Italy

Composite Indicator of Systemic Stress (CISS)

Below Parameters are common for all combinations : Frequency - Quarterly Measure -Amounts Outstanding / Stocks CBS Bank Type - Domestic Banks CBS Reporting Basis - Immediate Counterparty Basis Balance Sheet Position - Total Claims Type of Instruments - All Instruments Remaining Maturity - All Maturities Currency Type of Booking Location - All Currencies Counterparty Sector - All Sectors Data cited at : https://www.bis.org/statistics/index.htm

This dataset presents the Consolidated financial transactions by economic sector (Quarterly table 0610), according to SNA 2008 methodology. It comprises all flows, which record, by type of financial instruments, the financial transactions between institutional sectors.

Citation: Damodaran, Aswath, Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2016 Edition (March 5, 2016). Available at SSRN: https://ssrn.com/abstract=2742186 or http://dx.doi.org/10.2139/ssrn.2742186   This dataset summarizes the latest bond ratings and appropriate default spreads for different countries. While you can use these numbers as rough estimates of country risk premiums, you may want to modify the premia to reflect the additional risk of equity markets. To estimate the long term country equity risk premium, I start with a default spread, which I obtain in one of two ways: (1) I use the local currency sovereign rating (from Moody's: www.moodys.com) and estimate the default spread for that rating (based upon traded country bonds) over a default free government bond rate. For countries without a Moody's rating but with an S&P rating, I use the Moody's equivalent of the S&P rating. To get the default spreads by sovereign rating, I use the CDS spreads and compute the average CDS spread by rating. Using that number as a basis, I extrapolate for those ratings for which I have no CDS spreads. (2) I start with the CDS spread for the country, if one is available and subtract out the US CDS spread, since my mature market premium is derived from the US market. That difference becomes the country spread. For the few countries that have CDS spreads that are lower than the US, I will get a negative number. You can add just this default spread to the mature market premium to arrive at the total equity risk premium. I add an additional step. In the short term especially, the equity country risk premium is likely to be greater than the country's default spread. You can estimate an adjusted country risk premium by multiplying the default spread by the relative equity market volatility for that market (Std dev in country equity market/Std dev in country bond). I have used the emerging market average of 1.12 (estimated by comparing a emerging market equity index to an emerging market government/public bond index) to estimate country risk premium.I have added this to my estimated risk premium of 5.08% for mature markets (obtained by looking at the implied premium for the S&P 500) to get the total risk premium. Notes:  The year of publication has been considered as per publication date. For example, data published on 2018-Jan considered as 2018, similarly 2019-Jan as 2019    

This variable consists of the sum of all items of the assets side or the sum of all items of the liabilities side. This indicator gives an idea of the economic importance of credit institutions.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

All income received by credit institutions from assets such as loans and advances, treasury bills, fixed income securities. It also includes fees and commissions similar in nature to interest and calculated on a time basis or by reference to the amount of the claim or liability.

A count of the number of all credit institutions active during at least a part of the reference period. Credit institutions are undertakings whose business it is to receive deposits or other repayable funds from the public and to grant credit for their own account.

The number of persons employed is the total number of persons who work in the observation unit as well as persons who work outside the unit who belong to it and are paid by it (e.g. sales representatives, delivery personnel, repair and maintenance teams). Yet the number of persons employed excludes manpower supplied to the unit by other companies.

Derived data: The derived data database contains data calculated by the ECB related to CPI, HICP, real GDP, population, unit labour costs, labour productivity, industrial production, balance of payments, government finance statistics, monetary aggregates and money gap, long-term and short-term interest rates, effective exchange rates, oil prices, deflators, earnings, negotiated wages and job vacancy rates.   ECB: Derived Data (DD), Monthly Update

MFI - List of MFIs

ECB: Risk Assessment Indicators (RAI), Monthly Update

A yield curve, also known as term structure of interest rates, represents the relationship between market remuneration (interest) rates and the remaining time to maturity of debt securities. The information content of a yield curve reflects the asset pricing process on financial markets. When buying and selling bonds, investors include their expectations of  future inflation, real interest rates and their assessment of risks. An investor calculates the price of a bond by discounting the expected future cash flows (coupon payments and/or redemption). ECB estimates zero-coupon yield curves for the euro area and also derives forward and par yield curves. A zero coupon bond is a bond that pays no cupon and is sold at a discount from its face value. The zero coupon curve represents the yield to maturity of hypothetical zero coupon bonds, since they are not directly observable in the market for a wide range of maturities. They must therfore be estimatedfrom existing zero coupon bonds and fixed coupon bond prices or yields.  The forward curve shows the short-term (instantaneous) interest rate for future periods implied in the yield curve. The par yield reflects hypothetical yields, namely the interest rates the bonds would have yielded had they been priced at par (i.e. at 100).

