This graph illustrates the average budget for a new watch of middle class mainland Chinese in 2016, by gender. This year, the average budget for a business watch of middle class Chinese women in mainland China was around ***** yuan.

As of January 2022, the largest share of Chinese middle-class families had an annual income of between *** thousand and *** thousand yuan per year. According to the same survey, almost ** percent of respondents have at least one child. Many middle-class families in China face significant financial burdens because not only do living costs continuously increase but they also often have to support their parents. In that case, one family has to care for four elders and least one kid.

Korea Average: AH: Middle School: Receiving Money From A Lease data was reported at 14,050.000 KRW th in 2017. This records an increase from the previous number of 13,040.000 KRW th for 2016. Korea Average: AH: Middle School: Receiving Money From A Lease data is updated yearly, averaging 12,895.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 14,710.000 KRW th in 2014 and a record low of 9,310.000 KRW th in 2010. Korea Average: AH: Middle School: Receiving Money From A Lease data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H080: SHFLC: Household Assets, Liabilities & Income By Educational Attainments of Household Head.

The global savings software market is experiencing robust growth, driven by increasing smartphone penetration, rising financial literacy, and a growing need for personalized financial management tools. The market, estimated at $5 billion in 2025, is projected to expand at a compound annual growth rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This growth is fueled by several key trends: the increasing adoption of mobile-first financial solutions, the integration of AI and machine learning for personalized savings recommendations and budgeting assistance, and the rise of subscription-based models offering premium features like investment advice and financial planning tools. Furthermore, the market is segmented by application (personal vs. family savings) and operating system (Android vs. iOS), with the Android segment currently holding a larger market share due to its wider global user base. However, the iOS segment is experiencing faster growth due to its strong presence in high-income countries with higher average spending on financial applications. Restraints on market growth include concerns about data security and privacy, the prevalence of free or basic alternatives, and the digital literacy gap in certain regions. The competitive landscape is highly fragmented, with numerous players offering diverse solutions catering to varying user needs. Established players like Quicken and YNAB compete alongside newer entrants focusing on specific niche functionalities, such as goal-based savings (Piggy Goals, Qapital) or automated savings (Acorns, Plum). Geographic expansion remains a key strategy for many companies, with North America and Europe currently dominating the market. However, rapid growth is anticipated in the Asia-Pacific region, driven by the burgeoning middle class and increased smartphone adoption in countries like India and China. The continued focus on user experience, innovative features, and robust security measures will be crucial for players to thrive in this dynamic and competitive market.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

Personal Finance & Budgeting Software Market Outlook

The global personal finance and budgeting software market size was valued at approximately $1.2 billion in 2023 and is projected to reach around $2.4 billion by 2032, registering a robust CAGR of 8% during the forecast period from 2024 to 2032. The market's growth is driven by increasing consumer awareness about financial management, technological advancements in software solutions, and the rising adoption of digital platforms for personal finance management. This trend is further accelerated by the growing demand for personalized financial planning and the convenience offered by mobile and web-based applications.

One of the primary growth factors for the personal finance and budgeting software market is the increasing awareness among individuals about the importance of financial literacy and management. As consumers become more conscious of their spending habits and financial planning, the demand for reliable and user-friendly budgeting tools has surged. Moreover, the rise in disposable income and the complexity of financial instruments available to the average consumer have necessitated a more structured approach to personal finance, which these software solutions readily provide. The trend towards automating personal financial planning is further fueled by the millennials and Gen Z population, who prefer digital solutions for managing their financial tasks.

Technological advancements have significantly contributed to the growth of this market. Innovations such as artificial intelligence and machine learning are increasingly being integrated into personal finance software, enhancing their capabilities in areas like predictive analytics and personalized financial advice. These technologies enable the software to provide more accurate and tailored financial insights, helping users make informed decisions. Additionally, the seamless integration of these software solutions with other financial tools and platforms, such as bank accounts and investment portfolios, has made them indispensable for many consumers seeking a comprehensive view of their financial health.

