In a survey conducted in June 2022, 81.1 percent of respondents were aware of afterpay as a buy now, pay later (BNPL) digital payment service in Australia. As BNPL has increased in popularity in Australia, many smaller fintech companies, such as Humm and Openpay, have joined the market.

Buy now, pay later (BNPL) method was more likely to be used for e-commerce by younger consumers in 2024 than their older counterparts. This is according to a survey held in 14 different countries across North America, Europe, and Latin America. The share of users among Millennials respondents in the survey - ages 28 to 43 years old - were higher than the other age groups, with Gen Z and X being at the average level. Regarding gender, male respondents reported to use more this method more frequently than female respondents.

According to a 2024 survey on buy now pay later (BNPL) services, the share of BNPL users from Gen Z and Gen Y was the highest out of the four generations. In July 2024, 59 percent of Gen Z respondents and Gen Y respondents stated to have used BNPL in the past six months. The usage of BNPL plans among the Baby Boomer generation was well below the other generations, but peaked in April 2023, when 15 percent of respondents of that generation indicated to have used BNPL services.

In a survey conducted among financial advisors in Australia in 2023, around 62 percent of respondents agreed that cost of living pressures had led to more clients using buy now, pay later for essential expenses than in the past. Just two percent of respondents disagreed with this statement.

Unlock data-backed intelligence on Asia Pacific Buy Now Pay Later Market, size at USD 140 billion in 2023, showcasing industry demand trends and future growth opportunities.

Buy now, pay later (BNPL) methods were over twice as likely to be used for e-commerce in Austria than in other countries in Europe and North America. This is according to a survey held in seven different countries across the region. Austria and Germany stood out in the survey, as they were the only countries to be above the European and North American average for the use of this payment method in e-commerce.

The Buy Now Pay Later (BNPL) platform market has emerged as a transformative force in the retail and e-commerce sectors, providing consumers with flexible payment options that enhance their shopping experience. By allowing customers to make purchases immediately while deferring payment over time, BNPL solutions have

Mexico has several buy now, pay later services - with Kueski Pay and Aplazo being the main pure play providers behind several shopping apps from Latin America. This is according to a ranking of the biggest apps that can offer BNPL services, based on a minimum of 10,000 monthly active users (MAU) or more. Domestic brand Kueski Pay had approximately **** million monthly active users. Sweden's Klarna, however, attracted only one percent of its global MAU from within Mexico.

Monthly installs of mobile apps which enable U.S. consumers to purchase goods on payments plans doubled between April and September 2020. This conclusion was reached after an analysis of installation data that included Klarna, Affirm, Afterpay, and QuadPay. No figures were provided for the providers individually, but what is noticeable is their growth since lockdown began: U.S. installs for the buy now pay later - or BNPL - apps grew substantially in May, before declining in the summer, and then picking up again in August and September. The source adds the *** million installs in September equaled the all-time record that was set in December 2019. BNPL or buy now pay later is an alternative form of consumer lending, where consumers can split their purchase into multiple installments, and is a payments option is increasingly offered in online shopping.

In June 2023, people with an household income of under ****** U.S. dollars had the highest buy now, pay later (BNPL) usage rates. The relationship between the usage of BNPL and household income was inversely proportional. As household incomes increased, the share of respondents who claimed to have used BNPL decreased.

Australia's buy now, pay later (BNPL) user figure climbed by around ** percent year-on-year in the wake of the coronavirus pandemic in 2020. Now reaching almost *********** users, the country is regarded as one of the top countries in the world when it comes to the use of buy now, pay later or POS financing. This is largely due to local players AfterPay and and Zip, although PayPal did announce it would enter the world with its own Pay in * service in 2021.

