Coincident Index in Japan remained unchanged at 116 points in May. This dataset provides the latest reported value for - Japan Coincident Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In May 2025, the index for consumer confidence in China ranged at ** points, up from **** points in the previous month. The index dropped considerably in the first half of 2022 and performed a sideways movement during 2023 and 2024. Consumer confidence Index The consumer confidence index (CCI), also called Index of Consumer Sentiment (ICS) is a commonly used indicator to measure the degree of economic optimism among consumers. Based on information about saving and spending activities of consumers, changes in business climate and future spending behavior are being projected. The CCI plays an important role for investors, retailers, and manufacturers in their decision-making processes. However, measurement of consumer confidence varies strongly from country to country. As consumers need time to react to economic changes, the CCI tends to lag behind other indicators like the consumer price index (CPI) and the producer price index (PPI). Development in China As shown by the graph at hand, confidence among Chinese consumers picked up since mid of 2016. In October 2017, the CCI hit a record value of 127.6 index points and entered into a sideward movement. Owing to a relative stability in GDP growth, a low unemployment rate, and a steady development of disposable household income, Chinese consumers gained more confidence in the state of the national economy. Those factors also contribute to the consumers’ spending power, which was reflected by a larger share of consumption in China’s GDP. After the outbreak of the coronavirus pandemic, consumer confidence dropped quickly in the beginning of 2020, but started to recover in the second half of the year, leading to a v-shaped movement of the index in 2020.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

