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The dataset used for analysis is the "Household Expenditure Survey Australia Summary Results" for the year 2015-16. This dataset provides a complete perspective on the distribution of financial resources by Australian households across different expenditure categories. The dataset, that can be accessed on the official website of the Australian Bureau of Statistics, has been selected due to its significant importance in providing valuable insights into consumer behavior, economic well-being, and policy implications.

The dataset offers significant value for scholars, politicians, and enterprises looking to gain insights into the expenditure patterns of Australian families (Johnston et al., 2019). It comprises a variety of expenditure categories, such as housing costs, food, healthcare, transportation, and other related expenses. Through the study of this dataset, individuals can acquire a more profound comprehension of how various elements, such as income levels, demographics, and geographic location, exert effect on the distribution of funds inside each respective category.

Despite its abundance, it is imperative to recognize specific constraints linked to this collection. In the beginning, the age of the data, pertaining to the fiscal year 2015-16, raises concerns regarding its ability to accurately represent the present economic environment, consumer inclinations, and policy modifications. It is essential for researchers to show caution when deriving conclusions or making decisions completely based on this dataset.

Selected Dataset

The Australian Household Expenditure Survey Summary Results for 2015–16 was used as the dataset for this study. It can be viewed at the following URL: "Statistics/Economy/Finance/Household-Expenditure-Survey-Australia-Summary-Results/2015-16". This dataset was selected by using a number of different criteria. A thorough investigation of the spending habits and trends of Australian households is possible with this dataset. Through the analysis of the distribution of resources across several categories, researchers can obtain significant understanding of the financial health of the populace. Government policy can be influenced by this data, which can also help businesses offer more individualized goods and services to customers and get insight into the population's health and well-being. In addition, a broad range of spending categories are addressed by the information, such as housing, food, transportation, healthcare, and more. The presence of many components enables researchers to investigate how different attributes, such as income, demographics, and geography, affect the spending within each category.( Oostenbach et al., 2020).

The dataset used in this study corresponds to the time period of 2015-16, hence which could restrict its ability to accurately capture present spending trends. Over the course of time, there can be significant changes in economic situations, alterations in consumer behavior, and shifts in government regulations.

Selected Analysis/Visualisation Tool

Power BI provides a variety of visualization options for including bar charts, line charts, pie charts, dotterchart, and various other visual representations. The visuals have the capability to be altered and adjusted according to individual requirements for data analysis.
Interaction facilitates the use of interactive dashboards, permitting users to delve further into data, create filters, and dynamically explore insights.

Visualizations (Diagrams)

Alcoholic Beverages and Date of Household expenditure survey Australia

The continuous examination of alcoholic beverage utilization was followed by comparable consumption figures presented in millions of liters. The data shows a consistent upward trend in the consumption of alcoholic beverages over the years, reaching its highest point in 2015-16(b) at a volume of 31.95 million liters. The total consumption throughout the period spanning from 2009–10 to 2015–16 is recorded as 153.51 million liters. In Power BI, a clustered bar chart can be used as a suitable method of visualization to properly represent the given data. The "Row Labels," indicating different time periods (years) such as 1984, 1988–89, 1993–94, etc., would be placed on the x-axis. The y-axis would represent the "aggregate volume of alcoholic beverages" expressed in millions of liters. The consumption for each year can be graphically represented by a unique bar, distinguished by its bar. The utilization of a clustered bar chart allows a simple comparison of consumption patterns across various years, enabling the observation of trends or abnormalities over time.

Total of alcoholic beverages and date

Figure 1: Total of alcoholic beverages and date

Clothing and Footwear of Household expenditure survey Australia

Clothing and footwear were consumed for a long time, with millions of units being consumed overall (Selvanathan et al., 2021). According to the data indicates a general trend of rising consumption, which culminated in 2015–2016 at 43.75 million units. The total amount of units consumed between 2009–10 and 2015–16 is 241.8 million.

This dataset can be effectively displayed in Power BI using a stacked bar chart. Plotting the "sum of clothing and footwear" values in millions of units should be done along the y-axis, and the "row labels" indicating different historical periods should be placed along the x-axis. The bars can be divided into segments, each of which represents a different year, demonstrating annual consumption. These bars can be stacked on top of one another to visually represent the annual accumulative consumption. With the help of the supplied stacked bar chart, consumption patterns over a variety of years can be observed, and the relative contributions of each year to the total quantity consumed may be recognized.

Total of clothing and footwear by date

Figure 2: Total of clothing and footwear by date

Communication of Household Expenditure Survey Australia

The data shows the consumption patterns of clothing and footwear over a span of several years, with the consumption numbers expressed in millions of units (Tran et al., 2018).The data shows a general upward trajectory in consumption, reaching its highest point in the year 2015-2016 with a total of 43.75 million units. The total consumption for the duration spanning from 2009–10 to 2015–16 amounts to 241.8 million units.

The "Row Labels" indicating various historical periods should be placed along the x-axis, while the "Sum of Clothing and Footwear" values, measured in millions of units, should be plotted along the y-axis. The representation of annual consumption can be represented by segmented bars, wherein the bars can be vertically stacked to visually illustrate the cumulative consumption for each successive year.