The Vietnam payments industry is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) of 10.58% from 2025 to 2033. This expansion is fueled by several key drivers. The rising adoption of smartphones and internet penetration is creating a fertile ground for digital payment methods like mobile wallets (MoMo, ZaloPay, VNPAY) to flourish. Government initiatives promoting financial inclusion and digital transformation are further accelerating this shift away from cash transactions. The burgeoning e-commerce sector, particularly in retail, entertainment, and healthcare, necessitates efficient and seamless payment solutions, driving demand for both online and point-of-sale (POS) payment systems. While the exact 2025 market size is unavailable, considering the 10.58% CAGR and a reasonable estimation based on regional market trends and similar developing economies, the Vietnam payments market likely exceeds $5 billion in 2025. Key players like Vietcombank, VietinBank Group, and international entities like PayPal are vying for market share, fostering competition and innovation within the ecosystem. However, challenges remain, including the need for improved cybersecurity infrastructure to address potential fraud risks and the necessity of further educating users in less digitally-literate areas to ensure widespread adoption of digital payment systems. The continued expansion of digital payment infrastructure and government support will be critical to overcoming these restraints and sustaining the industry's impressive growth trajectory. Segmentation analysis reveals a strong preference toward digital wallet usage, reflecting the overall trend of digitalization within the Vietnamese economy. The competitive landscape is dynamic, with a mix of domestic banks (Vietcombank, VietinBank, Bank for Investment and Development of Vietnam), specialized payment processors (VNPAY, MoMo, ZaloPay), and international players (PayPal, Samsung Pay). This diverse range of providers offers consumers a variety of options, contributing to the market’s vitality. Future growth will be influenced by factors such as the development of innovative payment technologies (e.g., biometric authentication, blockchain-based solutions), the expansion of financial literacy programs, and the increasing integration of payment systems with other digital services (e.g., ride-hailing apps, e-commerce platforms). The segment breakdown across payment methods and end-user industries will offer insights into market share dynamics and areas ripe for further investment and innovation. Continued investment in infrastructure and security is crucial for sustained growth and consumer confidence. Comprehensive Coverage Vietnam Payments Industry Report (2019-2033) This in-depth report provides a comprehensive analysis of the dynamic Vietnam payments industry, projecting robust growth from 2025 to 2033. With a focus on key market drivers, challenges, and emerging trends, this report is an essential resource for businesses, investors, and policymakers seeking to understand and capitalize on opportunities within this rapidly evolving sector. The report utilizes data from the historical period (2019-2024), the base year (2025), and estimated year (2025) to forecast market performance until 2033. The study covers various payment methods, including digital wallets, card payments, and cash transactions, across diverse end-user industries. Recent developments include: June 2022 - Vietnam Posts and Telecommunications Group (VNPT) and the Joint Stock Commercial Bank for Foreign Trade of Vietnam (Vietcombank) have inked a collaboration agreement (VNPT). The development of digital payment services and platforms is covered under this bilateral cooperation agreement., November 2021 - Le Minh Khai, the Deputy Prime Minister, has signed a resolution authorizing a scheme for the growth of cashless payment in Vietnam from 2021 to 2025. The project's goals are to promote positive changes in cashless payment in a high-growth economy, make the practice a habit for inhabitants in metropolitan regions, progressively expand to rural, remote, and mountainous locations, as well as eliminate cash-related expenditures.. Key drivers for this market are: High Proliferation of E-commerce, including the rise of m-commerce and cross-border e-commerce supported by the increase in purchasing power, Enablement Programs by Key Retailers and Government encouraging digitization of the market; Growth of Real-time Payments, especially Buy Now Pay Later in the country. Potential restraints include: High Installation Costs Coupled with Maintenance Costs. Notable trends are: Digital Wallets to Drive the Payment Market.

National pension insurance, basic guaranteed old-age pension and monthly statistics data on cases and amounts of indigenous people's benefits

