Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.

In 2020, the personal consumption expenditures (PCE) of the United States came to 1.1 percent in a year over year comparison to the previous period. The PCE refers to household expenditures over a period of time and has become the measure for inflation in the United States. This inflation metric is forecasted to have a 2.2 percent year over year increase by 2024.

Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Kayah, Shan (East), Chin, Magway, Sagaing, Market Average

Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.

Argentina's dairy industry is experiencing significant price volatility, with the Consumer Price Index (CPI) for dairy products in the Greater Buenos Aires region reaching 9514.1 in December 2024. This marks a dramatic increase from 3998.9 just a year earlier, highlighting the rapid inflation affecting the sector. The surge in dairy prices is part of a broader trend of rising food costs across the country, impacting consumers and the industry alike. While the Greater Buenos Aires region leads in dairy product CPI, other regions are also experiencing substantial increases. For instance, the Pampeana region saw its bread and cereals CPI rise to 8405.1 in November 2024, up from 3247.2 the previous year. These price hikes are occurring despite the relatively stable milk consumption volume. In 2022, Argentina's milk consumption reached 11.9 million metric tons, with a slight increase to 12 million metric tons projected for 2023. Despite the challenging economic environment, some dairy brands have maintained strong consumer loyalty. In 2023, La Serenísima led the market, followed by La Paulina and Ilolay. However, the industry faces headwinds, as evidenced by the industrial production index (IPI) for dairy products, which stood at 127.5 points in August 2024, representing a 6.6 percent decrease from the previous year. This decline in production, coupled with rising prices, suggests potential challenges for both consumers and producers in the Argentine dairy market.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

In February 2025, the Consumer Price Index including owner occupiers' housing costs (CPIH) inflation rate of the United Kingdom was 3.7 percent, unchanged from the previous month. The inflation rate fell noticeably after the COVID-19 pandemic, but rose sharply between Spring 2021 and Autumn 2022. After peaking at 9.6 percent in October 2022, CPIH inflation declined throughout 2023 and into 2024, falling to 2.6 percent by September of that year, before increasing again in recent months. Cost of living problems persist into 2025 Although it is likely that the worst of the recent inflation surge may have passed, the issues caused by it look set to linger into 2025 and beyond. While the share of households experiencing living cost rises has fallen from 91 percent in August 2022, to 45 percent in July 2024, this share rose towards the end of the year, with more than half of households reporting rising costs in December. Even with lower inflation, overall consumer prices have already increased by around 20 percent in the last three years, rising to almost 30 percent for food prices, which lower income households typically spend more of their income on. The significant increase in people relying on food banks across the UK, is evidence of the magnitude of this problem, with approximately 3.12 million people using food banks in 2023/24. Other measure of inflation While the CPIH inflation rate displayed here is the preferred index of the UK's Office of National Statistics, the Consumer Price Index (CPI) is often more prominently featured in the media in general. An older index, the Retail Price Index (RPI) is also still used by the government to calculate certain taxes, and rail fare rises. Other metrics include the core inflation rate, which measures prices increases without the volatility of food and energy costs, while price increases in goods and services can also be tracked separately. The inflation rate of individual sectors can also be measured, and as of December 2024, prices were rising fastest in the communications sector, at 6.1 percent, with costs falling in the transport and furniture sectors.

The Automatic Tire Inflation System (ATIS) market is poised for significant growth over the next decade, driven by increasing demand for fuel efficiency, extended tire lifespan, and improved vehicle safety. The market is projected to grow from USD 2,428.8 million in 2025 to USD 4,188.3 million by 2035, at a compound annual growth rate (CAGR) of 5.6% during the forecast period.

Metric	Value
Industry Size (2025E)	USD 2,428.8 million
Industry Value (2035F)	USD 4,188.3 million
CAGR (2025 to 2035)	5.6%

Country-Wise Analysis

Country	CAGR (2025 to 2035)
United States	5.4%

Country	CAGR (2025 to 2035)
United Kingdom	5.3%

Country	CAGR (2025 to 2035)
Germany	5.8%

Country	CAGR (2025 to 2035)
Japan	5.7%

Country	CAGR (2025 to 2035)
South Korea	5.6%

Competitive Outlook

Company Name	Estimated Market Share (%)
Dana Incorporated	12-18%
SAF-Holland	10-15%
Michelin	9-13%
The Goodyear Tire & Rubber Company	7-12%
Hendrickson USA	5-9%
Other Companies	40-50%

We monitor and process economic data and financial indicators across 200+ global markets, covering inflation trends, bankruptcy filings, and consensus estimates with 100+ key data points for macroeconomic analysis, risk modeling, and investment strategies.

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The average inflation rate in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.6 percentage points. The inflation is estimated to amount to two percent in 2029. Following the definitions provided by the International Monetary Fund, this indicator measures inflation based upon the year on year change in the average consumer price index. The latter expresses a country's average level of prices based on a typical basket of consumer goods and services. Depicted here is the year-on-year change in said index measure, expressed in percent.Find more statistics on other topics about the United Kingdom with key insights such as the total population, the national debt, and the share in the global GDP adjusted for purchasing power parity.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Market Average

Abstract

Energy price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes energy price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Market Average

The so-called Big Mac index is regarded as an indicator for the purchasing power of an economy. The average price for a Big Mac burger in Mexico was estimated at 5.19 U.S. dollars in January 2024. Due to the high increases during the last few years, the Big Mac burger price became one of the highest in Latin America.

Big Mac Index

The Bic Mac index has been published annually by The Economist since 1986 and is rated as a simplified indicator of a country’s individual purchasing power. As many countries have different currencies, the standardized Big Mac prices are calculated by converting the average national Big Mac prices with the latest exchange rate to U.S. dollars. The Big Mac, as the top-selling McDonald’s burger, is used for comparison because it is available in almost every country and manufactured in a standardized size, composition and quality. McDonald’s is a worldwide operating fast food restaurant chain with headquarters in Oak Brook, Illinois. In Latin America, McDonald's largest franchisee is Arcos Dorados Holdings, with headquarters in Montevideo, Uruguay.

Power Purchasing Parity

This conversion endeavor seeks to level the purchasing power disparities among nations by neutralizing price discrepancies. Notably, in Mexico, the Purchasing Power Parity (PPP) has demonstrated a consistent upward trajectory, yielding positive repercussions on the minimum wage for the labor force. This, in turn, has triggered a favorable effect on the affordability of the essential food basket. Furthermore, this upswing has propelled five major Mexican cities into the upper positions of PPP rankings within Latin America. Consequently, Mexico now stands as the 15th largest global economy, a status achieved despite a slight, yet steady, decline in its share of the global GDP, which is adjusted according to PPP metrics.

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

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2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.

Abstract

Energy price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes energy price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Lori, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Armavir, Vayotz Dzor, Yerevan, Market Average