 A yield curve, also known as term structure of interest rates, represents the relationship between market remuneration (interest) rates and the remaining time to maturity of debt securities. The information content of a yield curve reflects the asset pricing process on financial markets. When buying and selling bonds, investors include their expectations of  future inflation, real interest rates and their assessment of risks. An investor calculates the price of a bond by discounting the expected future cash flows (coupon payments and/or redemption). The European Central Bank estimates zero-coupon yield curves for the euro area and also derives forward and par yield curves. A zero coupon bond is a bond that pays no coupon and is sold at a discount from its face value. The zero coupon curve represents the yield to maturity of hypothetical zero coupon bonds, since they are not directly observable in the market for a wide range of maturities. They must therfore be estimated from existing zero coupon bonds and fixed coupon bond prices or yields.  The forward curve shows the short-term (instantaneous) interest rate for future periods implied in the yield curve. The par yield reflects hypothetical yields, namely the interest rates the bonds would have yielded had they been priced at par (i.e. at 100). An outlier removal mechanism is applied to bonds that have passed the selection criteria described in 11.1. Bonds are removed if their yields deviate by more than twice the standard deviation from the average yield in the same maturity bracket. Afterwards, the same procedure is repeated. 

European Central Bank: Bank Lending Survey (BLS)

The financial indicators are based on data compiled according to the 2008 SNA "System of National Accounts, 2008". Many indicators are expressed as a percentage of Gross Domestic Product (GDP) or as a percentage of Gross Disposable Income (GDI) when referring to the Households and NPISHs sector. The definition of GDP and GDI are the following: Gross Domestic Product: Gross Domestic Product (GDP) is derived from the concept of value added. Gross value added is the difference of output and intermediate consumption. GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output [System of National Accounts, 2008, par. 2.138]. GDP is also equal to the sum of final uses of goods and services (all uses except intermediate consumption) measured at purchasers’ prices, less the value of imports of goods and services [System of National Accounts, 2008, par. 2.139]. GDP is also equal to the sum of primary incomes distributed by producer units [System of National Accounts, 2008, par. 2.140]. Gross Disposable Income: Gross Disposable Income (GDI) is equal to net disposable income which is the balancing item of the secondary distribution income account plus the consumption of fixed capital. The use of the Gross Disposable Income (GDI), rather than net disposable income, is preferable for analytical purposes because there are uncertainty and comparability problems with the calculation of consumption of fixed capital. GDI measures the income available to the total economy for final consumption and gross saving [System of National Accounts, 2008, par. 2.145]. Definition of Debt: Debt is a commonly used concept, defined as a specific subset of liabilities identified according to the types of financial instruments included or excluded. Generally, debt is defined as all liabilities that require payment or payments of interest or principal by the debtor to the creditor at a date or dates in the future. Consequently, all debt instruments are liabilities, but some liabilities such as shares, equity and financial derivatives are not debt [System of National Accounts, 2008, par. 22.104]. According to the SNA, most debt instruments are valued at market prices. However, some countries do not apply this valuation, in particular for securities other than shares, except financial derivatives (AF33). In this dataset, for financial indicators referring to debt, the concept of debt is the one adopted by the SNA 2008 as well as by the International Monetary Fund in “Public Sector Debt Statistics – Guide for compilers and users” (Pre-publication draft, May 2011). Debt is thus obtained as the sum of the following liability categories, whenever available / applicable in the financial balance sheet of the institutional sector:special drawing rights (AF12), currency and deposits (AF2), debt securities (AF3), loans (AF4), insurance, pension, and standardised guarantees (AF6), and other accounts payable (AF8). This definition differs from the definition of debt applied under the Maastricht Treaty for European countries. First, gross debt according to the Maastricht definition excludes not only financial derivatives and employee stock options (AF7) and equity and investment fund shares (AF5) but also insurance pensions and standardised guarantees (AF6) and other accounts payable (AF8). Second, debt according to Maastricht definition is valued at nominal prices and not at market prices. To view other related indicator datasets, please refer to: Institutional Investors Indicators [add link] Household Dashboard [add link]