This data set is a subset of the "Records of foreign capital" (Registros de capitais estrangeiros", RCE) published by the Central Bank of Brazil (CBB) on their website.The data set consists of three data files and three corresponding metadata files. All files are in openly accessible .csv or .txt formats. See detailed outline below for data contained in each. Data files contain transaction-specific data such as unique identifier, currency, cancelled status and amount. Metadata files outline variables in the corresponding data file.RCE_Unclean_full_dataset.csv - all transactions published to the Central Bank website from the four main categories outlined belowMetadata_Unclean_full_dataset.csvRCE_Unclean_cancelled_dataset.csv - data extracted from the RCE_Unclean_full_dataset.csv where transactions were registered then cancelledMetadata_Unclean_cancelled_dataset.csvRCE_Clean_selection_dataset.csv - transaction data extracted from RCE_Unclean_full_dataset.csv and RCE_Unclean_cancelled_dataset.csv for the nine companies and criteria identified belowMetadata_Clean_selection_dataset.csvThe data include the period between October 2000 and July 2011. This is the only time span for the data provided by the Central Bank of Brazil at this stage. The records were published monthly by the Central Bank of Brazil as required by Art. 66 in Decree nº 55.762 of 17 February 1965, modified by Decree nº 4.842 of 17 September 2003. The records were published on the bank’s website starting October 2000, as per communique nº 011489 of 7 October 2003. This remained the case until August 2011, after which the amount of each transaction was no longer disclosed (and publication of these stopped altogether after October 2011). The disclosure of the records was suspended in order to review their legal and technical aspects, and ensure their suitability to the requirements of the rules governing the confidentiality of the information (Law nº 12.527 of 18 November 2011 and Decree nº 7724 of May 2012) (pers. comm. Central Bank of Brazil, 2016. Name of contact available upon request to Authors).The records track transfers of foreign capital made from abroad to companies domiciled in Brazil, with information on the foreign company (name and country) transferring the money, and on the company receiving the capital (name and federative unit). For the purpose of this study, we consider the four categories of foreign capital transactions which are published with their amount and currency in the Central Bank’s data, and which are all part of the “Register of financial transactions” (abbreviated RDE-ROF): loans, leasing, financed import and cash in advance (see below for a detailed description). Additional categories exist, such as foreign direct investment (RDE-IED) and External Investment in Portfolio (RDE-Portfólio), for which no amount is published and which are therefore not included.We used the data posted online as PDFs on the bank’s website, and created a script to extract the data automatically from these four categories into the RCE_Unclean_full_dataset.csv file. This data set has not been double-checked manually and may contain errors. We used a similar script to extract rows from the "cancelled transactions" sections of the PDFs into the RCE_Unclean_cancelled_dataset.csv file. This is useful to identify transactions that have been registered to the Central Bank but later cancelled. This data set has not been double-checked manually and may contain errors.From these raw data sets, we conducted the following selections and calculations in order to create the RCE_Clean_selection_dataset.csv file. This data set has been double-checked manually to secure that no errors have been made in the extraction process.We selected all transactions whose recipient company name corresponds to one of these nine companies, or to one of their known subsidiaries in Brazil, according to the list of subsidiaries recorded in the Orbis database, maintained by Bureau Van Dijk. Transactions are included if the recipient company name matches one of the following:- the current or former name of one of the nine companies in our sample (former names are identified using Orbis, Bloomberg’s company profiles or the company website);- the name of a known subsidiary of one of the nine companies, if and only if we find evidence (in Orbis, Bloomberg’s company profiles or on the company website) that this subsidiary was owned at some point during the period 2000-2011, and that it operated in a sector related to the soy or beef industry (including fertilizers and trading activities).For each transaction, we extracted the name of the company sending capital and when possible, attributed the transaction to the known ultimate owner.The name of the countries of origin sometimes comes with typos or different denominations: we harmonized them.A manual check of all the selected data unveiled that a few transactions (n=14), appear twice in the database while bearing the same unique identification number. According to the Central Bank of Brazil (pers. comm., November 2016), this is due to errors in their routine of data extraction. We therefore deleted duplicates in our database, keeping only the latest occurrence of each unique transaction. Six (6) transactions recorded with an amount of zero were also deleted. Two (2) transactions registered in August 2003 with incoherent currencies (Deutsche Mark and Dutch guilder, which were demonetised in early 2002) were also deleted.To secure that the import of data from PDF to the database did not contain any systematic errors, for instance due to mistakes in coding, data were checked in two ways. First, because the script identifies the end of the row in the PDF using the amount of the transaction, which can sometimes fail if the amount is not entered correctly, we went through the extracted raw data (2798 rows) and cleaned all rows whose end had not been correctly identified by the script. Next, we manually double-checked the 486 largest transactions representing 90% of the total amount of capital inflows, as well as 140 randomly selected additional rows representing 5% of the total rows, compared the extracted data to the original PDFs, and found no mistakes.Transfers recorded in the database have been made in different currencies, including US dollars, Euros, Japanese Yens, Brazilian Reais, and more. The conversion to US dollars of all amounts denominated in other currencies was done using the average monthly exchange rate as published by the International Monetary Fund (International Financial Statistics: Exchange rates, national currency per US dollar, period average). Due to the limited time period, we have not corrected for inflation but aggregated nominal amounts in USD over the period 2000-2011.The categories loans, cash in advance (anticipated payment for exports), financed import, and leasing/rental, are those used by the Central Bank of Brazil in their published data. They are denominated respectively: “Loans” (“emprestimos” in original source) - : includes all loans, either contracted directly with creditors or indirectly through the issuance of securities, brokered by foreign agents. “Anticipated payment for exports” (“pagamento/renovacao pagamento antecipado de exportacao” in original source): defined as a type of loan (used in trade finance)“Financed import” (“importacao financiada” in original source): comprises all import financing transactions either direct (contracted by the importer with a foreign bank or with a foreign supplier), or indirect (contracted by Brazilian banks with foreign banks on behalf of Brazilian importers). They must be declared to the Central Bank if their term of payment is superior to 360 days.“Leasing/rental” (“arrendamento mercantil, leasing e aluguel” in original source) : concerns all types of external leasing operations consented by a Brazilian entity to a foreign one. They must be declared if the term of payment is superior to 360 days.More information about the different categories can be found through the Central Bank online.(Research Data Support provided by Springer Nature)