General Abstract/Purpose (70 words): Data were collected to assist in cost-benefit analysis of flood mitigation actions that could be taken by the U.S. and Canada to prevent structural damage and associated costs and losses in future flood conditions, including conditions worse than the historical record flooding in spring of 2011. Data were commissioned to revise or fill gaps in estimates from structural damage modeling software commonly used for depth-damage economic assessments of flood impacts. The Summary text that immediately follows this introductory sentence offers overview information, but also includes context and detail that is not present in the Word document ("Principal Indicator Combo SET - REVIEW FINAL v2.docx") that constitutes the main body of this data release, supported by Excel files (that are copied without formatting in csv files for each Excel tab). Lake Champlain is a relatively large lake bordered by New York on the western side and Vermont on the eastern side, whose uppermost region spans the U.S.-Canadian border. The 436 mi^2 (1,130 km^2) lake sits within a 9,277 mi^2 (23,900 km^2) basin, and Champlain’s only drainage point is north into Canada via the Richelieu River into the province of Quebec. About 75% of the Lake Champlain shoreline of New York is within Adirondack State Park, covering all or part of Clinton, Essex, and Washington counties. Of Vermont’s 14 counties, Franklin, Chittenden, and Addison Counties border Lake Champlain, while Grand Isle is surrounded by Champlain and at its northern edge the Canadian border. Development and anthropogenic modifications, especially over the last 50 years, have converted wetlands, changed the timing and flows of water, and increased impervious surface area including new residences in floodplains on both sides of the border. Occasionally there is damaging flooding, with significant economic damages in New York, Vermont, and Quebec. With flood stage at 99.57’ (30.35m) and major flooding from 101.07’ (30.81m) over sea level, a 101.4’ (30.91m) flood in 1993 broke the previous recorded high flood in 1869. Following the third heaviest recorded snow, almost no seasonal snowmelt, then heavy rains, the spring of 2011 brought record flooding more than one foot over the 1993 record to 102.77’ (31.32m), expanding the lake’s area by 66 mi^2 (106.2 km^2, or about 5.8%). From reaching flood stage to peak and then returning to a lake level below flood stage took around six weeks. Wind-to-wave-driven erosion was up to 5 feet (1.5m) above static lake elevation in some areas. The record flood height (102.77’) is often reported as 103.07’ or 103.27’ in Burlington, owing to different vertical and horizontal datums and digital elevation models (DEMs), and some wave action. In a 1976 flood the U.S. side incurred more than 50% of the economic damages, but in 2011, Quebec experienced some 80% of structural and economic damages estimated at $82 million. Tropical Storm Irene hit the area in August of 2011 and did far more damage on the American side, for example spurring $29 million in home and business repair loans for damage across 12 of Vermont’s 14 counties. Co-reporting across the two events for 2011 confounded some data, making it impossible to separately identify spring flooding numbers. Following the Boundary Waters Treaty between the U.S. and Canada in 1909, from 1912 the International Joint Commission (IJC) handles boundary water issues between the two countries. The IJC Lake Champlain Richelieu River (LCRR) Study Project is a bi-national (U.S., Canada) multi-agency effort to assess flood risk and flood mitigation options as they affect potential structural damages and wider non-structural damages that include secondary economic, community, and psychological effects. Key economic parts of the report to the IJC LCRR Study Board are calculated using a new tool developed for the study project, an Integrated Socio-Economic-Environmental (ISEE) model, with forecasting for damages up to 105.57’ flood (105.9’, or 106’ [32.3m] for short, by alternative datum and DEMs, as apply in some of the modeling and estimations herein). There is also a Collaborative Decision Support Tool (CDST) that also processes non-structural economic damages, costs, or losses as inputs. CDST is a pared-down version of ISEE that applies historical estimates but does not project outcomes for higher floods in the future. Outputs from this data release are inputs to the ISEE or the CDST for calculations of the benefit-to-cost ratios projected to follow different structural interventions. For example adding a weir in the Richelieu River yielded a greater-than-one benefit-to-cost ratio in late-stage modeling, whereas a dam on either side, or an entirely new canal on the Canadian side, were never entertained as cost feasible or even appropriate. USGS economists were contracted to supply economic “principal indicators” for potential U.S.-side depth-damage effects from lake-rise flooding. The scope of this analysis is limited by several factors associated with the objectives of the IJC LCRR Study Board. Damages from tributary flooding were defined out of a project focused on joint-management options for mitigating flood effects, as tributary flows would be managed only by the U.S. Uncommonly low Lake Champlain levels were also ultimately considered as a stakeholder concern (the weir option also addressed this concern). It is standard to model economic damages to structures and related economic costs due to flooding using the FEMA-designed Hazus®-MH (Multi-Hazard) Flood Model of structural damages (https://www.fema.gov/flood-maps/products-tools/hazus; the Hazus-MH Technical Manual, 2011, 569pp, which explains definitions and parameterization of the tool rather than use of the tool itself, is a frequently referred source here). “Hazus” (tool) modeling is used in the LCRR Study Board research to estimate structural damages at different flood depths, and the primary work presented in this data release estimates depth-damage values for “Principal Indicators” (PIs) that were defined to supplement or alternatively estimate results from applying Hazus, where gaps exist or where straight Hazus values may be questionable in the LCRR context. A number of Principal Indicators were estimated on the Canadian and U.S. sides, where no PIs include any estimates for repair of structural damage, as those calculations are done separately using the Hazus tool (or the ISEE model application with Hazus outputs as inputs). In the final list, the USGS team produced estimates for six PIs: temporary lodging costs, residential debris clean-up and disposal, damage to roads and bridges, damage to water treatment facilities, income loss from industrial or commercial properties, and separately and specifically recreation sector income loss. So associated with residential damage, the costs of securing emergency and longer-term lodging when a household is displaced by lake-rise flooding are estimated, and the costs of cleaning up and removing and disposing of debris from residential property damage are estimated. In the public sector, costs of clean up and repair of damages to roads and bridges from lake-rise flooding are calculated, as are damages and potential revenue losses from flood mitigation measures and service reductions where public or private water utilities are inundated by lake-rise flooding. In the commercial sector, revenue losses from being closed for business due to flooding are calculated outside of the recreation sector, and then also for the recreation sector as lakeside campgrounds, marinas, and ferry services (where the last is also used for local commercial traffic). All of these PIs are characterized by being little-discussed in the literature. To derive information necessary to bound economic estimates for each of the 6 PIs, consultation with subject-matter experts in New York and Vermont (or at agencies covering these areas) was employed more often than anything in peer-reviewed literature specifically applied. Depth-damage functions that result are not formal mathematical functions, and across the six PIs calculations and results tend to be in increments of one foot or more. Results thus suggest magnitudes of costs that comply with reasonable scenario assumptions for a small but fairly consistent set of flood depths from 99.57’ to 105.57’, where the latter value is almost three feet (1m) above the historic maximum flood. Nothing reported in these estimates is empirically deterministic, or capable of including probabilistic error margins. Simplifying assumptions serve first to actually simplify the calculations and legibility of estimated results, and second to avoid the impression that specifically calibrated empirical estimations are being conducted. This effort offers plausible, logical, reliable, and reproducible magnitudes for estimates, using a method that can be easily modified if better information becomes available for future estimations. Certain worksheets and specific results are withheld to avoid the outright identification of specific businesses (or homes). Facts in this abstract generally attribute to: International Lake Champlain-Richelieu River Study Board, 2019. The Causes and Impacts of Past Floods in the Lake Champlain-Richelieu River Basin – Historical Information on Flooding, A Report to the International Joint Commission, 108pp (https://ijc.org/en/lcrr). Some supplemental factual support is from: Lake Champlain Basin Program, 2013. Flood Resilience in the Lake Champlain Basin and Upper Richelieu River, 93 pp (https://ijc.org/en/lcrr).

