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.

This document was prepared to outline the methods used and citations for the calculation of the map layers considered to be the City of Vancouver's Equity Index, created by Kobel Solutions.

This table shows the price indices, quarterly and yearly changes in prices of services that companies provide. The figures are broken down by type of services according to the Classification of Products by Activity (CPA 2015 version 2.1). For some services, a further breakdown has been made on the basis of market data that differ from the CPA. This breakdown is indicated with a letter after the CPA-code.

The base year for all Services producer price indices is 2021. The year average, quarterly and yearly changes are calculated with unrounded figures.

Data available from: 4th quarter 2002.

Status of the figures: The figures for the most recent quarter are provisional. These figures are made definite in the publication for the subsequent quarter.

Changes as of November 14 2025: The provisional figures of the 3rd quarter 2025 are published for approximately half of the branches. All previous figures are made definite. For all other branches the figures of the 3rd quarter 2025 are available at a later date.

When will new figures be published? New figures are available twice per quarter. Halfway each quarter, the results of the pricing method Model pricing (around half of the branches) are published and the other branches with the Unit value method follow at the end of the quarter. This concerns the price development of the previous quarter. The Services producer price index of the total commercial services is also calculated and published at the end of each quarter.

The Services producer price indices publication schedule can be downloaded as an Excel file under section: 3 Relevant articles. More information about the pricing method can be found in the video under section: 3 Relevant articles.

Ecuador Consumer Confidence Index: Quito data was reported at 35.742 Point in Jun 2019. This records a decrease from the previous number of 36.325 Point for May 2019. Ecuador Consumer Confidence Index: Quito data is updated monthly, averaging 37.059 Point from Dec 2014 (Median) to Jun 2019, with 55 observations. The data reached an all-time high of 47.820 Point in Dec 2014 and a record low of 26.457 Point in May 2016. Ecuador Consumer Confidence Index: Quito data remains active status in CEIC and is reported by Central Bank of Ecuador. The data is categorized under Global Database’s Ecuador – Table EC.H005: Consumer Confidence Index: New Methodology.

Context

Data and methodology for the Big Mac index https://www.economist.com/news/2018/07/11/the-big-mac-index.

Updated yearly, with data from 1986 to 2019.

Content

• big-mac-raw-index.csv contains values for the “raw” index
• big-mac-adjusted-index.csv contains values for the “adjusted” index
• big-mac-full-index.csv contains both

Acknowledgements

Banner Photo by amirali mirhashemian on Unsplash

This software is published by The Economist under the MIT licence. The data generated by The Economist are available under the Creative Commons Attribution 4.0 International License.

The licences include only the data and the software authored by The Economist, and do not cover any Economist content or third-party data or content made available using the software. More information about licensing, syndication and the copyright of Economist content can be found here.

From https://github.com/TheEconomist/big-mac-data

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Time series data for the statistic Getting credit: Strength of legal rights index (0-10) (DB05-14 methodology) and country Romania.

US Census Annual Estimates of the Resident Population for Selected Age Groups by Sex for the United States. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Median age is calculated based on single year of age. For population estimates methodology statements, see http://www.census.gov/popest/methodology/index.html.

Prominent rent growth indices often give strikingly different measurements of rent inflation. We create new indices from Bureau of Labor Statistics (BLS) rent microdata using a repeat-rent index methodology and show that this discrepancy is almost entirely explained by differences in rent growth for new tenants relative to the average rent growth for all tenants. Rent inflation for new tenants leads the official BLS rent inflation by four quarters. As rent is the largest component of the consumer price index, this has implications for our understanding of aggregate inflation dynamics and guiding monetary policy. Download NTRR and ATRR indices through 2022q3 here.

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

United States RMI: South: Market Outlook Compared to Three Months Ago data was reported at 64.000 Point in Jun 2020. This records an increase from the previous number of 21.000 Point for Mar 2020. United States RMI: South: Market Outlook Compared to Three Months Ago data is updated quarterly, averaging 42.500 Point from Mar 2020 (Median) to Jun 2020, with 2 observations. The data reached an all-time high of 64.000 Point in Jun 2020 and a record low of 21.000 Point in Mar 2020. United States RMI: South: Market Outlook Compared to Three Months Ago data remains active status in CEIC and is reported by National Association of Home Builders. The data is categorized under Global Database’s United States – Table US.EB067: Remodelling Market Index (New Methodology).

