Purpose and brief description The consumer price index is an economic indicator whose main task is to objectively reflect the price evolution over time for a basket of goods and services purchased by households and considered representative of their consumer habits. The index does not necessarily measure the price level of this basket for a specific period of time, but rather the fluctuation between two periods, the first one acting as basis for comparison. Moreover, this difference in the price level is not measured in absolute, but in relative terms. The consumer price index can be determined as a hundred times the ratio between the observed prices of a range of goods and services at a given time and the prices of the same goods and services, observed under the same circumstances during the reference period, chosen as basis for comparison. Price observations always take place in the same regions. Since 2014, the consumer price index has been a chain index in which the weighting reference period is regularly shifted and prices and quantities are no longer compared between the current period and a fixed reference period, but the current period is compared with an intermediate period. By multiplying these short-term indices, and so creating a chain, we get a long-term series with a fixed reference period. Population Belgian private households Data collection method and possible sampling Survey technique applied using a computer, based on the use of electronic questionnaires and laptops. Frequency Monthly. Timing of publication The results are available on the penultimate working day of the reference period. Definitions Weight (CPI): The weight represents the importance of the goods and services included in the CPI in the total expenditure patterns of the households. Weights are determined based on the household budget survey. Consumer price index (CPI): The consumer price index is an economic indicator whose main task is to objectively reflect the price evolution over time for a basket of goods and services purchased by households and considered representative of their consumer habits. Health index: The health index is derived from the consumer price index and has been published since January 1994. The current value of this index is determined by removing a number of products from the consumer price index product basket, in particular alcoholic beverages (bought in a shop or consumed in a bar), tobacco products and motor fuels except for LPG. Inflation: Inflation is defined as the ratio between the value of the consumer price index of a given month and the index of the same month the year before. Therefore, inflation measures the rhythm of the evolution of the overall price level. Consumer price index without petroleum products: This index is calculated by removing the following products from the consumer price index: butane, propane, liquid fuels and motor fuels. Consumer price index without energy products: This index is calculated by removing the following products from the consumer price index: electricity, natural gas, butane, propane, liquid fuels, solid fuels and motor fuels. Smoothed index: The smoothed health index, also called smoothed index (the average value of the health indexes of the last 4 months) is used as a basis for the indexation of retirement pensions, social security benefits and some salaries and wages. Public wages and social benefits are indexed as soon as the smoothed index reaches a given value, called the central index. The smoothed index is also called moving average. In order to perform a 2% index jump (laid down in the Law of 23 April 2015 on employment promotion), the smoothed health index has been temporarily blocked at its value of March 2015 (100.66). The smoothed health index was then reduced by 2% from April 2015. When the reduced smoothed health index (also called the reference index) had increased again by 2% or in other words when it had exceeded the value of 100.66, the index was no longer blocked. It occurred in April 2016. Since April 2016 the smoothed health index is calculated in the same manner as the reference index and therefore corresponds to the arithmetical mean of the health indexes of the last 4 months multiplied by a factor of 0.98. The central index is a predetermined threshold value against which the smoothed health index is compared. If the central index is reached or exceeded, there is an indexation of the wages and salaries or benefits. This indexation is proportional to the percentage between the old and the new central index. For the public sector and social benefits, the difference between the central indices always amounts to 2 %. Therefore, a 2 % indexation is applied every time the central index is reached. There are also collective labour agreements according to which the difference between the central indices amounts to 1 % or 1.5 %. The reaching of a central index then leads to an indexation of 1 % or 1,5 %. See also: https://bosa.belgium.

Abstract

The survey was conducted in Ethiopia between July 2011 and July 2012 as part of the Africa Enterprise Survey 2011 rollout, an initiative of the World Bank. Data from 644 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Ethiopia was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry and one service as defined in the sampling manual. The manufacturing industry had a target of 340 interviews and service industry had a target of 240 interviews.

Size stratification was defined following the standardized definition for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in five regions (city and the surrounding business area): Addis Ababa, Oromya, SNNPR, Amhara, and Tigray.