In the 2023 edition of the globalization index, Switzerland had the highest index score at 90.75. Belgium followed behind, with the Netherlands in third. Overall, globalization declined in 2020 due to the COVID-19 outbreak, but increased somewhat in 2021, even though it was still below pre-pandemic levels.

About the index

The KOF Index of Globalization aims to measure the rate of globalization in countries around the world. Data used to construct the 2023 edition of the index was from 2021. The index is based on three dimensions, or core sets of indicators: economic, social, and political. Via these three dimensions, the overall index of globalization tries to assess current economic flows, economic restrictions, data on information flows, data on personal contact, and data on cultural proximity within surveyed countries.

Defining globalization

Globalization is defined for this index as the process of creating networks of connections among actors at multi-continental distances, mediated through a variety of flows including people, information and ideas, capital and goods. It is a process that erodes national boundaries, integrates national economies, cultures, technologies and governance and produces complex relations of mutual interdependence.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Jun 2025 about recession indicators, academic data, and USA.

The statistic lists the 20 countries with the lowest inflation rate in 2023. In 2023, China ranked 5th with a inflation rate of about 0.23 percent compared to the previous year. Inflation rates and the financial crisis Due to relatively stagnant worker wages as well as a hesitation from banks to so easily distribute loans to the ordinary citizen, inflation has remained considerably low. Low inflation rates are most apparent in European countries, which stems from the on-going Eurozone debt crisis as well as from the global financial crisis of 2008. With continuous economical struggles and a currently sensitive economic situation throughout Europe, precautions were taken in order to maintain stability and to prevent consequential breakdowns, such as those in Greece and Spain. Additionally, the average European consumer had to endure financial setbacks, causing doubt in the general future of the entire European Union, as evident in the consumer confidence statistics, which in turn raised the question, if several handpicked countries should step out of the EU in order to improve its economic position. Greece, while perhaps experiencing the largest economic drought out of all European countries, improved on its inflation rate. The situation within the country is slowly improving itself as a result of a recent bailout as well as economic stimulus packages issued by the European Union. Furthermore, the Greek government managed its revenues and expenses more competently in comparison to the prime of the global and the Greek financial crisis, with annual expenses only slightly exceeding yearly revenues.

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

Key Change: Will KYCH Stock Hit the Right Note?

