In 2023/24 the state pension in the United Kingdom grew by **** percent, based on the CPI inflation rate the previous September. The triple lock system determines how much the state pension grows in the UK based on which of three different measures are highest; the inflation rate, earnings growth, or *** percent.

According to Cognitive Market Research, the global Pension Fund market size is USD 75484984.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.80% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 30193993.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.0% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 22645495.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 17361546.44 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.8% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 3774249.23 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 1509699.69 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.5% from 2024 to 2031.
The Distributed Contribution held the highest Pension Fund market revenue share in 2024.

Market Dynamics of Pension Fund Market

Key Drivers for Pension Fund Market

Demographic Shifts to Increase the Demand Globally

Globally, aging populations are intensifying the need for stable retirement profits. As existence expectancy rises and birth fees decline, the percentage of aged individuals is developing. This demographic shift puts pressure on existing pension systems, riding an urgent call for improved contributions to pension price range. Governments and people are spotting the need to bolster retirement savings to ensure financial balance in later years. Consequently, both public and private sectors are improving pension schemes and encouraging higher savings rates. The trend displays a proactive method to deal with the economically demanding situations posed by an aging society, aiming to provide adequate help and safety for the elderly population.

Regulatory Changes to Propel Market Growth

Governments globally are enacting rules to beautify the pension industry's transparency, accountability, and threat management—these regulatory modifications intend to create an extra stable and secure funding environment. By mandating clear disclosure of pension fund operations and performance, regulators ensure that stakeholders are better knowledgeable. Enhanced duty measures require pension fund managers to adhere to stringent fiduciary requirements, safeguarding beneficiaries' interests. Improved danger management frameworks assist in mitigating economic threats, promoting long-term stability. These reforms are designed to construct public acceptance as true with self-assurance inside the pension machine, ultimately making sure that retirement price ranges are managed prudently and sustainably for destiny.

Restraint Factor for the Pension Fund Market

High Initial Investment Cost to Limit the Sales

Low hobby charges pose sizable demanding situations for pension finances aiming to attain their favored funding returns. When hobby costs are low, the yields on traditionally safe investments, including authorities bonds, decrease. This state of affairs forces pension funds to seek opportunity investments that provide higher returns; however frequently come with extended danger. The trouble in generating enough returns can affect the lengthy-time period sustainability of the pension budget, leading to higher contribution necessities from employers and employees or decreased advantages for retirees. To navigate these low-interest surroundings, pension price ranges must undertake extra different and innovative investment strategies, balancing the want for better returns with prudent risk management to make a certain economic balance for destiny retirees.

Fluctuating financial environment and economic uncertainty hamper the growth of market 

One of the most important restraints on the pension fund market is the fluctuating financial environment and economic uncertainty. Pension funds use long-term investments to provide returns, so they can fulfill their future payment obligations to retirees. But global economic uncertainty based on factors like volatile stock markets, inflation rates, changes in interest rate...

As of January 1, 2025, retirees in Russia received a gross pension of approximately ******** Russian rubles on average, or ****U.S. dollars per month at the exchange rate as of May 16, 2025. The reform of 2019 introduced a retirement age hike to gradually increase the retirement age to 60 years for women and 65 years for men until 2028. Pensions in Russia are guaranteed by the state, like in many European countries. Pension growth in Russia The amount of retirement benefits in Russia increased by roughly ******* Russian rubles, or *** percent, over the course of 2024. The pensions increased more significantly than prices in the country, as Russia's annual inflation rate stood at around *** percent in the same year. Pensioners in Russia Despite the increase in pension amounts, there has been a decrease in the number of individuals entitled to receive pensions until the start of 2024. As of January 1, 2025, the number of pensioners in Russia reached roughly **** million, more than a year prior. That corresponded to nearly *** pensioners per 1,000 population.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Pension Real Estate Market Outlook

The global pension real estate market size was valued at approximately USD 1.2 trillion in 2023 and is projected to reach nearly USD 2.3 trillion by 2032, registering a compound annual growth rate (CAGR) of 7%. The significant growth factor for this market is the increasing need for stable, long-term investment returns within retirement portfolios, driven by the aging global population and the rising demand for diversified investment avenues.