The proliferation of mobile devices and the internet has also played a crucial role in the expansion of the personal finance and budgeting software market. With the widespread use of smartphones, mobile-based finance applications have become a preferred choice for consumers due to their accessibility and ease of use. This shift towards mobile solutions is particularly evident in developing regions where mobile penetration is high. Furthermore, digital transformation initiatives by financial institutions and fintech companies are driving the adoption of personal finance software, as they seek to offer enhanced digital services to their customers, creating a favorable market environment.

In the realm of financial management, Cash Flow Forecasting Software has emerged as a crucial tool for both individuals and businesses. This software enables users to predict future financial positions by analyzing current cash inflows and outflows. By providing insights into potential cash shortages or surpluses, it allows for proactive financial planning and decision-making. As the complexity of financial transactions increases, especially for small businesses and enterprises, having reliable cash flow forecasting capabilities becomes indispensable. This software not only aids in maintaining liquidity but also supports strategic planning by highlighting areas for cost optimization and investment opportunities. With the integration of advanced technologies, cash flow forecasting solutions are becoming more sophisticated, offering real-time data analysis and predictive insights that enhance financial stability and growth.

From a regional perspective, North America holds the largest market share due to the early adoption of technology and the presence of major software providers. The region's focus on financial literacy and education is driving the demand for personal finance and budgeting tools. Meanwhile, the Asia-Pacific region is expected to witness the highest growth rate, driven by rapidly increasing digitalization and a burgeoning middle-class population. Countries such as China and India are seeing a surge in the adoption of budgeting software as internet connectivity improves and more consumers seek to manage their finances digitally.

Product Type Analysis

The personal finance and budgeting software market is segmente

Korea Average: AH: Middle School: Financial Assets data was reported at 51,750.000 KRW th in 2017. This records a decrease from the previous number of 54,100.000 KRW th for 2016. Korea Average: AH: Middle School: Financial Assets data is updated yearly, averaging 50,865.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 56,160.000 KRW th in 2015 and a record low of 34,110.000 KRW th in 2010. Korea Average: AH: Middle School: Financial Assets data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H080: SHFLC: Household Assets, Liabilities & Income By Educational Attainments of Household Head.

Korea Average: AH: Middle School: Other Than Non-Financial Assets data was reported at 12,880.000 KRW th in 2017. This records a decrease from the previous number of 14,050.000 KRW th for 2016. Korea Average: AH: Middle School: Other Than Non-Financial Assets data is updated yearly, averaging 13,340.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 14,560.000 KRW th in 2014 and a record low of 4,400.000 KRW th in 2010. Korea Average: AH: Middle School: Other Than Non-Financial Assets data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H080: SHFLC: Household Assets, Liabilities & Income By Educational Attainments of Household Head.

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing.

Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics.

The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details.

Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year.

Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP.

In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending.

The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

Korea Average: AH: Middle School: Non-Financial Assets data was reported at 214,750.000 KRW th in 2017. This records an increase from the previous number of 207,900.000 KRW th for 2016. Korea Average: AH: Middle School: Non-Financial Assets data is updated yearly, averaging 201,645.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 214,750.000 KRW th in 2017 and a record low of 184,070.000 KRW th in 2010. Korea Average: AH: Middle School: Non-Financial Assets data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H080: SHFLC: Household Assets, Liabilities & Income By Educational Attainments of Household Head.

Model-based estimates of the proportion of households with mean weekly income lower than 60% of the national median weekly income, by middle layer super output area, England and Wales.

This table contains 58320 series, with data for years 1999 - 2016 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (20 items: Canada; Atlantic; Newfoundland and Labrador; Prince Edward Island; ...); Assets and debts (27 items: Total assets; Private pension assets; Registered Retirement Savings Plans (RRSPs), Registered Retirement Income Funds (RRIFs), Locked-in Retirement Accounts (LIRAs) and other; Employer-sponsored Registered Pension Plans (EPPs); ...); Net worth quintiles (6 items: Total, all net worth quintiles; Lowest net worth quintile; Second net worth quintile; Middle net worth quintile; ...); Statistics (6 items: Total values; Percentage of total assets or total debts; Number holding asset or debt; Percentage holding asset or debt; ...); Confidence intervals (3 items: Estimate; Lower bound of a 95% confidence interval; Upper bound of a 95% confidence interval).