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

The UK economy shrank by 0.1 percent in May 2025 after shrinking by 0.3 percent in April. Since a huge decline in GDP in April 2020, the UK economy has gradually recovered and is now around 4.4 percent larger than it was before the COVID-19 pandemic. After the initial recovery from the pandemic, however, the UK economy has effectively flatlined, fluctuating between low growth and small contractions since January 2022. Labour banking on growth to turn around fortunes in 2025 In February 2025, just over half a year after winning the last general election, the approval rating for the new Labour government fell to a low of -48 percent. Furthermore, the Prime Minister, Keir Starmer was not only less popular than the new Conservative leader, Kemi Badenoch, but also the leader of the Reform Party, Nigel Farage, whose party have surged in opinion polls recently. This remarkable decline in popularity for the new government is, in some part, due to a deliberate policy of making tough decisions early. Arguably, the most damaging of these policies was the withdrawal of the winter fuel allowance for some pensioners, although other factors such as a controversy about gifts and donations also hurt the government. While Labour aims to restore the UK's economic and political credibility in the long term, they will certainly hope for some good economic news sooner rather than later. Economy bounces back in 2024 after ending 2023 in recession Due to two consecutive quarters of negative economic growth, in late 2023 the UK economy ended the year in recession. After not growing at all in the second quarter of 2023, UK GDP fell by 0.1 percent in the third quarter, and then by 0.3 percent in the last quarter. For the whole of 2023, the economy grew by 0.4 percent compared to 2022, and for 2024 is forecast to have grown by 1.1 percent. During the first two quarters of 2024, UK GDP grew by 0.7 percent, and 0.4 percent, with this relatively strong growth followed by zero percent growth in the third quarter of the year. Although the economy had started to grow again by the time of the 2024 general election, this was not enough to save the Conservative government at the time. Despite usually seen as the best party for handling the economy, the Conservative's economic competency was behind that of Labour on the eve of the 2024 election.

France's main stock market index, the FR40, fell to 7546 points on August 1, 2025, losing 2.91% from the previous session. Over the past month, the index has declined 2.48%, though it remains 4.06% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on August of 2025.

Italy's Gross Domestic Product (GDP) amounted to 2.13 trillion euros in 2023. The Italian economy grew at low rates between 2010 and 2019, and significantly shrank in 2020 following the consequences of the pandemic on the global economy. However, since 2021, GDP recorded a steady uprise, with remarkably higher growth rates compared to the pre-pandemic period. A difficult outlook for the Italian economy Besides the positive performance recorded right after the COVID-19 pandemic, projections indicate a different outlook. The slow growth of the Italian economy, less than one percent each year from 2024 to 2029, is believed to remove Italy from the giants of the global players. Indeed, by 2028, the ranking of the world's largest economies might appear quite different from the present one. In addition to slow growth, Italy's economy is characterized by large internal disparities. After 160 years of national unity, the country is economically still very divided, as data on unemployment, GDP, and poverty confirm. National debt: Italy's most difficult challenge Italy still ranges among the top-20 largest economies in the world. However, the large amount of the national debt risks hampering future growth. In 2023, it reached 134 percent of the GDP, equivalent to 3.1 trillion U.S. dollars, and forecasts expect figures to increase over the coming years. By 2029, the debt-to-GDP ratio may hit 145 percent. A large amount of national debt significantly limits the government's possibility to earmark resources for public investments. In fact, a considerable share of the state budget is devoted to reimbursing the debt.

Silo Indicator Market Outlook

The global silo indicator market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.3 billion by 2032, expanding at a robust Compound Annual Growth Rate (CAGR) of 7.1% during the forecast period. The market is driven by increasing industrial automation and the growing emphasis on precision in inventory management across various sectors. The rise in global food demand, coupled with advancements in sensor technology, are key factors propelling the market's significant growth.

One of the primary growth factors of the silo indicator market is the agricultural sector's increasing reliance on accurate and real-time inventory management solutions. With the global population expected to hit 9.7 billion by 2050, the demand for food production is anticipated to surge. Farmers and agricultural enterprises are adopting advanced technologies to enhance productivity and minimize losses. Silo indicators play a crucial role in ensuring optimal storage conditions for grains and other agricultural products, preventing spoilage and maintaining quality, thereby driving the market's expansion.

