviours among households, focusing on factors such as income levels, technological advancements, and policy interventions. By analysing these dynamics, Bhatia contributes to understanding the economic implications of energy pricing on household budgets and energy use efficiency in the context of India's energy sector.

The study employs a mixed method approach, incorporating both primary and secondary data sources to analyse the impact of perceived inflation on household budget allocation in Hyderabad, India.

PRIMARY DATA: Primary data collection involves conducting surveys among a sample of 72 households across diverse socioeconomic backgrounds in Hyderabad. A random sampling technique is utilized to ensure representation from various demographic segments within the city.

SECONDARY DATA: Secondary datagathered from existing literature, reports, and statistical databases related to inflation trends, economic indicators, and consumer behaviour patterns in Hyderabad.

SAMPLE SIZE AND SAMPLING TECHNIQUE:

Sample size :72

Sampling technique: Random sampling technique.

Statistical Tools and Techniques:

1. Correlation Analysis: Used to examine the relationship between variables such as inflation perceptions and budget allocation for essentials versus nonessentials. It helps identify the strength and direction of associations among these factors.
2. Multiple Regression Analysis: Employed to explore the impact of inflation perceptions (independent variable) on household budget allocation (dependent variable), while controlling for demographic variables like age, income, and education level. This technique enables the identification of significant predictors and their respective contributions to budgetary decisions.

ANALYSIS:

H0:The frequency of noticing price changes in everyday goods and services is not influenced by various demographic factors, perceived inflation rate, and concerns about inflation.

H1: The frequency of noticing price changes in everyday goods and services is influenced by various demographic factors, perceived inflation rate, and concerns about inflation.

Table: 1 Regression Model Summary Table

Source: Computed Data

INFERENCE: The model summary indicates that the independent variables are highly effective in explaining variations in how frequently individuals notice price changes in everyday goods and services. With an R² value of 0.934, the model explains about 93.4% of the variance in the dependent variable, which is a strong level of explanatory power. The Adjusted R² of 0.923 confirms that the model remains reliable even when accounting for the number of independent variables. The standard error of the estimate is relatively low at 0.304, suggesting a good fit between the dependent and independent variables. The F statistic of 87.835, with a significance level of 0.000, indicates that the overall model is statistically significant, indicating that the predictors have a meaningful impact on the dependent variable. However, the Durbin-Watson statistic of 1.155 suggests some positive autocorrelation in the residuals, which could imply that the model might have some issues with the independence of residuals. Despite this, the model's high explanatory power and statistical significance make it a strong fit for the data.

Table: 2 Analysis of Variance

Source: Computed Data

INFERENCE: The above ANOVA table 2 shows that the regression model is highly significant in explaining variations in how frequently price changes are noticed. The F-statistic of 87.835, with a significance level of 0.000, indicates that the independent variables collectively have a strong impact on the dependent variable. The model accounts for a large portion of the variance in the dependent variable, demonstrating its effectiveness.

Table:3 Coefficient from Multiple Regression Analysis

Source: computed data

INFERENCE: The multiple regression analysis aimed to explore factors influencing the frequency of noticing price changes in everyday goods and services. The results indicate several significant predictors. Age shows a significant negative impact (B = -.477, p = .000), suggesting older individuals are less likely to notice price changes frequently. Higher education level significantly increases the frequency of noticing price changes (B = 3.268, p = .000). Current employment status negatively impacts this frequency (B = -.198, p = .005), indicating employed individuals are less likely to notice price changes. Higher household income is associated with a greater frequency of noticing price changes (B = .209, p = .004). The perceived inflation rate in Hyderabad significantly increases the frequency of noticing price changes (B = .679, p = .000). On the other hand, increased spending on essential items due to perceived inflation reduces the frequency of noticing price changes (B = -.375, p = .001). Spending on nonessential items due to perceived inflation shows a marginally significant positive impact (B = .330, p = .051). Factors like gender, concern about inflation's impact, and prioritizing spending when perceiving price increases do not significantly influence the frequency of noticing price changes. These findings highlight the importance of demographic and perception-related variables in shaping consumer sensitivity to price changes.

