Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Aug 2025 about savings, personal, rate, and USA.

In June 2025, the personal savings rate in the United States amounted to 4.5 percent. That was a slightly lower figure than a year earlier. The personal savings rate is calculated as the ratio of personal savings to disposable personal income. Within the topic of personal savings in the U.S., there are different goals and reasons for saving. What are personal savings? Saving refers to strategies of accumulating capital for future use by either not spending a part of one’s income or cutting down on certain costs. Saved money may be preserved as cash, put on a deposit account, or invested in various financial instruments. Investing usually incorporates some level of risk which means that part of the invested money can be gone. An example of a relatively safe investment would be saving bonds, such as the debt securities issued by the U.S. Department of the Treasury. Saving trends in the U.S. and abroad Looking at the personal saving rate in the United States throughout the past decades, it can be observed that savings had been decreasing until the mid-2000s, and they increased after the 2008 financial crisis. Still, the largest savings rates were reached in 2020 and 2021. The reason for that increase in the savings rate that year might be related to the measures to contain the COVID-19 pandemic. The value of personal savings in the United Kingdom has also followed a similar trend. Although events like the COVID-19 pandemic may have affect many countries in a similar way, the ability to save, as well as the average savings as a share of personal income across countries can vary significantly depending on multiple factors affecting each territory.

Household Saving Rate in Australia decreased to 4.20 percent in the second quarter of 2025 from 5.20 percent in the first quarter of 2025. This dataset provides - Australia Households Savings - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Malaysia GDP: USD: Gross Domestic Savings data was reported at 102.179 USD bn in 2017. This records an increase from the previous number of 96.564 USD bn for 2016. Malaysia GDP: USD: Gross Domestic Savings data is updated yearly, averaging 13.429 USD bn from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 115.794 USD bn in 2014 and a record low of 22.936 USD mn in 1961. Malaysia GDP: USD: Gross Domestic Savings data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank: Gross Domestic Product: Nominal. Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). Data are in current U.S. dollars.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;

This table presents household income and saving from the national accounts broken down by income quintile. It includes primary income, disposable income, adjusted disposable income and saving. Primary income is the income that households receive as a result of their involvement in production. It includes gross operating surplus (in the case of households, imputed income from owning your own house), gross mixed income (profits of the self-employed) and compensation of employees (wages and salaries and employers’ social contributions) as well as net interest, distributed income of corporations (dividends) and rent. Disposable income is the income that households receive after taxes on income and wealth, social contributions and benefits, non-life insurance premiums and claims, and other current transfers like remittances. Adjusted disposable income is derived from disposable income, but also includes the value of social transfers in kind received by households. Saving represents that part of disposable income (adjusted for the change in pension entitlements) that is not spent on final consumption goods and services.

   In this table, households are grouped on the basis of their composition, taking into account the presence, number and age of the members of the household. Eight categories are shown: a) single person (adult) less than 65 years old, b) single adult aged 65 and older, c) single adult with children living at home, d) a couple (two adults) where both are less than 65 years old without children living at home, e) two adults where at least one is aged 65 or older without children living at home, f) two adults with fewer than 3 children living at home, g) two adults with at least 3 children living at home, and h) others. In this classification, an adult is defined as anyone who is 18 years old or older.<br><br>

   Results are presented in national currency and as averages per household and per consumption unit (you can choose these from the ‘Unit of measure’ filter). Results per consumption unit (equivalised income and saving) are obtained by dividing each household’s result by the number of consumption units, reflecting its consumption needs, for example by applying the standard OECD-modified equivalence scale, counting the first adult as 1, any additional people aged 14 and over as 0.5 and all children under 14 as 0.3.<br><br>

   The default view of this table is for a single country (‘Reference area’ filter) and single year (‘Time period’ filter). In cases where countries appear to be greyed-out, data may be available for earlier years, and these can be selected by selecting a different start and end year in the ‘Time period’ filter. Users are recommended to select one country at a time to obtain a comprehensive overview of the distributional results for that country for a given period of time. Alternatively, you may select a specific item from the ‘Transaction’ filter to make cross-country comparisons. <br><br>

   For more information on the (compilation of) these results, please see the <a href="https://www.oecd.org/sdd/na/household-distributional-results-in-line-with-national-accounts-experimental-statistics.htm"> webpage on household distributional results </a>.