The construction and manufacturing industries are also significant contributors to the growth of the silo indicator market. These sectors require precise measurement and monitoring of materials such as cement, sand, and powders. With increasing infrastructure development and industrial growth worldwide, the demand for efficient inventory management solutions is rising. Silo indicators help in maintaining the right stock levels, reducing material wastage, and optimizing production processes. The integration of digital solutions and IoT-enabled devices further enhances the accuracy and efficiency of these systems.

Technological advancements and innovation in silo indicators are another critical factor propelling the market. The transition from analog to digital silo indicators, incorporating features like remote monitoring, real-time data analytics, and predictive maintenance, is revolutionizing the industry. The adoption of smart sensors and wireless communication technologies enables seamless data transfer and integration with other industrial systems, providing enhanced control and operational efficiency. Such advancements are attracting investments and fostering market growth.

Regionally, North America and Europe are the leading markets for silo indicators, driven by the high adoption rate of advanced technologies and stringent regulatory standards for inventory management in these regions. Additionally, the increasing focus on sustainable agricultural practices and efficient resource utilization is boosting market growth. The Asia Pacific region is expected to witness the highest CAGR during the forecast period, fueled by rapid industrialization, urbanization, and the growing agricultural sector in countries like China and India. Latin America and the Middle East & Africa regions are also experiencing steady growth due to ongoing infrastructural developments and the expanding food and beverage industry.

Type Analysis

The silo indicator market is segmented by type into analog silo indicators and digital silo indicators. Analog silo indicators represent the traditional form of inventory management, providing basic measurement and monitoring functions. These devices are generally more affordable and easier to operate, making them suitable for smaller operations or regions with limited technological infrastructure. However, their lack of advanced features and lower precision compared to digital counterparts can be a limitation for large-scale industrial applications. Despite this, the ongoing demand for cost-effective solutions ensures a steady market for analog silo indicators.

On the other hand, digital silo indicators are gaining significant traction due to their superior accuracy, real-time data capabilities, and integration with modern digital systems. These indicators utilize advanced sensor technologies and often come with features like remote monitoring, automated alerts, and data analytics. The ability to connect with IoT platforms and industrial control systems provides enhanced operational efficiency and decision-making capabilities. As industries increasingly adopt digital transformation strategies, the demand for digital silo indicators is expected to surge, making this segment the fastest-growing in the market.

In the first quarter of 2020, global stock indices posted substantial losses that were triggered by the outbreak of COVID-19. The period from March 6 to 18 was particularly dramatic, with several stock indices losing more than ** percent of their value. Worldwide panic hits markets From the United States to the United Kingdom, stock market indices suffered steep falls as the coronavirus pandemic created economic uncertainty. The Nasdaq 100 and S&P 500 are two indices that track company performance in the United States, and both lost value as lockdowns were introduced in the country. European markets also recorded significant slumps, which triggered panic selling among investors. The FTSE 100 – the leading share index of companies in the UK – plunged by as much as ** percent in the opening weeks of March 2020. Is it time to invest in tech stocks? The S&P 500 is regarded as the best representation of the U.S. economy because it includes more companies from the leading industries. However, helped in no small part by its focus on tech companies, the Nasdaq 100 has risen in popularity and seen remarkable growth in recent years. Global demand for digital technologies has increased further due to the coronavirus, with remote working and online shopping becoming part of the new normal. As a result, more investors are likely to switch to the tech stocks listed on the Nasdaq 100.

Spain's main stock market index, the ES35, fell to 14127 points on August 1, 2025, losing 1.88% from the previous session. Over the past month, the index has climbed 0.58% and is up 32.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on August of 2025.

