Crowdsourced price data from 15 pilot countries, namely, Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam; from December 2015 to August 2016 and covering 162 household good and service items. This database is a repository of information collected during a World Bank pilot study on the feasibility of crowdsourced price data collection utilizing modern information and communication technologies. The collected data can be used for a variety of spatial and temporal price studies and other price-related applications. The data was collected by leveraging a privately-operated network of paid on-the-ground contributors that had access to a smartphone application. Price collection tasks and related guidance was pushed through the application to specific geographical locations. The contributors carried out the requested collection tasks and submitted price data and other metadata using the application. The pilot was conducted in 15 pilot countries, namely, Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam from December 2015 to August 2016. The collected price data covers 162 tightly specified household good and service items, including food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine household maintenance; health; transport; communication; recreation and culture; education; restaurants and hotels; and miscellaneous goods and services. In total, the database includes 1,262,458 price observations, ranging from 196,188 observations for Brazil to 14,102 observations for Cambodia. The observations are accompanied by a rich set of metadata, including longitude and latitude coordinates and related geographical designations, time-stamps, outlet identifiers, volume and weight details, and brand and model information. Due to the pilot nature of this data, the survey coverage varies between and within countries. In addition, the comparability of price data for goods is typically more reliable than those for services. This database is a product of the World Bank Development Data Group. Use is subject to World Bank policies and procedures on access to information. Site-specific terms of use apply and are stated below.

This data set contains consumer-level prices for a sample of retail markets in Honolulu for 2016. Data include weekly prices for fish species prevalent in Honolulu retail seafood markets. Additionally, each record contains information on the product form, origin of the fish (if known), labeling schemes, quality (where applicable), and the use of preservation methods (such as CO-treatment).

This database contains a time series of consumer-level prices for a sample of retail markets in Honolulu between 2007-2011. Data include weekly prices for fish species prevalent in Honolulu retail seafood markets. Additionally, each record contains information on the product form, origin of the fish (if known), labelling schemes, quality (where applicable), and the use of preservation methods (such as CO-treatment).

Abstract

This is a panel dataset of monthly price data from a selected sample of rural markets in Rwanda, with data points starting in January 2017 and ending in November 2020.

Geographic coverage

The dataset includes markets in 21 districts of Rwanda, covering all 4 provinces.

Analysis unit

Product (one observation per product per month per market).

Kind of data

Sample survey data [ssd]

Sampling procedure

Markets were purposively sampled, based on proximity to roads prioritized for rehabilitation by the Government of Rwanda.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

2-3 price points were taken per product at each market per month. In order to deal with outliers, prices were logged and averaged to get mean monthly prices, then exponentiated to be interpretable. Where a product was not available, there is no price data. However, there are cases where a product was available but price data was not available.

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

Abstract copyright UK Data Service and data collection copyright owner.

The Prices Survey Microdata include the underlying price data used by the Office for National Statistics (ONS) to produce the Consumer Prices Index (CPI), the Retail Prices Index (RPI) and associated price indices. The CPI has become the main domestic measure of inflation for macroeconomic purposes in the UK. Since December 2003 it has been used for the inflation target that the Bank of England is required to achieve. The RPI is the most long-standing measure of inflation in the UK, and its uses have included the indexation of pensions, state benefits and index-linked gilts. The study also includes the data underlying the Producer Prices Index.

There are four levels of sampling for local price collection: locations/shopping areas; outlets/shops within locations; representative items/goods and services; and products and varieties (price quotes).

There are two basic price collection methods: local and central. Local collection is used for most items; prices are obtained from outlets in about 150 locations around the country. Some 110,000 quotations are obtained by this method. Normally, collectors must visit the outlet, but prices for some items may be collected by telephone. Central collection is used for items where all the prices can be collected centrally by the ONS with no field work. These prices can be further sub-divided into two categories, depending on their subsequent use: 1) central shops, where the prices are combined with prices obtained locally, and 2) central items, where the prices are used on their own to construct centrally calculated indices. There are about 130 items for which the prices are collected centrally.

The retail price data include the locations containing the shopping outlets from which the price quotes were obtained. These locations are intended to be broadly representative of a central shopping area and the areas where the local shopping population tend to live. The data also include the regions in which those shopping areas are located.

Linking to other business studies
The producer prices data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest edition information
For the thirty-fifth edition (May 2024), monthly Item Indices and Price Quotes data files for January to March 2024 have been added to the study.