The Financial Soundness Indicators (FSIs) were developed by the IMF, together with the international community, with aim of supporting analysis and assessing strengths and vulnerabilities of financial systems. The Statistics Department of the IMF, disseminates data and metadata on selected FSIs provided by participating countries. For a description of the various FSIs, as well as the consolidation basis, consolidation adjustments, and accounting rules followed, please refer to the concepts and definitions document in the document tab. Reporting countries compile FSI data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata to conduct more meaning cross-country comparisons or to assess the evolution of a given FSI for any of the countries.

Data cited at: Financial Soundness Indicators (FSI), Reporting Entities, The International Monetary Fund. The Reporting entities dataset provides information on the structure, size, and coverage of the financial institutions that are used for compiling financial soundness indicators. It provides a better understanding of the structure of the reporting entities in terms of the type of institution, number of entities, size of assets, and type of control. Reporting entities are domestically incorporated entities but are divided into two: domestically controlled and foreign controlled. The concepts of residency criterion and control are determined based on FSI Guide methodology which is in line with international best practices such as Systems of National Accounts. Data on reporting entities cover the branches,

Global Financial Stability Report, October 2023: Financial and Climate Policies for a High-Interest-Rate Era

This table provides a detailed breakdown of the financial assets and liabilities of households and non-profit institutions serving households (NPISH) by financial instrument. On the asset side of the balance sheet, it shows data on households’ and NPISHs’ holdings of investment fund shares, life insurance and annuity entitlements, and pension entitlements; and on the liability side, it shows data on their short- and long-term borrowing (loans).

Institutional Investors' Assets and Liabilities data are reported by Central Banks, National Statistical Institutes or Supervisory Authorities. The indicators reported here are compiled on the basis of those statistics.   The first set of indicators measure total financial assets (liabilities) held by each institutional investor as a percentage of GDP. Total financial assets (liabilities) is defined as the sum of the following asset (liability) categories: currency and deposits (F2), debt securities (F3), loans (F4), equity and investment fund shares (F5), insurance pension and standardized guarantee schemes (F6), financial derivatives and employee stock options (F7), and other accounts receivable (payable) (F8). The second set of indicators shows the share of each asset (liability) category in the total financial assets (liabilities) of each investor. They help to analyse the investment portfolio composition of the investor as well as financial risks borne by the investor. The third set of indicators shows the sub-sector composition of total financial assets (liabilities) by investor category, by showing the share of each sub-sector in the total financial assets (liabilities) of each investor category.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Global marketing has become more and more important over the years with the increasing trend of internationalization. Faced with too many choices, marketers have the challenge of determining which international markets to enter and the appropriate marketing strategies for those countries. The purpose of this study is to rank, with a US focus, the market potential of the largest economies and to provide guidance to the US companies that plan to expand their markets internationally. While the US is not included in the rankings, the insights provided by the index are still applicable to companies located in other international markets. This indexing study is conducted by the Michigan State University — International Business Center to help companies compare prospect markets on several dimensions. Eight dimensions are chosen to represent the market potential of a country on a scale of 1 to 100. The dimensions are measured using various indicators and are weighted in determining their contribution to the overall Market Potential Index(MPI). Between years 1996 and 2014, the MPI has been calculated for 26 countries identified as "Emerging Markets" by The Economist magazine. However, in order to cover a wider range of markets, a decision has been made in 2014 to increase the number of countries according to the criteria explained below.

The Monetary and Financial Statistics (MFS) database contains the aggregated surveys covering: i) Central Bank ii) Depository Corporations and iii) Other Financial Corporations. The key macroeconomic aggregates in this dataset include: i) Monetary base and broad money; ii) Credit aggregates (including credit to the private sector); and iii) Foreign assets and liabilities.   Beginning in 2009, there are two presentations of Monetary Statistics in IFS. The new presentation data follows the Monetary and Financial Statistics Manual (MFSM) and the Monetary and Financial Statistics Compilation Guide (MFSCG), a companion to the MFSM that contains more detailed coverage of the classification, economic sectorization, valuation, and recording of financial assets and liabilities in an economy. The MFSCG gives prominence to the source data for monetary and financial statistics.   The majority of countries use the standardized report forms (SRFs) to report monetary data to the IMF and are presented under SRF Countries.   The old presentation is used for those countries that do not use the SRFs for reporting Monetary data and presented under Non-SRF Countries. The presentation of these countries will be changed to the new presentation when the countries implement the reporting of SRF-based data.   The Monetary and Financial Statistics Manual and Compilation Guide (Manual) updates and merges into one volume methodological and practical aspects of the compilation process for monetary and financial statistics (MFS). Aimed at compilers and users of MFS, it offers a conceptual framework for the collection, compilation, and analytical presentation of monetary data, which provide a critical input for monetary policy formulation and monitoring.   Detailed monetary statistics based on the standardized report forms reflecting the conceptual framework of the above Manual and its predecessors.