This data set contains Help to Buy: Equity Loan statistics at parliamentary constituency level.

The figures cover the launch of the scheme on 1 April 2013 until 31 October 2014.

Figures have been attributed to an individual constituency by reconciling data against the ONS Postcode Directory (May 2014) where possible. Figures for some constituencies may be subject to revision later in the year.

. For sales before 31 March 2014, properties are included under the local authority district to which they were initially allocated. In some cases, this differs from latest information, which forms the basis of the first column of local authority district figures. Figures for some local authorities may be subject to revisions later in the year. Although local authority information is validated against other geographic data at the time of data entry, detailed reconciliation of the data, conducted twice a year, may result in a small number of changes to these monthly releases, for example where a new development crosses a local authority boundary.

An equity loan is Government financial assistance given to eligible applicants to purchase an eligible home through a Government equity mortgage secured on the home. The Government equity mortgage is ranked second in priority behind an owner’s main mortgage lender.

This scheme offers up to 20 per cent of the value as Government assistance to purchasers buying a new build home. The buyer must provide a cash deposit of at least 5 per cent and a main mortgage lender must provide a loan of at least 75 per cent.

The Government assistance to buy is made through an equity loan made by the Homes and Communities Agency (HCA) to the purchaser.

Help to Buy equity loans are only available on new build homes and the maximum purchase price is £600,000. Equity loan assistance for purchasers is paid via house builders registered with the HCA to participate in the Help to Buy equity loan initiative. The payment is made to builders (via solicitors) at purchaser legal completion.

The equity loan is provided without fees for the first five years of ownership.

The property title is held by the home owner who can therefore sell their home at any time and upon sale should provide the government the value of the same equity share of the property when it is sold.

For further information see
Help to Buy (equity loan) scheme monthly statistics.