The UK inflation rate was 3.6 percent in June 2025, up from 3.4 percent in the previous month, and the fastest rate of inflation since January 2024. Between September 2022 and March 2023, the UK experienced seven months of double-digit inflation, which peaked at 11.1 percent in October 2022. Due to this long period of high inflation, UK consumer prices have increased by over 20 percent in the last three years. As of the most recent month, prices were rising fastest in the communications sector, at 6.1 percent, but were falling in both the furniture and transport sectors, at -0.3 percent and -0.6 percent, respectively.
The Cost of Living Crisis High inflation is one of the main factors behind the ongoing Cost of Living Crisis in the UK, which, despite subsiding somewhat in 2024, is still impacting households going into 2025. In December 2024, for example, 56 percent of UK households reported their cost of living was increasing compared with the previous month, up from 45 percent in July, but far lower than at the height of the crisis in 2022. After global energy prices spiraled that year, the UK's energy price cap increased substantially. The cap, which limits what suppliers can charge consumers, reached 3,549 British pounds per year in October 2022, compared with 1,277 pounds a year earlier. Along with soaring food costs, high-energy bills have hit UK households hard, especially lower income ones that spend more of their earnings on housing costs. As a result of these factors, UK households experienced their biggest fall in living standards in decades in 2022/23. Global inflation crisis causes rapid surge in prices The UK's high inflation, and cost of living crisis in 2022 had its origins in the COVID-19 pandemic. Following the initial waves of the virus, global supply chains struggled to meet the renewed demand for goods and services. Food and energy prices, which were already high, increased further in 2022. Russia's invasion of Ukraine in February 2022 brought an end to the era of cheap gas flowing to European markets from Russia. The war also disrupted global food markets, as both Russia and Ukraine are major exporters of cereal crops. As a result of these factors, inflation surged across Europe and in other parts of the world, but typically declined in 2023, and approached more usual levels by 2024.

Business Confidence in Switzerland increased to 101.10 points in July from 96.30 points in June of 2025. This dataset provides the latest reported value for - Switzerland Business Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2024, the gross domestic product (GDP) of China amounted to around 18.7 trillion U.S. dollars. In comparison to the GDP of the other BRIC countries India, Russia and Brazil, China came first that year and second in the world GDP ranking. The stagnation of China's GDP in U.S. dollar terms in 2022 and 2023 was mainly due to the appreciation of the U.S. dollar. China's real GDP growth was 3.1 percent in 2022 and 5.4 percent in 2023. In 2024, per capita GDP in China reached around 13,300 U.S. dollars. Economic performance in China Gross domestic product (GDP) is a primary economic indicator. It measures the total value of all goods and services produced in an economy over a certain time period. China's economy used to grow quickly in the past, but the growth rate of China’s real GDP gradually slowed down in recent years, and year-on-year GDP growth is forecasted to range at only around four percent in the years after 2024. Since 2010, China has been the world’s second-largest economy, surpassing Japan.China’s emergence in the world’s economy has a lot to do with its status as the ‘world’s factory’. Since 2013, China is the largest export country in the world. Some argue that it is partly due to the undervalued Chinese currency. The Big Mac Index, a simplified and informal way to measure the purchasing power parity between different currencies, indicates that the Chinese currency yuan was roughly undervalued by 38 percent in 2024. GDP development Although the impressive economic development in China has led millions of people out of poverty, China is still not in the league of industrialized countries on the per capita basis. To name one example, the U.S. per capita economic output was more than six times as large as in China in 2024. Meanwhile, the Chinese society faces increased income disparities. The Gini coefficient of China, a widely used indicator of economic inequality, has been larger than 0.45 over the last decade, whereas 0.40 is the warning level for social unrest.