Main Topics:

The Prices Survey Microdata include both retail and producer prices. The retail data include the following files:

• 'backdata' or background information files
• locally collected files
• centrally collected item files
• item indices files
  
• price quote files
• Classification of Individual Consumption by Purpose (COICOP) level maps and weights files
  

The 'backdata' background information files include:

• COICOP descriptions and identification codes
• descriptions and identification codes for each item (goods and services)
• location descriptions and identification codes (and the region of the UK)
• shop codes for each item and location

The pre-2007 data also include postcodes for the shops.

The retail prices data span from 1996 to 2009 (centrally collected item indices), from 1996 to 2013 (annual item indices), from 1st quarter 1996 to 3rd quarter 2016 (quarterly item indices and quarterly price quote data), from October 2016 to July 2023 (monthly item indices and monthly price quote data), and from 1996 to 2013 (locally collected data).

The producer prices files span from 1998 to 2021 and include:

• item and index number codes
• Inter-Departmental Business Register Reporting Unit reference numbers, allowing the data to be matched to other ONS business survey data
• index descriptions
• prices for each item/index number

Additional producer prices files spanning 1996 to 2019 provide only the Producer Prices Indices and summary tables.

In a group of 4, we were randomly assigned to one parking site where we randomly sampled 10 cars with transponders and 10 cars without transponders. This was completed 4 times at each parking site within a span of 2 weeks. The prices of the cars were determined after each visit using Kelley Blue Book.

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. 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 and estimations of non-available items' prices: Under each category, a number of common items are used in Palestine to calculate the price levels and to represent the commodity within the commodity group. Of course, it is

Israel IL: Consumer Price Index (CPI): Local Source Base Year: Clothing and Footwear data was reported at 90.467 2020=100 in 2022. This records a decrease from the previous number of 95.092 2020=100 for 2021. Israel IL: Consumer Price Index (CPI): Local Source Base Year: Clothing and Footwear data is updated yearly, averaging 119.871 2020=100 from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 167.358 2020=100 in 1997 and a record low of 53.850 2020=100 in 1985. Israel IL: Consumer Price Index (CPI): Local Source Base Year: Clothing and Footwear data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Israel – Table IL.OECD.MEI: Consumer Price Index: COICOP 1999: OECD Member: Annual. The CPI measures the change in prices which consumer pay for fixed market basket of consumption goods and services. Price coverage: Prices include applicable taxes (VAT) and fees on the products at the time of sale. Cash payments are the basis for the price survey. Monthly installment payment and credit card interest are excluded. Price collection procedure: The data collection methods are adapted according to the specific characteristics of the CPI classes. The main price surveys are: Computer Assisted Telephone Interviews (CATI), conducted by the CBS staff at the central office; Computer Assisted Personal Interviews (CAPI) by field collectors with handheld personal computers (HPC) and Direct Data Entry (DDE) into the database. Also for some special items Internet is used either in parallel with CAPI or as a part of DDE collection. The CPI includes a measure of rented housing Owner Occupied Housing (OOH) is included in the CPI and is calculated using rental equivalent method. The method for imputation of OOH is based on stratified average prices of contracts that are subject to renewal. In order to reduce variance in the monthly series, two month moving averages are compared each month. However, the method for OOH still leaves room for quality differences to play role in month-to-month average price changes. The method relies on successful stratification of apartments to groups whose relative price changes are as similar as possible. While the stratification is based on apartment location and number of rooms, some quality characteristics may experience month-to-month variation. Treatment of own account production is not included Goods and services sold illegally, second hand goods, goods and services partially or totally subsidized by the government and financial transactions are not included. Insurance: Insurance of personal transport and Health insurance (private and provided by the Government) are included. Treatment of missing items: Price changes for missing observations are imputed based on the price movements of other observations of the same item. Selection of replacement items: Products that become permantely unavailable are replaced in the sample and enumerators select a replacement possessing as many of the same quality characteristics as possible. Prices from previous period are sought for the replacement item for linking purpose. Treatment of quality change: There are two types of replacement approach: comparable and non-comparable. If a new product possesses the previously defined important characteristics of the old product, the new product is defined as comparable and a minor quality change is regarded as price change. Otherwise, if a significant quality change is introduced, the new product is defined as not comparable. The breakage in price series is treated by the linking method. Explicit quality adjustments are usually not performed. Hedonic methods are being considered but not yet implemented. In some cases, where the product cycle is short and new versions with improved quality characteristics are frequently introduced, the overlap method may give biased estimates. Introduction of new products: New items are introduced when the market basket is updated. New products are introduced into the sample as they gain significant market share. Business and professional periodicles are closely followed to gain information on new products that are gaining consumer demand. Seasonal items: Missing prices for seasonal products are imputed. Certain procedures are in place to avoid too early reintroduction of seasonal products back to the index. For price changes a bridge method is used when the items are reintroduced to the collection. Index series are also calculated and released in seasonally adjusted form.; Index series starts in November 1985