This dataset is completely based on personal expenses, incomes & savings throughout the year done by my family members. The finance analysis of 2022 year shows how much I had earned, saved and spent on several categories. Here you can see the following attributes like description : It shows the area of finance category: which are the area where money is going and coming sub category: It is the sub part of category category type : Like income, savings & expenses debit amount: includes expenses credit amount: includes incomes and savings Also where I had invested and should I have continue or not, what all things I have to control and what are the savings I can do from my income , this I can analyze from this report very easily. what are my way of spending and how much I'm focusing on my savings after having incomes from several sources. Which sub category shows more expenses and what I can limit that analysis I can do from this and many more to do.

Graph and download economic data for Gross Private Saving (GPSAVE) from Q1 1947 to Q2 2025 about savings, gross, private, GDP, and USA.

Graph and download economic data for Households; Net Worth, Level (BOGZ1FL192090005Q) from Q4 1987 to Q2 2025 about net worth, Net, households, and USA.

This survey shows the outcome of the survey question: "What is the value of your saving deposits and saving accounts?"*. During the survey period in the second half of 2016, roughly 15 percent of the respondents indicate they have savings worth between 10,000 and 25,000 euros.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Malaysia Savings Rate data was reported at 0.963 % pa in 2017. This records a decrease from the previous number of 1.001 % pa for 2016. Malaysia Savings Rate data is updated yearly, averaging 3.483 % pa from Dec 1966 (Median) to 2017, with 52 observations. The data reached an all-time high of 6.667 % pa in 1985 and a record low of 0.940 % pa in 2009. Malaysia Savings Rate data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Malaysia – Table MY.IMF.IFS: Lending, Saving and Deposit Rates: Annual.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income. D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 54 percent of the Estimated Annual kWh Savings and 70 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf. This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Malaysia Adjusted Savings: Net National Savings: % of GNI data was reported at 11.751 % in 2016. This records a decrease from the previous number of 11.946 % for 2015. Malaysia Adjusted Savings: Net National Savings: % of GNI data is updated yearly, averaging 16.310 % from Dec 1974 (Median) to 2016, with 43 observations. The data reached an all-time high of 23.608 % in 2008 and a record low of 5.479 % in 1986. Malaysia Adjusted Savings: Net National Savings: % of GNI data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank: Gross Domestic Product: Nominal. Net national savings are equal to gross national savings less the value of consumption of fixed capital.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted Average;

Survey of Household Spending (SHS), average household spending by household type.

A detailed table for establishing a monthly family budget, including income, fixed and variable expenses, as well as savings, for better financial management.

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.

Malaysia Adjusted Savings: Mineral Depletion data was reported at 197.601 USD mn in 2016. This records a decrease from the previous number of 478.642 USD mn for 2015. Malaysia Adjusted Savings: Mineral Depletion data is updated yearly, averaging 41.827 USD mn from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 478.642 USD mn in 2015 and a record low of 4.086 USD mn in 2005. Malaysia Adjusted Savings: Mineral Depletion data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank: Gross Domestic Product: Nominal. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime. It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; ;

A structured data that describes how much of my 2021 annual salary was spent each month on; - Accomodating/Housing/Rent (how much I pay as rent every month) - Health/Fitness (how much I spent on gym subscriptions and dietary products and medicines) - Personal expenses (other social and personal expenses) - Utilities/Energy (how much I spent on gas and electricity and water and internet) - Automobiles/Transport (how much I spent on repairs of my car and fuel) - Food/Dining (cost of grocery and dinners I had with friends and family) I made sure I collected receipts and stamps for each item that was purchased and each bill that was paid and everything was totaled at the end of the month. For items that I could not get receipts or stamps, the expense was written in my handbook and captured during the data collection stage.

I categorised all the expenses under these 6 headings and created an excel table for its analysis and visualization purposes. I hope to find out if there's a way I can reduce my expenditure and increase savings and also understand how much of my incomes goes into what particular expenditure.

Name: Characterization of investments profiles on the energy transition for european citizens

Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description.

License: cc-BY-SA

Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.

Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.

Collection Date: 22/07/2022

Publication Date: 15/10/2023

DOI: 10.5281/zenodo.4455198

Other repositories:

Author: University of Deusto

Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.