In June 2025, the monthly inflation rate in China ranged at 0.1 percent compared to the same month in the previous year. Inflation had peaked at 2.8 percent in September 2022, but eased thereafter. The annual average inflation rate in China ranged at 0.2 percent in 2024. China’s inflation in comparison The term inflation means the devaluation of money caused by a permanent increase of the price level for products such as consumer or investment goods. The inflation rate is most commonly measured by the Consumer Price Index. The Consumer Price Index shows the price development for private expenses based on a basket of products representing the consumption of an average consumer household. Compared to other major economies in the world, China has a moderate and stable level of inflation. The inflation in China is on average lower than in other BRIC countries, although China enjoys higher economic growth rates. Inflation rates of developed regions in the world had for a long time been lower than in China, but that picture changed fundamentally during the coronavirus pandemic with most developed countries experiencing quickly rising consumer prices. Regional inflation rates in China In China, there is a regional difference in inflation rates. As of May 2025, Shaanxi province experienced the highest CPI growth, while Guangxi reported the lowest. In recent years, inflation rates in rural areas have often been slightly higher than in the cities. According to the National Bureau of Statistics of China, inflation was mainly fueled by a surge in prices for food and micellaneous items and services in recent months. The price gain in other sectors was comparatively slight. Transport prices have decreased recently, but had grown significantly in 2021 and 2022.

Abstract

The Nepal Multiple Indicator Cluster Survey (MICS) was carried out in 2014 by the Central Bureau of Statistics (CBS) as part of the global MICS programme. Technical and financial support was provided by the United Nations Children’s Fund (UNICEF). The global MICS programme was developed by UNICEF in the 1990s as an international household survey programme to support countries in the collection of internationally comparable data on a wide range of indicators on the situation of children and women. MICS surveys measure key indicators that allow countries to generate data for use in policies and programmes, and to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

The Nepal Multiple Indicator Cluster Survey (MICS 2014) was conducted by the Central Bureau of Statistics under the National Planning Commission from January to June 2014. Technical and financial support for the survey was provided by the United Nations Children’s Fund (UNICEF) Nepal.

Nepal MICS 2014 provides valuable information and the latest evidence on the situation of children and women in Nepal before the country was hit by an earthquake of 7.8 magnitude on 25 April 2015. The survey presents data from an equity perspective by indicating disparities by sex, region, area, education, household wealth, and other characteristics. Nepal MICS 2014 is based on a sample of 12,405 households interviewed and provides a comprehensive picture of children and women in the 15 sub-regions of the country.

Geographic coverage

National coverage

Analysis unit

• Individuals
• Households

Universe

The survey covered all de jure household members (usual residents) the household, and the dwelling, all women aged 15-49 years resident in the household, all children aged 0-4 years (under age 5) resident in the household, and water quality testing questionnaire to test for bacteria and measure E. coli content in household drinking water and water source in a subsample of the households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Nepal MICS 2014 was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the 15 ecological zones of the country: Eastern Mountains, Eastern Hills, Eastern Terai, Central Mountains, Central Hills, Central Terai, Western Mountains, Western Hills, Western Terai, Mid-Western Mountains, Mid-Western Hills, Mid-Western Terai, Far Western Mountains, Far Western Hills, Far Western Terai. Urban and rural areas in each of the 15 ecological zones were defined as the sampling strata. The Central Hills zone is further divided into two substrata as Kathmandu Valley and Other urban areas.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

Water quality testing was carried out in each of the 519 clusters sampled for this survey. Three households were selected from the list of 25 households interviewed in each cluster using a random systematic selection procedure. This yielded a total of 1,557 households for E. coli testing in drinking water. For one of the three households in each cluster, a sample was also taken from the household's source of drinking water, yielding 519 samples. Samples of household drinking water were taken from a glass of water that would be given to a child to drink, and each sample of source water was collected in a sterile Whirl-Pak bag.

The sample size for the Nepal MICS 2014 was calculated as 13,000 households. For the calculation of the sample size, the key indicator used was the birth registration prevalence among children aged 0-4 years.

For the calculation, r (birth registration) was assumed to be 42.3 percent. The value of deff (design effect) was taken as 2 based on estimates from previous surveys, pb (percentage of children aged 0-4 years in the total population) was taken as 9.7 percent, AveSize (average household size) was taken as 4.88 persons per household, and the response rate was assumed to be 95 percent, based on experience from previous surveys.