Data Collection Software Market Outlook

The global data collection software market size is anticipated to significantly expand from USD 1.8 billion in 2023 to USD 4.2 billion by 2032, exhibiting a CAGR of 10.1% during the forecast period. This remarkable growth is fueled by the increasing demand for data-driven decision-making solutions across various industries. As organizations continue to recognize the strategic value of harnessing vast amounts of data, the need for sophisticated data collection tools becomes more pressing. The growing integration of artificial intelligence and machine learning within software solutions is also a critical factor propelling the market forward, enabling more accurate and real-time data insights.

One major growth factor for the data collection software market is the rising importance of real-time analytics. In an era where time-sensitive decisions can define business success, the capability to gather and analyze data in real-time is invaluable. This trend is particularly evident in sectors like healthcare, where prompt data collection can impact patient care, and in retail, where immediate insights into consumer behavior can enhance customer experience and drive sales. Additionally, the proliferation of the Internet of Things (IoT) has further accelerated the demand for data collection software, as connected devices produce a continuous stream of data that organizations must manage efficiently.

The digital transformation sweeping across industries is another crucial driver of market growth. As businesses endeavor to modernize their operations and customer interactions, there is a heightened demand for robust data collection solutions that can seamlessly integrate with existing systems and infrastructure. Companies are increasingly investing in cloud-based data collection software to improve scalability, flexibility, and accessibility. This shift towards cloud solutions is not only enabling organizations to reduce IT costs but also to enhance collaboration by making data more readily available across different departments and geographies.

The intensified focus on regulatory compliance and data protection is also shaping the data collection software market. With the introduction of stringent data privacy regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are compelled to adopt data collection practices that ensure compliance and protect customer information. This necessitates the use of sophisticated software capable of managing data responsibly and transparently, thereby fueling market growth. Moreover, the increasing awareness among businesses about the potential financial and reputational risks associated with data breaches is prompting the adoption of secure data collection solutions.

Component Analysis

The data collection software market can be segmented into software and services, each playing a pivotal role in the ecosystem. The software component remains the bedrock of this market, providing the essential tools and platforms that enable organizations to collect, store, and analyze data effectively. The software solutions offered vary in complexity and functionality, catering to different organizational needs ranging from basic data entry applications to advanced analytics platforms that incorporate AI and machine learning capabilities. The demand for such sophisticated solutions is on the rise as organizations seek to harness data not just for operational purposes but for strategic insights as well.

The services segment encompasses various offerings that support the deployment and optimization of data collection software. These services include consulting, implementation, training, and maintenance, all crucial for ensuring that the software operates efficiently and meets the evolving needs of the user. As the market evolves, there is an increasing emphasis on offering customized services that address specific industry requirements, thereby enhancing the overall value proposition for clients. The services segment is expected to grow steadily as businesses continue to seek external expertise to complement their internal capabilities, particularly in areas such as data analytics and cybersecurity.

Integration services have become particularly important as organizations strive to create seamless workflows that incorporate new data collection solutions with existing IT infrastructure. This need for integration is driven by the growing complexity of enterprise IT environments, where disparate systems and applications must wo

Consumer price indexes (CPIs) are index numbers that measure changes in the prices of goods and services purchased or otherwise acquired by households, which households use directly, or indirectly, to satisfy their own needs and wants. In practice, most CPIs are calculated as weighted averages of the percentage price changes for a specified set, or ‘‘basket’’, of consumer products, the weights reflecting their relative importance in household consumption in some period. CPIs are widely used to index pensions and social security benefits. CPIs are also used to index other payments, such as interest payments or rents, or the prices of bonds. CPIs are also commonly used as a proxy for the general rate of inflation, even though they measure only consumer inflation. They are used by some governments or central banks to set inflation targets for purposes of monetary policy. The price data collected for CPI purposes can also be used to compile other indices, such as the price indices used to deflate household consumption expenditures in national accounts, or the purchasing power parities used to compare real levels of consumption in different countries.