One of the primary growth drivers for the pension real estate market is the steady shift towards alternative investments among pension funds. Traditional asset classes like stocks and bonds have exhibited higher volatility and lower returns in recent years, prompting fund managers to seek more stable and lucrative investment opportunities. Real estate, with its potential for steady income generation and capital appreciation, has emerged as a compelling option. The tangible nature of real estate assets also provides a level of security and risk mitigation that is attractive to both public and private pension funds.

Another key factor is the increasing urbanization and infrastructural development worldwide. As cities expand and economies grow, the demand for residential, commercial, and industrial properties rises correspondingly. Pension funds, with their long-term investment horizon, are well-positioned to capitalize on these trends. Moreover, the strategic allocation of capital into real estate helps in inflation hedging, as property values and rental incomes typically increase with inflation. This aspect is particularly relevant in the current economic climate, where inflationary pressures are a growing concern.

Technological advancements and the digital transformation of real estate management practices are also contributing to market growth. The adoption of PropTech solutions, such as blockchain for property transactions, AI-driven analytics for market forecasting, and IoT for building management, is enhancing the efficiency and transparency of real estate investments. These innovations are making real estate a more accessible and manageable asset class for pension funds, encouraging greater participation and investment.

In the realm of real estate investments, the adoption of an Investment Management Solution for Real Estate is becoming increasingly vital. This solution provides a comprehensive framework for managing diverse real estate portfolios, optimizing asset performance, and enhancing decision-making processes. By integrating advanced analytics and data-driven insights, these solutions enable pension funds to effectively navigate market complexities and capitalize on emerging opportunities. The ability to streamline operations, manage risks, and ensure compliance with regulatory standards makes investment management solutions indispensable tools for real estate investors aiming to achieve sustainable growth and long-term value creation.

Regionally, North America is expected to dominate the pension real estate market, driven by a robust economic landscape and well-established real estate sector. Europe follows closely, benefiting from strong institutional frameworks and favorable regulatory environments. The Asia-Pacific region is witnessing rapid growth, fueled by rising urbanization, economic expansion, and an increasing middle-class population. Latin America and the Middle East & Africa are also emerging as potential markets, albeit at a slower pace, due to economic and political variability.

Property Type Analysis

The pension real estate market can be segmented based on property type into residential, commercial, industrial, and others. Residential properties continue to be a major focus for pension real estate investments. The steady demand for housing, compounded by the global population growth and urban migration trends, makes residential real estate a lucrative and stable investment. Pension funds are increasingly investing in multifamily units, senior housing, and affordable housing projects to meet the diverse needs of the population.

Commercial properties, including office spaces, retail centers, and hospitality assets, also represent a significant portion of pension real estate investments. The commercial real estate sec

The Pension Funding industry in France has navigated complex economic conditions in recent years, ranging from regulatory changes to erratic capital markets. Although a notable point of contention among workers, the increase in the retirement age from 62 to 64 in 2023 is set to support pension funds by increasing the duration of time that contributions are made. This will support asset values and improve the funding position of pension schemes, driving investment activity. Revenue is expected to grow at a compound annual rate of 2.3% over the five years through 2025 to €26.2 billion, including estimated growth of 3.9% in 2025. Capital markets have been particularly volatile in recent years following the COVID-19 outbreak, characterised by rising interest rates, spiralling inflation and subdued economic growth. In 2022, the diversification benefits typically seen between bonds and equity broke down, with both asset classes falling amid the rising base rate environment and bleak economic outlook, hitting investment income. Only at the end of 2023 did markets begin to recover, as investors bet on rate cuts from central banks across the globe, inciting an inflow of capital into stocks and bonds and ratcheting up asset valuations. In 2024, rate cut expectations continued to prop up bond values, although not to the same extent as in 2023, allowing pension funds to realise capital gains. Despite cuts, interest rates will remain higher than the low levels after the financial crisis, supporting investment income from periodic coupon payments. However, investors remain cautious as geopolitical tensions show potential to escalate and central banks continue to navigate a soft landing in the face of sticky inflation. Revenue is expected to grow at a compound annual rate of 6.6% over the five years through 2030 to €36.1 billion. Looking ahead, French pension funds will welcome the higher interest rate environment, benefitting from the lower present value of liabilities and healthier coupon payments from their bond investments. The shift towards sustainable investments will continue to gather momentum as pension funds look to capitalise on the long-term growth of sustainable companies and draw in funds from ESG-conscious investors. However, this will also bring with it greater regulatory scrutiny as the French government cracks down on greenwashing. ESG-focused funds also limit the potential investment universe, weighing on investment returns if the hype around ESG doesn’t materialise.