Table: 4 Residual statistics

Source: Computed Data

INFERENCE: The above Residuals Statistics table provides key details about the residuals from the regression model. The Independent Value ranges from 0.85 to 5.25, with a mean of 3.86 and a standard deviation of 1.060, indicating the range and dispersion of the predicted values for how frequently price changes are noticed. The Residualvalues, which represent the difference between the observed and predicted values, vary from -1.275 to 0.965, with a mean of 0.000 and a standard deviation of 0.282. This shows that the residuals are fairly small and centered around 0(zero), suggesting a good fit of the model to the data. The Std. Predicted Value ranges from -2.847 to 1.307, with a mean of 0.000 and a standard deviation of 1.000. This standardizes the predicted values, showing their dispersion relative to the mean. The Std. Residual values, which are the residuals standardized, range from -4.200 to 3.179, with a mean of 0.000 and a standard deviation of 0.928. These values indicate how much the residuals deviate from the mean residual. The relatively wide range of standardized residuals suggests some variation in prediction accuracy across observations but is typical in regression analysis.

The study has provided significant insights into the pervasive influence of cognitive biases on financial decision-making processes. The primary data, collected from a diverse sample of individuals across various districts in Telangana, revealed that cognitive biases such as overconfidence, loss aversion, and herding behaviour substantially affect how individuals plan and execute their retirement savings and investments.

The statistical analysis indicated a significant association between the district of residence and the cognitive biases influencing savings behaviour. This suggests that local socio-economic conditions and cultural factors play a crucial role in shaping financial behaviours. The correlation analysis further highlighted the negative impact of cognitive biases on financial decision-making. The strong negative correlation between cognitive biases and familiarity with behavioural finance principles highlight the necessity for improved financial literacy programs.

The study also found that savings behaviour is positively correlated with cognitive biases, indicating that biases may lead to irrational saving patterns. Moreover, the negative correlations between cognitive biases and socio-economic conditions suggest that adverse economic factors exacerbate the effects of these biases, leading to poorer financial outcomes.

The regression analysis and corresponding ANOVA results demonstrate that the model effectively explains the variance in how frequently individuals notice price changes in everyday goods and services. The high R² value of 0.934 indicates that the predictors collectively account for approximately 93.4% of the variation in the dependent variable. This strong explanatory power is further supported by the significant F-statistic of 87.835, confirming that the model's predictors, including spending priorities, income range, employment status, and perceptions of inflation, significantly impact the frequency of noticing price changes.

In conclusion, the study highlights the critical need for integrating behavioural finance principles into financial education and advisory services. Tailored financial education programs that address specific cognitive biases can significantly improve financial decision-making and retirement planning. Policymakers, financial educators, and advisors must recognize the profound impact of cognitive biases and work towards developing strategies to mitigate their effects. By doing so, individuals can be better equipped to make rational and informed financial decisions, ultimately leading to more secure and favourable retirement outcomes. The findings from this study offer valuable insights that can guide future interventions aimed at enhancing financial literacy and reducing the adverse effects of cognitive biases in financial planning.

1. Zaheeruddin, m. (2018). inflationary trends and its impact on consumption of essential commodities in hyderabad city(doctoral dissertation, rayalaseema university).
2. Zaheeruddin, m., & ahmed, m. o. (2018). inflationary trends of selected essential commodities with special reference to city of hyderabad. clear international journal of research in commerce & management, 9(5).
3. Yadav, S., & Shankar, R. (2016). Inflation expectations and consumer spending in India: evidence from the Consumer Confidence survey. Reserve Bank of India Occasional Papers, 35(1), 3886.
4. Pervar, P. (1993). Effects of landuse policies on land prices in middle income housing, Hyderabad, India. University of London, University College London (United Kingdom).
5. Pothula, J. D. (1992). Residential location of lowincome households in Hyderabad, India. University of London, University College London (United Kingdom).
6. Tiwari, P., & Hingorani, P. (2014). An institutional analysis of housing and basic infrastructure services for all: the case of urban India. International Development Planning Review, 36(2), 227256.
7. Echternacht, L. (2014). Pricing urban water: evaluation of economics in the water sector of Hyderabad and Varanasi (India). Springer Science & Business Media.
8. Bhatia, R. (1988). Energy pricing and household energy consumption in India. The Energy Journal, 9(1_suppl), 71105