In an effort to further coordinate and harmonize the collection of CPI data, the international organizations agreed that the International Monetary Fund (IMF) and the Organisation for Economic Cooperation and Development (OECD) would assume responsibility for the international collection and dissemination of national CPI data. Under this data collection initiative, countries are reporting the aggregate all items index; more detailed indexes and weights for 12 subgroups of consumption expenditure (according to the so-called COICOP-classification), and detailed metadata. These detailed data represent a valuable resource for data users throughout the world and this portal would not be possible without the ongoing cooperation of all reporting countries. In this effort, the OECD collects and validates the data for their member countries, including accession and key partner countries, whereas the IMF takes care of the collection of data for all other countries.

Graph and download economic data for Producer Price Index by Commodity: Waste Collection and Remediation Services (Partial): Solid Waste Collection (WPU501101) from Dec 2008 to Jun 2025 about waste, collection, services, commodities, PPI, inflation, price index, indexes, price, and USA.

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. 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 and estimations of non-available items' prices: Under each category, a number of common items are used in Palestine to calculate the price levels and to represent the commodity within the commodity group. Of course, it is

Indonesia ID: Consumer Price Index (CPI): Local Source Base Year: Restaurants and Hotels data was reported at 117.777 2018=100 in Dec 2023. This records an increase from the previous number of 117.453 2018=100 for Sep 2023. Indonesia ID: Consumer Price Index (CPI): Local Source Base Year: Restaurants and Hotels data is updated quarterly, averaging 110.810 2018=100 from Mar 2020 (Median) to Dec 2023, with 16 observations. The data reached an all-time high of 117.777 2018=100 in Dec 2023 and a record low of 105.668 2018=100 in Mar 2020. Indonesia ID: Consumer Price Index (CPI): Local Source Base Year: Restaurants and Hotels data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Indonesia – Table ID.OECD.MEI: Consumer Price Index: COICOP 1999: Non OECD Member: Quarterly. The CPI measures the average change in prices between times of a package of goods and services consumed by the population/households in a certain base period. Price coverage: Prices includes sales taxes. Price collection method: Price data of each commodity is obtained from 3 or 4 outlets, which are visited by data collector (direct interview). Frequency of data collection: The frequencies of price data collection are different from one item to another depending on the characteristic of the items as follows: - Rice price data collection in Jakarta is daily - For several items popularly known as basic necessities, price data are collected weekly on Monday and Tuesday. - For some food items, price data are collected every two weeks on Wednesday and Thursday of the first week and the third week. - For other food items, processed food, drinks, cigarettes and tobacco, price data are collected monthly on Tuesday close to day 15 during 3 days (Tuesday, Wednesday and Thursday). - For durable goods price data are collected monthly on day 5 to day 15. Services price data are collected monthly on day 1 to day 10. - House rents data are collected monthly on day 1 to 10. - Domestic servant and baby sitter salaries are observed monthly on day 1 to 10. - Data related to the education expenses are collected monthly on day 1 to 10. A measure of owner occupied housing is not included Treatment of missing prices: In case of missing prices, the prices are treated by using the pairing price data. When in certain cases, the item for the CPI is replaced, the procedure of replacement is choosing the similar quality items and then ask the current and previous prices. Treatment of quality changes: Adjustment for quality differences is never done. Introduction of new products: never considered. Treatment of seasonal products: never considered. Seasonally adjustment: Data are not seasonally adjusted.; Index series starts in January 2020

Brochure Theme: A0 – Analysis and studies – General Under Theme: A000.01 – Statistical studies