The Annual Sector Accounts (ASA) are compiled in accordance with the European System of Accounts - ESA 1995 (Council Regulation 2223/96). The transmission of the ASA data by the Member States follows the Regulation (EC) N° 1392/2007 of the European Parliament and of the Council (new ESA95 transmission programme). The ASA encompass the non-financial accounts and provide a description of the different stages of the economic process: production, generation of income, distribution of income, redistribution of income, use of income and non-financial accumulation. The ASA record the economic flows of institutional sectors in order to illustrate their economic behaviour and show relations between them. They also provide a list of balancing items that have high analytical value in their own right: value added, operating surplus and mixed income, balance of primary incomes, disposable income, saving, net lending / net borrowing. All of them but net lending / net borrowing, can be expressed in gross or net terms, i.e. with and without consumption of fixed capital that accounts for the use and obsolescence of fixed assets. In terms of institutional sectors, a broad distinction is made between the domestic economy (ESA 1995 classification code S.1) and the rest of the world (S.2). Within S.1 and S.2, in turn, more detailed subsectors are distinguished in "3.2 Classification system". Data are presented in the table "Non-financial transactions" (nasa_nf_tr). The table contains data, as far as they are available, expressed in national currency and millions of euro in current prices. The availability of back data varies according to country. A subset of national key indicators is available in the table "Key indicators" for most of the members of the European Economic Area (EEA), for the euro area and for the EU28. See also the sector accounts dedicated website for more information.

The Quarterly Sector Accounts (QSA) are compiled in accordance with the European System of Accounts - ESA 1995 (Council Regulation 2223/96). The transmission of the QSA data by the Member States follows the Regulation (EC) N° 1161/2005 of the European Parliament and of the Council (QSA regulation). The QSA encompass the non-financial accounts and provide a description of the different stages of the economic process: production, generation of income, distribution of income, redistribution of income, use of income and non-financial accumulation. The compilation of QSA is the outcome of a close collaboration by Eurostat and the ECB, in cooperation with the national statistical institutes and national central banks. The ECB and Eurostat are publishing integrated non-financial and financial accounts, including financial balance sheets, for the euro area (the euro area accounts). Eurostat is also publishing the non-financial accounts for the EU. The QSA record the economic flows of institutional sectors in order to illustrate their economic behaviour and show relations between them. They also provide a list of balancing items that have high analytical value in their own right: value added, operating surplus and mixed income, balance of primary incomes, disposable income, saving, net lending / net borrowing. All of them but net lending / net borrowing, can be expressed in gross or net terms, i.e. with and without consumption of fixed capital that accounts for the use and obsolescence of fixed assets. In terms of institutional sectors, a broad distinction is made between the domestic economy (ESA 1995 classification code S.1) and the rest of the world (S.2). Within S.1, in turn, the following institutional sectors are distinguished: - Non-financial Corporations (S.11) - Financial Corporations (S.12) - General Government (S.13) - Households and Non-profit Institutions Serving Households (S.14 + S.15). The full set of quarterly sector accounts is published for euro area / EU28 aggregates only. However, a subset of quarterly national key indicators is available at dedicated section (see "Quarterly data") as well as in the database (see table "Key indicators") for most of the 17 members of the European Economic Area (EEA) whose GDP is above 1% of the EU28 total. The other EEA members do not have to transmit the quarterly accounts of corporations and households to Eurostat. Non-financial accounts are presented in the table "Non-financial transactions" (nasq_nf_tr). The other three tables (only the Euro area) include financial accounts, other flows and balance sheets. QSA data are provided at current prices only. The key indicators (and their components) of households and non-financial corporations as published in the QSA news release are seasonally adjusted. Data are presented in millions of national currency, euro and as percentages.