The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:

a brownfield site,

a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment or (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; and an unemployment rate at or above the national average for the previous year, or

a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.

These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:

Brownfields – Source: EPA. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).

Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census County Business Patterns. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).

--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.

--Relevant columns include:

  a) SUM_nhgis0: Total employment in 2020.


  b) SUM_nhgis1: Total unemployment in 2020.


  c) P_Unemp: Percent unemployment in 2020.


  d) Q_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.


  e) FF_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.


  f) final_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.

Retired Power Plants – Source: EIA via HFLID. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

Abandoned Coal Mines – Source: MSHA. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

5) US State Borders– Source: IPUMS NHGIS.

Also included here are polygon shapefiles for Onshore Wind and Solar Candidate Project Areas from Princeton REPEAT. These files have been updated to include columns related to the energy communities.

New columns include:

CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.

CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.

FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.

UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.

MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.

P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.

India IESH: RBI: Inflation Expectations: Retired Persons: One Year Ahead: Standard Deviation data was reported at 5.000 % in Jun 2018. This records a decrease from the previous number of 5.400 % for May 2018. India IESH: RBI: Inflation Expectations: Retired Persons: One Year Ahead: Standard Deviation data is updated monthly, averaging 4.320 % from Sep 2008 (Median) to Jun 2018, with 44 observations. The data reached an all-time high of 5.860 % in Sep 2009 and a record low of 2.800 % in Mar 2009. India IESH: RBI: Inflation Expectations: Retired Persons: One Year Ahead: Standard Deviation data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC003: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Occupation.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available...Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013-2017 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .Accuracy of the Data..). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates

The Canadian pension fund market, valued at $4.11 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.05% from 2025 to 2033. This expansion is driven by several key factors. Firstly, Canada's aging population necessitates increased retirement savings, fueling demand for robust pension fund management. Secondly, a growing awareness of long-term financial security and the need for diversified investment strategies among Canadians contributes to the market's expansion. Government regulations promoting pension plan participation and the increasing adoption of sophisticated investment techniques by fund managers further bolster this growth. The market is segmented by plan type, encompassing Distributed Contribution, Distributed Benefit, Reserved Fund, and Hybrid models, each catering to diverse investor needs and risk tolerances. Major players such as the Canada Pension Plan Investment Board (CPPIB), Caisse de dépôt et placement du Québec (CDPQ), and Ontario Teachers' Pension Plan dominate the landscape, leveraging their expertise in global asset allocation and alternative investments. While competitive pressures exist among these prominent institutions, the overall market shows a high degree of stability due to the long-term nature of pension fund investments. The market's future trajectory will likely be influenced by factors such as global economic conditions, fluctuating interest rates, and evolving regulatory frameworks. Increased adoption of sustainable and responsible investing practices is anticipated, reflecting growing societal and investor interest in Environmental, Social, and Governance (ESG) factors. Technological advancements in data analytics and portfolio management are also expected to reshape the competitive landscape, driving efficiency and enhancing investment performance. The continued focus on achieving long-term investment returns, while managing risks effectively, will remain paramount for pension fund managers operating within this dynamic market. The presence of substantial historical data from 2019-2024 allows for informed projections and a more accurate understanding of future growth trends. Recent developments include: In June 2023, Qualtrics, the pioneer and foremost provider of Experience Management (XM) software, announced that its acquisition by Silver Lake, a prominent in technology investment globally, in collaboration with Canada Pension Plan Investment Board (CPP Investments)., In May 2023, Franklin Resources, Inc., known as Franklin Templeton, a global investment management firm, unveiled a strategic partnership with Power Corporation of Canada and Great-West Lifeco, Inc.. Key drivers for this market are: Shift to Capital Light Products from traditional products., A push for technology led engagement. Potential restraints include: Shift to Capital Light Products from traditional products., A push for technology led engagement. Notable trends are: Increase in Inflation affecting Canada Pension funds Market.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2011-2015 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates

Market prices of pension funds in Mexico, inflation, rates of return

The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2010 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, and other durables. Explored in this survey were respondents' types of savings and financial investments, loan use, family income, and retirement planning. This survey also asked respondents about financial and health literacy; adult and online education; and technology use in health, finances, travel, and communication. Additional questions on independent living communities and general feelings were asked. Other topics in this series typically include ownership, lease, and use of automobiles, respondents' use of personal computers at home and in the office, and respondents' familiarity with and use of the Internet. Demographic information include ethnic origin, sex, age, marital status, and education.

India IESH: RBI: Inflation Expectations: Retired Persons: Current: Standard Deviation data was reported at 4.400 % in Jun 2018. This records a decrease from the previous number of 4.700 % for May 2018. India IESH: RBI: Inflation Expectations: Retired Persons: Current: Standard Deviation data is updated monthly, averaging 4.050 % from Sep 2008 (Median) to Jun 2018, with 44 observations. The data reached an all-time high of 6.140 % in Sep 2009 and a record low of 1.990 % in Mar 2009. India IESH: RBI: Inflation Expectations: Retired Persons: Current: Standard Deviation data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC003: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Occupation.

India IESH: RBI: Inflation Expectations: Retired Persons: Three Months Ahead: Mean data was reported at 10.200 % in Sep 2018. This records an increase from the previous number of 9.900 % for Jun 2018. India IESH: RBI: Inflation Expectations: Retired Persons: Three Months Ahead: Mean data is updated monthly, averaging 10.600 % from Sep 2008 (Median) to Sep 2018, with 45 observations. The data reached an all-time high of 13.600 % in Sep 2013 and a record low of 5.300 % in Mar 2009. India IESH: RBI: Inflation Expectations: Retired Persons: Three Months Ahead: Mean data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC003: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Occupation.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Pensionbee (PBEE) Faces Retirement: Can it Outperform?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Pensionbee (PBEE) Stock: A Retirement Revolution in the Making?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

India IESH: RBI: Inflation Expectations: Retired Persons: Three Months Ahead: Standard Deviation data was reported at 4.600 % in Jun 2018. This records a decrease from the previous number of 4.800 % for May 2018. India IESH: RBI: Inflation Expectations: Retired Persons: Three Months Ahead: Standard Deviation data is updated monthly, averaging 4.200 % from Sep 2008 (Median) to Jun 2018, with 44 observations. The data reached an all-time high of 6.010 % in Sep 2009 and a record low of 2.590 % in Mar 2009. India IESH: RBI: Inflation Expectations: Retired Persons: Three Months Ahead: Standard Deviation data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC003: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Occupation.

Inflation is an important measure of any country’s economy, and the Retail Price Index (RPI) is one of the most widely used indicators in the United Kingdom, with the rate expected to be 4.1 percent in 2025, compared with 3.6 percent in 2024. This followed 2022, when RPI inflation reached a rate of 11.6 percent, by far the highest annual rate during this provided time period. CPI vs RPI Although the Retail Price Index is a commonly utilized inflation indicator, the UK also uses a newer method of calculating inflation, the Consumer Price Index. The CPI, along with the CPIH (Consumer Price Index including owner occupiers' housing costs) are usually preferred by the UK government, but the RPI is still used in certain instances. Increases in rail fares for example, are calculated using the RPI, while increases in pension payments are calculated using CPI, when this is used as the uprating factor. The use of one inflation measure over the other can therefore have a significant impact on people’s lives in the UK. High inflation falls to more typical levels by 2024 Like the Retail Price Index, the Consumer Price Index inflation rate also reached a recent peak in October 2022. In that month, prices were rising by 11.1 percent and did not fall below double figures until April 2023. This fall was largely due to slower price increases in key sectors such as energy, which drove a significant amount of the 2022 wave of inflation. Inflation nevertheless remains elevated, fueled not only by high food inflation, but also by underlying core inflation. As of February 2025, the overall CPI inflation rate was 2.8 percent, although an uptick in inflation is expected later in the year, with a rate of 3.7 percent forecast for the third quarter of the year.