United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data was reported at 168.237 Jan1997=100 in Nov 2018. This records an increase from the previous number of 166.528 Jan1997=100 for Oct 2018. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data is updated monthly, averaging 96.825 Jan1997=100 from Jan 1973 (Median) to Nov 2018, with 551 observations. The data reached an all-time high of 168.237 Jan1997=100 in Nov 2018 and a record low of 1.998 Jan1997=100 in Jul 1973. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

The statistic shows the development of the MSCI World USD Index from 1986 to 2024. The 2024 year-end value of the MSCI World USD index amounted to 3,707.84 points. MSCI World USD index – additional information The MSCI World Index, developed by Morgan Stanley Capital International (MSCI), is one of the most important stock indices. It includes stocks from developed countries all over the world and is regarded as benchmark of global stock market. According to MSCI, this index covers about 88 percent of the free float-adjusted market capitalization in each country. As seen on the statistics above, in 2024, MSCI World USD index reported its highest value since 1986 amounting, a threefold increase from the figure recorded in 2013, when the year-end value of the MSCI World index was equal to 1,161.07. Along with the S&P Global Broad Market, the MSCI World is one of the most important global stock market performance indexes. Aside of including markets around the globe, these two indexes are global in a sense that they disregard where the companies are domiciled or traded, whereas other important indexes such as the Dow Jones Industrial Average, the Japanese index Nikkei 225, Wilshire 5000, the NASDAQ 100 index, have different approaches.

Graph and download economic data for Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) (TWEXO) from 1995-01-04 to 2020-01-01 about trade-weighted, trade, exchange rate, currency, goods, rate, indexes, and USA.

United States USD Trade Weighted Index: Real: Major Currencies data was reported at 104.556 Mar1973=100 in Oct 2018. This records an increase from the previous number of 103.639 Mar1973=100 for Sep 2018. United States USD Trade Weighted Index: Real: Major Currencies data is updated monthly, averaging 91.800 Mar1973=100 from Jan 1973 (Median) to Oct 2018, with 550 observations. The data reached an all-time high of 131.551 Mar1973=100 in Mar 1985 and a record low of 77.674 Mar1973=100 in Jul 2011. United States USD Trade Weighted Index: Real: Major Currencies data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.M016: US Dollar Trade Weighted Index.

The main stock market index of United States, the US500, rose to 6074 points on June 24, 2025, gaining 0.80% from the previous session. Over the past month, the index has climbed 2.57% and is up 11.05% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

Confidence indicators have been severely affected since the coronavirus (COVID-19) outbreak in Romania. The first substantial drops were reported in April 2020. By April 2023, however, both indicators started to recuperate, with economic sentiment index and employment expectations index recording values of 101 percent and 108 percent, respectively.

Monthly historical movements in output for services and their industry components, by chained volume indices of gross value added, UK.

United States USD Trade Weighted Index: Nominal: Major Currencies data was reported at 91.550 Mar1973=100 in Nov 2018. This records an increase from the previous number of 90.774 Mar1973=100 for Oct 2018. United States USD Trade Weighted Index: Nominal: Major Currencies data is updated monthly, averaging 92.981 Mar1973=100 from Jan 1973 (Median) to Nov 2018, with 551 observations. The data reached an all-time high of 143.906 Mar1973=100 in Mar 1985 and a record low of 69.061 Mar1973=100 in Aug 2011. United States USD Trade Weighted Index: Nominal: Major Currencies data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

Data was collected via ethnobotanical methods decribed in detail in our manuscript entitled "Most cultural importance indices do not predict species cultural keystone status."

Cyprus Index: CSE: Main Market data was reported at 197.140 03Sep2004=1000 in Apr 2025. This records a decrease from the previous number of 199.390 03Sep2004=1000 for Mar 2025. Cyprus Index: CSE: Main Market data is updated monthly, averaging 98.205 03Sep2004=1000 from Sep 2004 (Median) to Apr 2025, with 248 observations. The data reached an all-time high of 5,618.670 03Sep2004=1000 in Oct 2007 and a record low of 26.110 03Sep2004=1000 in Oct 2020. Cyprus Index: CSE: Main Market data remains active status in CEIC and is reported by Cyprus Stock Exchange. The data is categorized under Global Database’s Cyprus – Table CY.Z001: Cyprus Stock Exchange: Indices.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around 40 percent of their value compared to January 5, 2020. However, Asian markets and the NASDAQ Composite Index only shed around 20 to 25 percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around 65 percent higher than in January 2020, while most other markets were only between 20 and 40 percent higher.

Why did the NASDAQ recover the quickest?

Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide.

Which markets suffered the most?

The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

United States - Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) was 169.61710 Index Jan 1997=100 in January of 2020, according to the United States Federal Reserve. Historically, United States - Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) reached a record high of 174.62780 in September of 2019 and a record low of 89.48190 in January of 1995. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) - last updated from the United States Federal Reserve on June of 2025.

United States - Relative Importance Weights (Contribution to the Total Industrial Production Index): Total Index was 100.00% in May of 2025, according to the United States Federal Reserve. Historically, United States - Relative Importance Weights (Contribution to the Total Industrial Production Index): Total Index reached a record high of 100.00 in February of 1972 and a record low of 100.00 in February of 1972. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Relative Importance Weights (Contribution to the Total Industrial Production Index): Total Index - last updated from the United States Federal Reserve on June of 2025.

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes

Graph and download economic data for Nominal Major Currencies U.S. Dollar Index (Goods Only) (DISCONTINUED) (TWEXMMTH) from Jan 1973 to Dec 2019 about major, trade-weighted, exchange rate, currency, goods, rate, indexes, and USA.

United States - Nominal Other Important Trading Partners U.S. Dollar Index (Goods Only) (DISCONTINUED) was 169.08910 Index Jan 1997=100 in December of 2019, according to the United States Federal Reserve. Historically, United States - Nominal Other Important Trading Partners U.S. Dollar Index (Goods Only) (DISCONTINUED) reached a record high of 175.00650 in September of 2019 and a record low of 89.07870 in January of 1995. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Nominal Other Important Trading Partners U.S. Dollar Index (Goods Only) (DISCONTINUED) - last updated from the United States Federal Reserve on June of 2025.

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.

Explore the Index of Real Gross Domestic Product By Main Economic Activities (2018=100) for Saudi Arabia. Find information on Oil activities, non-oil activities, government activities, and more in this quarterly dataset.Follow data.kapsarc.org for timely data to advance energy economics research.Important notes:2022,2023,2024: Preliminary Data.The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.Data were revised from 1970 to 2009

Temperature is a key factor affecting the physiological development of field crops as well as crop yield and agricultural product quality achieved during the growing season. Crop responses to the temperature are characterized by three important cardinal temperature indices; the cardinal minimum temperature, maximum cardinal temperature, and optimum temperature for field crop production at which the plant growth and development can start, stop, and proceed at the maximum rate respectively. Agriculture is an important primary production sector in Canada. Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.