Communication by date

Figure 3: Communication by Date

Domestic Fuel and Power Household Expenditure Survey Australia

The data given above relates to the expenditures spent on domestic fuel and power over a span of multiple years, with the amounts expressed in millions of units (Ellsworth-Krebs, 2019).The data shows an ongoing increase in expenditure on domestic fuel and power, with a significant spike observed in the fiscal year 2015-2016, achieving a value of 40.92 million units. The total spending for the duration spanning from 2009–10 to 2015–16 is 180.9 million units, demonstrating a sustained increase trend in the allocation of resources towards fulfilling domestic energy requirements over this period.

A line chart can be used as an effective means of visualizing the provided data. The expenditure pattern across the years can be easily followed by viewers by the placement of years on the x-axis and the corresponding expenditures on the y-axis. The line chart shows an upward trend, highlighting the progressive growth in expenditure on domestic fuel and power from the year 1984 to 2015-16(b). The below visualization facilitates a transparent and intuitive comprehension of expenditure patterns, making it a helpful instrument for the analysis and presentation of data.

Total Domestic fuel and power by date

Figure 4 : Total domestic fuel and power by date

Food And Non-Alcoholic Bverages

The dataset provided exhibits the average yearly household expenditure on "Food and non-alcoholic beverages" in Australia over a period of several years, ranging from 1984 to 2015-16 (Oostenbach et al., 2020). A recognizable pattern becomes evident. The data demonstrates a continuing and steady increase in household expenditure on food and non-alcoholic beverages during the observed time period.
Commencing from the year 1984, the average yearly spending within this particular category was at $71.22, and had a subsequent rise in the years 1988-89, reaching $95.83. The structure persisted with subsequent significant rises to $111 in the fiscal year 1993-1994, $127.01 in the fiscal year 1998-1999, and $152.87 in the fiscal year 2003-2004.

Sum of food and non  alcoholic beverages

Figure: Sum of food and non- alcoholic beverages

Findings and Limitations


The analysis and show of data by using Power BI revealed several insights into the expenditure patterns exhibited by households in Australia throughout the fiscal year of 2015-2016. Furthermore, the categories of "Housing costs" and "Transport" appeared as the most prominent areas of expenditure, underscoring their substantial impact on household budgets. Food and non-alcoholic beverages were an important component of overall expenses.( Nelson et al., 2019).

Sustained Expansion: Throughout the years, there was a persistent upward trend in expenditures across diverse categories, with significant increases observed in the "Communication" and "Domestic fuel and power" sectors.

Variation among Categories: While several categories exhibited a steady pattern of growth, others displayed oscillations during the analyzed time period.


Time Limitations of the information are used in this study pertains to the time period of 2015-16. Therefore, it is essential to recognize that the findings might not precisely reflect contemporary expenditure patterns or economic circumstances in Australia. The inclusion of more recent data would yield a more contemporary comprehension of customer behavior.
Lack of Demographic Information of the dataset exhibits a lack of demographic information related to the households surveyed, including but not limited to income levels, family size, and geographic region. This constraint restricts the capacity to conduct comprehensive demographic studies and develop particular findings.


Johnston, B. M., Burke, S., Barry, S., Normand, C., Ní Fhallúin, M., & Thomas, S. (2019, October). Private health expenditure in Ireland: Assessing the affordability of private financing of health care. Health Policy, 123(10), 963–969. Oostenbach, L. H., Lamb, K. E., Dangerfield, F., Poelman, M. P., Kremers, S., & Thornton, L. (2020, August 24). The role of dwelling type on food expenditure: a cross-sectional analysis of the 2015–2016 Australian Household Expenditure Survey. Public Health Nutrition, 1–12. He, H., Reynolds, C. J., Hadjikakou, M., Holyoak, N., & Boland, J. (2020, June). Quantification of indirect waste generation and treatment arising from Australian household consumption: A waste input-output analysis. Journal of Cleaner Production, 258, 120935. Froemelt, A., & Wiedmann, T. (2020, October 1). A two-stage clustering approach to investigate lifestyle carbon footprints in two Australian cities. Environmental Research Letters, 15(10), 104096. Selvanathan, S., Selvanathan, E., & Jayasinghe, M. (2021, January). A new approach to analyze conditional demand: An application to Australian energy consumption. Energy Economics, 93, 105037.
Tran, C., Kortt, M., & Dollery, B. (2018, July 20). Population size or population density? An empirical examination of scale economies in South Australian local government, 2015/16. Local Government Studies, 45(5), 632–653.
Ellsworth-Krebs, K. (2019, December 16). Implications of declining household sizes and expectations of home comfort for domestic energy demand. Nature Energy, 5(1), 20–25.
Yusuf, F., & Leeder, S. (2020). Recent estimates of the out-of-pocket expenditure on health care in Australia. Australian Health Review, 44(3), 340.
Oostenbach, L. H., Lamb, K. E., Dangerfield, F., Poelman, M. P., Kremers, S., & Thornton, L. (2020, August 24). The role of dwelling type on food expenditure: a cross-sectional analysis of the 2015–2016 Australian Household Expenditure Survey. Public Health Nutrition, 1–12. Nelson, T., McCracken-Hewson, E., Sundstrom, G., & Hawthorne, M. (2019, January). The drivers of energy-related financial hardship in Australia – understanding the role of income, consumption, and housing. Energy Policy, 124, 262–271.

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