Time series data for the statistic Getting credit: Strength of legal rights index (0-10) (DB05-14 methodology) - Score and country France.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Ecuador Consumer Confidence Index data was reported at 36.626 Point in Oct 2025. This records a decrease from the previous number of 37.217 Point for Sep 2025. Ecuador Consumer Confidence Index data is updated monthly, averaging 54.810 Point from Dec 2014 (Median) to Oct 2025, with 128 observations. The data reached an all-time high of 54.810 Point in Jun 2020 and a record low of 26.950 Point in Jul 2020. Ecuador Consumer Confidence Index data remains active status in CEIC and is reported by Central Bank of Ecuador. The data is categorized under Global Database’s Ecuador – Table EC.H: Consumer Confidence Index: New Methodology. Data gap for March, April and May 2020 was due to National Institute of Statistics and Census (INEC) wasn’t able to conduct interviews at the stage of a national health emergency in the country. Data for June 2020 corresponds to the data collected through phone calls from the shortened questionnaire of Consumer Confidence Index for the months of May and June 2020 due to the stage of national health emergency in the country. Data discrepancy between June and September was due to difference in data collection method. June 2020 data was collected through phone calls while September 2020 data was collected mostly (98.9%) through face-to-face interviews and the remaining (1.1%) through telephone calls. [COVID-19-IMPACT]

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

Context

The Corruption Perceptions Index (CPI)(link is external) was established in 1995 by Transparency International as a composite indicator used to measure perceptions of corruption in the public sector in different countries around the world. During the past 20 years, both the sources used to compile the index and the methodology has been adjusted and refined. The most recent review process took place in 2012 , and some important changes were made to the methodology in 2012(link is external). The method that was used up until 2012 to aggregate different data sources has been simplified and now includes just one year’s data from each data source. Crucially, this method now allows us to compare scores over time, which was not methodologically possible prior to 2012. The methodology follows 4 basic steps: selection of source data, rescaling source data, aggregating the rescaled data and then reporting a measure for uncertainty. Given the changes in the 2012 methodology, this dataset contains two indicators (CPI score and country ranking) for both the old methodology (up to 2011) and the new one (from 2012 onwards), totalling the following four indicators:

1. Corruption Perception Index - Score - New Method (from 2012 onwards - scale from 0 to 100, where 0 re presents very high levels of perceived corruption)
2. CPI score - Old methodology (from 1995 to 2011 - scale from 0 to 10, where 0 represents very high levels of perceived corruption)
3. CPI country ranking - New methodology (from 2012 onwards)
4. CPI country ranking - Old methodology (from 1995 to 2011)

What do the numbers indicate?

The indicator ranges from 0 (Very high level of perceived corruption) to 100 (very low level of perceived corruption.

Original Data

Acknowlegement

Foto von Elimende Inagella auf Unsplash

Time series data for the statistic Getting credit: Strength of legal rights index (0-10) (DB05-14 methodology) and country Liberia.

Logistics performance index: Efficiency of customs clearance process (1=low to 5=high) in Morocco was reported at 2.33 1=low to 5=high in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Morocco - Logistics performance index: Efficiency of customs clearance process (1=low to 5=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

The Computer Mediated Government Transparency Index (T-index) provides an assessment of government transparency based on different kinds of public information services that governments offer to their citizens, encompassing both de jure (signature of treaties and implementation of laws) and de facto (actual availability of data online) aspects of transparency. This index is an important tool for governments and civic actors around the world. Items missing on the index are a guide for transparency advocates.

Read more on the T-index Methodology.

Ecuador Consumer Confidence Index: Quito: Present Situation data was reported at 34.707 Point in Jun 2019. This records a decrease from the previous number of 35.322 Point for May 2019. Ecuador Consumer Confidence Index: Quito: Present Situation data is updated monthly, averaging 36.255 Point from Dec 2014 (Median) to Jun 2019, with 55 observations. The data reached an all-time high of 46.305 Point in Dec 2014 and a record low of 24.643 Point in May 2016. Ecuador Consumer Confidence Index: Quito: Present Situation data remains active status in CEIC and is reported by Central Bank of Ecuador. The data is categorized under Global Database’s Ecuador – Table EC.H005: Consumer Confidence Index: New Methodology.