Market Size for Bahrain Auto Finance Industry on the Basis of Credit Disbursed in USD Billion, 2018-2024 In 2023, the average loan tenure for auto finance in Bahrain was approximately5 years, with interest rates ranging between3% and 6%, depending on the type of loan and consumer profile. These figures reflect the growing accessibility of auto finance solutions in the country, enabling consumers to afford vehicles with manageable monthly payments. The Bahrain auto finance market reached a valuation ofBHD 1.2 Billionin 2023, driven by the increasing demand for vehicles, an expanding middle class, and a growing trend toward vehicle ownership. The market is characterized by major financial institutions and auto finance providers such as Bank of Bahrain and Kuwait (BBK), Al Salam Bank, and Bahrain Islamic Bank, which offer a range of vehicle financing options.

In the financial year 2021, the average annual saving of rich households in India was over 606 thousand Indian rupees, a stark contrast to destitute category which saved only five thousand Indian rupees. The middle-class saved almost 130 thousand Indian rupees annually. During the year, a rich household spent almost 25 times that of a destitute household, eight times that of an aspirer household, and almost three times that of a middle-class household.

In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between ******* and ******* Indian rupees a year. On the other hand, about ***** percent of households that same year, accounted for the rich, earning over * million rupees annually. The middle class more than doubled that year compared to ** percent in financial year 2005. Middle-class income group and the COVID-19 pandemic During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant. Economic inequality While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last ***** decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

Graph and download economic data for Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01134) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

The project adopted a broad approach, employing quantitative as well as qualitative methods. It covered both public and private forms of risk protection, and it analysed attitudes as well as actual behavior. First, we reviewed Britain's current 'mixed economy of welfare' in the aforementioned five key areas. We mapped the social programmes, occupational schemes and private options that have been available since the early 1990s. The second phase was based on quantitative data analysis, making use of the Family Resources Survey (FRS) and the ABI Risk and Protection Survey. We analysed the take-up of insurances and how it was influenced by attitudes and socio-demographic characteristics. Third, we conducted 61 qualitative interviews, where we explored personal risk management strategies of middle-income households from Scotland and England. The main result was a typology of risk management rationales that guide household economies. This stage also explored the ramifications of the recent financial uncertainties and economic downturn. Comparing England and Scotland, the purpose was to review Britain's current 'mixed economy of welfare' in key areas: unemployment, sickness, costs of higher education for children, retirement and infirmity in old age. The aim was to map the types of statutory protection against such risks and contingencies and examine changes in the scope of public provision. In parallel, we will examine the scope of non-statutory (occupational and personal) provision, investigating how 'private welfare markets' have developed since the early 1990s. The second phase is based on quantitative data analysis of household savings and investment behaviour in insurances and private market-based contracts for risk protection. Finally, via qualitative interviews, we explore personal risk management of socially and economically similar families from Scotland and England. This stage will also explore the potential ramifications of the most recent financial uncertainties and economic downturn. The project investigated risk management strategies of above average income households in England and Scotland. In the UK especially those with above average incomes are often assumed to have access to or seek private forms of risk protection, partly based on company provision or private voluntary protection complementing or substituting public social protection. The project investigated how households protect themselves against income loss due to unemployment, sickness or retirement and plan for expenses like long term care and higher education costs. We focused our analysis on how households balance these risks between public, occupational and private forms of protection. Moreover, we explored how the recent financial crisis has influenced the attitudes and behavior of households regarding their personal protection. The project sought to answer how and why some middle class households plan for contingencies and engage in private risk management strategies while others do not.

Survey of Household Spending (SHS), average household spending, Canada, regions and provinces.

97-107 Annual Comprehensive Income Tax Settlement and Declaration Manual Filing Summary Form for Five Regional Tax Offices

In the financial year 2021, the average annual expenditure of rich households in India was over * million Indian rupees, a stark contrast to destitute category which spent ** thousand Indian rupees. A rich household spent almost ** times that of a destiture household, * times that of an aspirer household, and almost * times that of a middle-class household.