Calculations of the required sample sizes indicated that 800 households per domain would be adequate to yield estimates with sufficient precision for most of the indicators, but in the case of three large domains (Eastern Terai, Central Terai, and Western Hills) the decision was made to increase the sample size to 1,000 households. One domain (Western Mountains) posed a particular problem because of its small size. The natural inclination would be to combine it with Mid-Western Mountains, but that was considered undesirable, because of the need to have a separate estimate for this latter domain (which is also known as Karnali). The decision was therefore made to keep Western Mountains as a separate domain. Only 400 households were allocated to it on the clear understanding that the resulting estimates were bound to have lower precision than corresponding estimates for other domains. The overall total sample size was 13,000 households.

The number of households selected per cluster for the Nepal MICS 2014 was determined as 25 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 40, 32 or 16 sample clusters would need to be selected in each zone.

The sampling procedures are more fully described in "Multiple Indicator Cluster Survey 2014 - Final Report" pp.233-237.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four sets of questionnaires were used in the survey: (1) a household questionnaire which was used to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a questionnaire for individual women administered in each household to all women aged 15–49 years; (3) an under-5 questionnaire, administered to mothers (or caretakers) for all children under five years of age living in the household; and (4) a water quality testing questionnaire to test for bacteria and measure E. coli content in household drinking water and water source in a subsample of the households.

The Household Questionnaire included the following modules: List of Household Members, Education, Child Labour, Child Discipline, Household Characteristics, Water and Sanitation, Handwashing, Salt Iodization.

The Questionnaire for Individual Women was administered to all women aged 15–49 years living in the households, and included the following modules: Woman’s Background, Access to Mass Media and Use of Information/Communication Technology, Fertility/Birth History, Desire for Last Birth, Maternal and Newborn Health, Postnatal Health Checks, Illness Symptoms, Contraception, Unmet Need, Attitudes Toward Domestic Violence, Marriage/Union, HIV/AIDS, Tobacco and Alcohol Use, Life Satisfaction.

The Questionnaire for Children Under Five was administered to mothers (or caretakers) of children under five years of age1 living in the households. Normally, the questionnaire was administered to mothers of under-5s; in cases when the mother was not listed in the household roster, a primary caretaker for the child was identified and interviewed. The questionnaire included the following modules: Age, Birth Registration, Early Childhood Development, Breastfeeding and Dietary Intake, Immunization, Care of Illness, Anthropometry.

The Questionnaire for Water Quality Testing was administered to a sub-sample of selected households for measuring E. coli content in the household drinking water and included only one module: Water Quality

The questionnaires are based on the MICS5 model questionnaire. From the MICS5 model English version, the questionnaires were customized and translated into Nepali, Maithili and Bhojpuri. Pre-test training was conducted in Dhulikhel, Kavre District, from 25 October to 2 November 2013. Pre-test fieldwork was conducted in 25 households of both urban and rural locations in Sindhupalchowk District (Mountains), Tanahun District (Hills) and Dhanusa District (Terai) during November 2013. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires. A copy of the Nepal MICS questionnaires is provided in Appendix F.

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, observed the place for handwashing, and measured the weights and heights of children under five. Details and findings of these observations and measurements are provided in the respective sections of the report.

In each cluster, water from three households and one source of drinking water were tested for E. coli. Testing was conducted by the team measurer. As a routine quality control measure, the supervisor regularly observed the measurer in the testing of blanks. In addition, professional laboratory technicians from an external agency were engaged for the purpose. They visited field teams during the survey and observed the measurers during testing, giving corrective support as needed.

Cleaning operations

Data were entered using CSPro software, Version 5.0. Data were entered on 10 laptop computers by 10 data-entry operators, one questionnaire administrator, overseen by one data-entry supervisor with two secondary editors. For quality assurance purposes, all questionnaires were double-entered and internal consistency checks were

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