Other Economic Flows: Flows that are not transactions according to ESA 95 definitions

The financial flows and stocks data are reported quarterly to the European Central Bank. Once validated the data are transmitted to Eurostat.  Financial flows consist of transactions and other flows and represent  the difference between opening balance sheet  at the beginning of the year and closing balance sheet at the end of the year.  Â

Financial flows and stocks data are often referred to collectively in the national accounts framework as 'financial accounts'. Financial flows consist of transactions and other flows, and represent the difference between the opening financial balance sheet at the start of the year and the closing balance sheet at the end of the year. The data are compiled in accordance with the European System of Accounts (ESA 2010), which came into force in September 2014, and are presented here in the following tables: 'Financial transactions', 'Other changes in volume', 'Revaluation account', and 'Financial balance sheets'.

According to the research, North America showed the most noticeable annualised growth rate over the last five years, growing by 6.7% during the period. Europe and Asia-Pacific regions showed annualised growth rates of 3.1 % and 2.8% respectively. The U.S. continues to hold its position as the country with the largest share of pension assets across the top 300 funds, representing 38.6% spread across 134 funds. Meanwhile, Canada has overtaken the U.K. as the fifth largest country by share of pension fund assets, accounting for 5.4% (5.3% in 2015). The U.K. now accounts for 4.8%, falling from 5.4% of total assets in 2015. A total of 28 new funds have entered the ranking over the last five years, with the U.S. contributing the most new funds (13) on a net basis. Germany and Mexico experienced the highest net losses over the period, losing a net four funds each. The U.S. has the largest number of funds within the top 300 ranking (134), followed by the U.K. (26), Canada (18), Japan and Australia (both 16). Defined benefit (DB) assets increased by 5.6% in 2016, compared to 9.6% for defined contribution (DC) plans, 3.9% for reserve funds and an increase of 2.9% for hybrid funds. DB assets account for 65.5% of the disclosed total AUM, down from 65.9% in 2015, whilst DC assets have increased their share, rising from 21.5% in 2015 to 22.2%. Reserve funds remain relatively unchanged at 11.5% (11.7% in 2015), as do hybrid funds (0.8%, falling from 0.9% in 2015)

This dataset covers only Cross-Country-Concepts - Portfolio Investment related indicators. Please visit  Principal Global Indicators - Data by Indicator  for other set of Principal Global Indicators. 

The Financial Accounts show net acquisition of financial assets (or changes in assets) during the period, net incurrence of financial liabilities (or changes in liabilities) during the period, and net financial transactions (or changes in the net position: financial assets minus financial liabilities) during the period. This table shows the Financial Accounts on a non-consolidated basis, meaning that it shows all flows in the economy, both between units belonging to different (sub)sectors and between units belonging to the same (sub)sector, in contrast with consolidated accounts in which flows between units within the same (sub)sector would be removed. In this table, the presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. The default view of the table is for the economy as a whole, but you can use the ‘Institutional sector’ filter to select specific sectors such as Non-financial Corporations, Financial Corporations, General Government and Households, as well as the Rest of the world account. For each sector, the table presents breakdowns by financial instrument, for example currency and deposits, debt securities, loans, equity and investment fund shares, insurance and pensions. Users can also choose to compare a single financial instrument, such as listed shares, for several countries. Users should note that Australia does not produce non-consolidated accounts. These indicators were presented in the previous dissemination system in the QASA_TABLE620R dataset. Explore also the OECD Financial Accounts and Balance Sheets webpage: Financial Accounts and Balance Sheets webpage OECD statistics contact: [email protected]

The Financial Balance Sheets show financial assets, liabilities and net financial worth (assets minus liabilities) at the end of the period. This table is on a consolidated basis, which means that counterpart assets and liabilities of units within the same sector or subsector (or the economy as a whole) have been removed. In this table, the presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. The default view of the table is for the economy as a whole, but you can use the ‘Institutional sector’ filter to select specific sectors such as Non-financial Corporations, Financial Corporations, General Government and Households, as well as the Rest of the world account. For each sector, the table presents breakdowns by financial instrument, for example currency and deposits, debt securities, loans, equity and investment fund shares, insurance and pensions. Users can also choose to compare a single financial instrument, such as listed shares, for several countries. Users should note that some countries do not produce consolidated accounts for all sectors. These indicators were presented in the previous dissemination system in the QASA_TABLE710R dataset. Explore also the OECD Financial Accounts and Balance Sheets webpage: Financial Accounts and Balance Sheets webpage OECD statistics contact: [email protected]