Description: The dataset contains a ODS spreadsheet file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset

Section 1 - Scenarios for energy transition.

ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible.

ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario

ID191, ID192.. Amount of money people would spend based on an assumed case.

ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.

[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.

[ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.

[ID141, ID5, ID147]. Used for statements that best represent survey responder

Section 2 - Determinants (factors). Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible.

ID100 Risk profile

ID101 Added value

ID102 Self-Satisfaction

ID103 Technical Fit

ID104 Own competence

ID105 Knowledge

ID106 cost-Efficiency

ID107 Safety

ID108 Trust

ID109 Autarky

ID110 Legal

ID111 climate protection

ID112 Wellbeing

ID113 Coziness

ID114 Rights and Duties

ID115 Peer-Pressure

ID116 Socialising

ID117 Support

ID118 Agreement

ID119 Brag

ID120 Fun

ID121 Novelty

ID122 Trends

ID123 Authority

ID124 Own Significance

ID125 Poseur

ID2 Frugality

ID3 Environmental concerns

ID31 Adherence

ID52 Commitment

ID97 Profits

ID99 Credit Score

Section 3 - “Socio-economic” description. Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked

ID164 Understanding of questions

ID300 Country of residence

ID137 Age

ID178 Highest level of education

ID136 Willingness to provide data on the investment decision (respond apply for -Investment decision section)

Section 4 - Investment decision. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle)

Appliances

ID42 Affordable cost of a Regular refrigerator

ID45 Energy efficient refrigerator costs

ID50 Willingness to purchase an energy efficient refrigerator

ID65 Why no

ID66 affordable cost of an energy efficient option

ID67 Years to amortize an efficient option

Insulation

ID47 Affordable cost of updating to a state of the art insulation on the facade

ID56 Willingness for paying/invest

ID74 Why no?

ID20 affordable cost of an energy efficient option

ID34 Years to amortize an energy efficient option

Energy Generation

ID68 Affordable cost of a solar photovoltaic system

ID76 Willingness for paying/invest

ID84 Why no?

ID132 Affordable cost of a photovoltaic system

ID138 Years that amortize a photovoltaic system

Energy Storage

ID142 Affordable cost of an energy storage system

ID146 Willingness for paying/invest

ID181 Why no?

ID182 Affordable cost of an energy storage system

ID183 Years that amortize an energy storage systems

Heating

ID140 Affordable cost of a gas boiler

ID209 Affordable cost of an energy efficient heating system

ID217 Willingness for paying/invest

ID238 Why no?

ID239 Affordable cost of a energy efficient option

ID241 Years that amortize a heat pumps

Mobility

ID41 Average kilometers traveled a typical day

ID51 Usual travel option

ID264 Affordable cost of a diesel or gasoline mid-range brand new car

ID265 Affordable cost of a mid-range brand new electric car

ID281 Willingness to buy an electric car

ID289 Why no?

ID290 Affordable price of an electric car

ID291 Years that amortize an electric car

Section 5 - Household characterization

ID127 Selecting an asked value

ID189 Type of living area

ID202 Gender identity

ID1 Those living in the house

ID32 Number of inhabitants

ID220 Average neat yearly income

ID229 Average monthly saving

ID240 Type of housing

ID249 Owner / co-owner

ID255 Usable area of the property (m²)

ID263 Insulation level

ID270 Climate zone

ID86 Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”

ID87 Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”

ID88 Level of self-awareness about energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”

ID89 Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”

ID190 feedback about survey

5 star: ⭐⭐⭐

Preprocessing steps: anonymization, data fusion, imputation of gaps.

Reuse: NA

Update policy: No more updates are planned

Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.

Technical aspects:

Other:

Malaysia GDP: Gross Domestic Savings data was reported at 439,410.000 MYR mn in 2017. This records an increase from the previous number of 400,578.000 MYR mn for 2016. Malaysia GDP: Gross Domestic Savings data is updated yearly, averaging 35,796.998 MYR mn from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 439,410.000 MYR mn in 2017 and a record low of 70.212 MYR mn in 1961. Malaysia GDP: Gross Domestic Savings data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank: Gross Domestic Product: Nominal. Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). Data are in current local currency.; ; World Bank national accounts data, and OECD National Accounts data files.; ;