Statistics on Labour Force, Unemployment and Underemployment - Table 210-06510 : Underemployed persons by monthly employment earnings (excluding Chinese New Year bonus/double pay) and sex

This statistical release makes available the most recent Mental Health and Learning Disabilities Dataset (MHLDDS) final monthly data (December 2014). This publication presents a wide range of information about care delivered to users of NHS funded secondary mental health and learning disability services in England. The scope of the Mental Health Minimum Dataset (MHMDS) was extended to cover Learning Disability services from September 2014. Many people who have a learning disability use mental health services and people in learning disability services may have a mental health problem. This means that activity included in the new MHLDDS dataset cannot be distinctly divided into mental health or learning disability spells of care - a single spell of care may include inputs from either of both types of service. We will be working with stakeholders to define specific information and reporting requirements relating to specific services or groups of patients. Four new measures have been added to this release to help with interpretation of the data. At local level these contextual figures will provide an indication of the increased caseload that could be attributed to the extension of the dataset to cover LD services. Information on these measures can found in the Announcement of Change paper which accompanies this release. The Currencies and Payment file that forms part of this release is specifically limited to services in scope for currencies and payment in mental health services and remains unchanged. This information will be of particular interest to organisations involved in delivering secondary mental health and learning disability care to adults and older people, as it presents timely information to support discussions between providers and commissioners of services. The MHLDS Monthly Report also includes reporting by local authority for the first time. For patients, researchers, agencies, and the wider public it aims to provide up to date information about the numbers of people using services, spending time in hospital and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis. The Currency and Payment (CaP) measures can be found in a separate machine-readable data file and may also be accessed via an on-line interactive visualisation tool that supports benchmarking. This can be accessed through the related links at the bottom of the page.

The Monthly Banking Statistics publication provides selected information on the banking business of individual banks within the domestic market.

It contains high-level breakdowns of the domestic assets and liabilities of each bank as well as more detail on loans & advances to and deposits by different sectors of the economy. Information on securitisation activity is also included. Both Australian-dollar denominated transactions and the Australian-dollar equivalent of foreign-currency denominated transactions are included.

The Balance of Payments is the statistical statement that systematically summarises transactions between residents and non-residents. It consists of the goods and services account, the primary income account, the secondary income account, the capital account and the financial account (BPM6 – 2.12) The financial account shows net acquisition and disposal of financial assets and liabilities The financial account indicates the functional categories, sectors, instruments, and maturities used for net international financing transactions. (BPM6 – 8.1). Five functional categories of investment are distinguished in the international accounts: a) direct investment, b) portfolio investment, c) financial derivatives and employee stock options, d) other investment and e) reserve assets. (BPM6 – 6.1). Source of euro area data: European Central Bank (ECB).

Key Table Information.Table Title.Construction: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2223BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesConstruction workers annual wages($1,000)Construction workers for pay period including March 12Construction workers for pay period including June 12Construction workers for pay period including September 12Construction workers for pay period including December 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Other employees for pay period including June 12Other employees for pay period including September 12Other employees for pay period including December 12Total fringe benefits ($1,000)Employers cost for legally required fringe benefits ($1,000)Employers cost for voluntarily provided fringe benefits ($1,000)Total selected costs ($1,000) Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of construction work subcontracted out to others ($1,000)Cost of purchased land ($1,000)Total cost of selected power, fuels, and lubricants ($1,000)Cost of gasoline and diesel fuel ($1,000)Cost of natural gas and manufactured gas ($1,000)Cost of on-highway use of gasoline and diesel fuel ($1,000)Cost of off-highway use of gasoline and diesel fuel ($1,000)Cost of all other fuels and lubricants ($1,000)Cost of purchased electricity ($1,000)Value of construction work ($1,000)Value of construction work on government owned projects ($1,000)Value of construction work on federally owned projects ($1,000)Value of construction work on state and locally owned projects ($1,000)Value of construction work on privately owned projects ($1,000)Value of other business done ($1,000)Value of construction work subcontracted in from others ($1,000)Net value of construction work ($1,000)Value added ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Gross value of depreciable assets (acquisition costs), beginning of year ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Total retirements ($1,000)Gross value of depreciable assets (acquisition costs), end of year ($1,000)Total depreciation during year ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous waste) services ($1,000)Advertising and promotional services ($1,000)Purchased professional and technical services ($1,000) Taxes and license fees ($1,000)All other operating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical locati...