The Financial Balance Sheets show financial assets, liabilities and net financial worth (assets minus liabilities) at the end of the period. This table is on a non-consolidated basis, meaning that it shows all assets and liabilities of units in a sector or subsector (or the economy as a whole), in contrast with consolidated balance sheets in which counterpart assets and liabilities of units within the same sector or subsector (or the economy as a whole) would be removed. In this table, the presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. The default view of the table is for the economy as a whole, but you can use the ‘Institutional sector’ filter to select specific sectors such as Non-financial Corporations, Financial Corporations, General Government and Households, as well as the Rest of the world account. For each sector, the table presents breakdowns by financial instrument, for example currency and deposits, debt securities, loans, equity and investment fund shares, insurance and pensions. Users can also choose to compare a single financial instrument, such as listed shares, for several countries. Users should note that Australia does not produce non-consolidated accounts. These indicators were presented in the previous dissemination system in the QASA_TABLE720R dataset. Explore also the OECD Financial Accounts and Balance Sheets webpage: Financial Accounts and Balance Sheets webpage OECD statistics contact: [email protected]

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Remittance Prices Worldwide Publication: https://datacatalog.worldbank.org/dataset/remittance-prices-worldwide License: http://creativecommons.org/licenses/by/4.0/   Provides data on the cost of sending and receiving relatively small amounts of money from one country to another. Data cover 365 "country corridors" worldwide, from 48 remittance sending countries to 105 receiving countries.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

The dataset on Statistical discrepancy (Institutional Investors – Financial Balance Sheets) represents the time series of the dataset on Institutional investors' assets and liabilities (7II) along with those of the dataset on Financial Balance Sheets (720), for the financial instruments and institutional sectors which are in common to these two datasets.  Additionally, for each of the above-mentioned time series, a statistical discrepancy is reported in order to show any possible differences which may exist between the two datasets (7II and 720).  In fact, the dataset on Institutional investors' assets and liabilities (7II) constitutes an attempt to better integrate these data in the framework of the System of National Accounts 2008 (SNA 2008).  However, discrepancies may exist and may, for example, be caused by balancing practices (e.g. when sector and counterpart sector data are reconciled) in the compilation of Financial Balance Sheets at a higher level of aggregation, which may not have been carried through at a lower level of aggregation. Moreover, differences may also be caused by the use of different source data.

The listed Companies have been ranked by "Global assets under management" and "by the country of the main headquarters and/or main European domicile". Assets managed by these groups of total €65.7trn (2017: €63.3trn). * 2017 value are referred as of 31/12/16 (€m) * 2018 value are referred as of 31/12/17 (€m)

While much of the comparative evidence on inequalities that is currently available refers to household income, wealth is a critical dimension of households’ economic well-being. How wealth is distributed is important for equity and inter-generational mobility, but also for the stability of the economic system and for its resilience to shocks. While the lack of comparative evidence in this field reflects the absence of an agreed standard that statistical offices could use when collecting this information, this gap has been addressed by the OECD with the release in 2013 of a set of statistical guidelines in this field. In 2013, the OECD issued a set of ‘Guidelines’ for micro statistics on household wealth (OECD, 2013) and an increasing number of countries have engaged in the collection of micro statistics in this field (European Central Bank, 2013). Building on these initiatives as well as others, such as the Luxembourg Wealth Study (Sierminska et al, 2006) which have informed previous OECD analysis (Jantii et al., 2008), the OECD has updated the data on the distribution of household wealth for OECD countries, based on the set of conventions and classifications proposed in the 2013 OECD Guidelines.

The primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates

The World Economic Situation and Prospects 2024 is a report produced by the United Nations Department of Economic and Social Affairs (UN DESA), in partnership with the United Nations Conference on Trade and Development (UNCTAD) and the five United Nations regional commissions: Economic Commission for Africa (UNECA), Economic Commission for Europe (UNECE), Economic Commission for Latin America and the Caribbean (UNECLAC), Economic and Social Commission for Asia and the Pacific (UNESCAP) and Economic and Social Commission for Western Asia (UNESCWA). The United Nations World Tourism Organization (UNWTO) also contributed to the report.