Israel IL: Consumer Price Index (CPI): Local Source Base Year: Transport data was reported at 114.000 2020=100 in Dec 2022. This records a decrease from the previous number of 114.600 2020=100 for Sep 2022. Israel IL: Consumer Price Index (CPI): Local Source Base Year: Transport data is updated quarterly, averaging 93.883 2020=100 from Mar 1985 (Median) to Dec 2022, with 152 observations. The data reached an all-time high of 114.600 2020=100 in Sep 2022 and a record low of 6.700 2020=100 in Mar 1985. Israel IL: Consumer Price Index (CPI): Local Source Base Year: Transport data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Israel – Table IL.OECD.MEI: Consumer Price Index: COICOP 1999: OECD Member: Quarterly. The CPI measures the change in prices which consumer pay for fixed market basket of consumption goods and services. Price coverage: Prices include applicable taxes (VAT) and fees on the products at the time of sale. Cash payments are the basis for the price survey. Monthly installment payment and credit card interest are excluded. Price collection procedure: The data collection methods are adapted according to the specific characteristics of the CPI classes. The main price surveys are: Computer Assisted Telephone Interviews (CATI), conducted by the CBS staff at the central office; Computer Assisted Personal Interviews (CAPI) by field collectors with handheld personal computers (HPC) and Direct Data Entry (DDE) into the database. Also for some special items Internet is used either in parallel with CAPI or as a part of DDE collection. The CPI includes a measure of rented housing Owner Occupied Housing (OOH) is included in the CPI and is calculated using rental equivalent method. The method for imputation of OOH is based on stratified average prices of contracts that are subject to renewal. In order to reduce variance in the monthly series, two month moving averages are compared each month. However, the method for OOH still leaves room for quality differences to play role in month-to-month average price changes. The method relies on successful stratification of apartments to groups whose relative price changes are as similar as possible. While the stratification is based on apartment location and number of rooms, some quality characteristics may experience month-to-month variation. Treatment of own account production is not included Goods and services sold illegally, second hand goods, goods and services partially or totally subsidized by the government and financial transactions are not included. Insurance: Insurance of personal transport and Health insurance (private and provided by the Government) are included. Treatment of missing items: Price changes for missing observations are imputed based on the price movements of other observations of the same item. Selection of replacement items: Products that become permantely unavailable are replaced in the sample and enumerators select a replacement possessing as many of the same quality characteristics as possible. Prices from previous period are sought for the replacement item for linking purpose. Treatment of quality change: There are two types of replacement approach: comparable and non-comparable. If a new product possesses the previously defined important characteristics of the old product, the new product is defined as comparable and a minor quality change is regarded as price change. Otherwise, if a significant quality change is introduced, the new product is defined as not comparable. The breakage in price series is treated by the linking method. Explicit quality adjustments are usually not performed. Hedonic methods are being considered but not yet implemented. In some cases, where the product cycle is short and new versions with improved quality characteristics are frequently introduced, the overlap method may give biased estimates. Introduction of new products: New items are introduced when the market basket is updated. New products are introduced into the sample as they gain significant market share. Business and professional periodicles are closely followed to gain information on new products that are gaining consumer demand. Seasonal items: Missing prices for seasonal products are imputed. Certain procedures are in place to avoid too early reintroduction of seasonal products back to the index. For price changes a bridge method is used when the items are reintroduced to the collection. Index series are also calculated and released in seasonally adjusted form.; Index series starts in November 1985

United States - Producer Price Index by Commodity: Waste Collection and Remediation Services (Partial) was 184.72900 Index Dec 2008=100 in June of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Waste Collection and Remediation Services (Partial) reached a record high of 191.21600 in March of 2025 and a record low of 100.00000 in December of 2008. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Waste Collection and Remediation Services (Partial) - last updated from the United States Federal Reserve on July of 2025.

This statistic shows the U.S. Bureau of Labor Statistics' national price index for garbage and trash collection in the United States from 2000 to 2018. In 2018, the index was *****, compared to 100 in 1983.

United States - Consumer Price Index for All Urban Consumers: Water and Sewer and Trash Collection Services in U.S. City Average was 316.85100 Index Dec 1997=100 in June of 2025, according to the United States Federal Reserve. Historically, United States - Consumer Price Index for All Urban Consumers: Water and Sewer and Trash Collection Services in U.S. City Average reached a record high of 316.85100 in June of 2025 and a record low of 100.10000 in December of 1997. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index for All Urban Consumers: Water and Sewer and Trash Collection Services in U.S. City Average - last updated from the United States Federal Reserve on July of 2025.

The World Bank Pilot Study for Crowd-Sourced Price Data Collection through Mobile Phones combined the need for high-frequency data, recent developments in information and communication technologies, and power of crowd. Crowd-sourced data are data collected and reported by the user community using information and communication technologies. The objective of the pilot was to study the feasibility of crowd-sourced price data collection. Non-professional price collectors used personal computers and mobile phones for collecting data and entering it in a multilingual web microsite developed for the pilot. Price data was collected for thirty tightly specified food commodity items on a monthly basis for approximately six months in eight pilot countries. Non-professional price collectors received compensation in the form of airtime rewards.