For the Ethiopia ES, three sample frames were used. The first sample frame was produced by Ethiopia Ministry of Trade and Industry. A copy of that frame was sent to the TNS statistical team in London to select the establishments for interview. However, the quality of the sample frames was not optimal and additional sample frames were acquired during the implementation of the survey in order to reach the target number of interviews. The second sample frame used was the Dun & Bradstreet (D&B) database and the third sample frame was the Ethiopia Yellow Pages 2011.

The enumerated establishments with five or more employees were then used as the sample frame for the Ethiopia Enterprise Survey with the aim of obtaining interviews at 600 establishments.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of noneligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone or fax numbers so the local contractor had to screen the contacts by visiting them. Due to response rate and ineligibility issues, additional sample had to be extracted by the World Bank in order to obtain enough eligible contacts and meet the sample targets.

Given the impact that non-eligible units included in the sample universe may have on results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 21% (392 out of 1,873 establishments) and 12% (37 out of 310 establishments) for the ES firms for the Ministry of Trade and D&B sample frames respectively. The non-eligibility rate for the Yellow Pages sample frame was 16% (98 out of 607 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire [ISIC Rev.3.1: 15-37] - Services Module Questionnaire [ISIC Rev.3.1: 45, 50, 51, 52, 60, 61, 62, 63, 64 & 72] - Screener Questionnaire.

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times, days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.16, 0.38, and 0.36 for formal ES firms using the sample frames from the Ministry of Industry and Trade, D&B, and Yellow Pages respectively. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.06, 0.05 and 0.007 using the sample frames from the Ministry of Industry and Trade, D&B, and Yellow Pages respectively.

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Ethiopia ES 2011 Implementation" in Technical Documents.

What is the Survey of Economic Conditions? Contacts located in the Seventh Federal Reserve District are asked to rate various aspects of economic conditions along a seven-point scale ranging from "large increase" to "large decrease." A series of diffusion indexes summarizing the distribution of responses is then calculated.

How are the indexes constructed? Respondents' answers on the seven-point scale are assigned a numeric value ranging from +3 to –3. Each diffusion index is calculated as the difference between the number of respondents with answers above their respective average responses and the number of respondents with answers below their respective average responses, divided by the total number of respondents. The index is then multiplied by 100 so that it ranges from +100 to −100 and will be +100 if every respondent provides an above-average answer and –100 if every respondent provides a below-average answer. Respondents with no prior history of responses are excluded from the calculation.

What do the numbers mean? Respondents' respective average answers to a question can be interpreted as representing their historical trends, or long-run averages. Thus, zero index values indicate, on balance, average growth (or a neutral outlook) for activity, hiring, capital spending, and cost pressures. Positive index values indicate above-average growth (or an optimistic outlook) on balance, and negative values indicate below-average growth (or a pessimistic outlook) on balance.

Beginning with the May 12, 2020 release, the CFSEC moved to a monthly release schedule. This release, with data for April 2020, now contains estimated monthly historical values for the CFSEC indexes, as will all future releases. For additional information on how the survey and indexes changed, see the CFSEC FAQs available here (https://www.chicagofed.org/research/data/cfsec/current-data).

Prior to April 2022, the Chicago Fed Survey of Economic Conditions was named the Chicago Fed Survey of Business Conditions (CFSBC). The name change was made to better represent the survey’s aim and base of respondents. The goal of the survey is to assess the state of the economy in the Seventh Federal Reserve District. Moreover, since the beginning of the survey, it was been filled out by both business and nonbusiness contacts.

From 1994 onwards The consumer price index, which takes into account price trends of all goods and services, forms the basis of another index: the health index. This health index has been calculated since January 1994 (introduced by the Royal Decree of 24 December 1993). The value of this index is determined by removing a number of products from the consumer price index product basket, in particular alcoholic beverages (bought in a shop or consumed in a bar), tobacco products and motor fuels except for LPG. What is the purpose of the health index? The health index is used for the indexation of housing rents. The health index is defined in the Law of 23 April 2015 on employment promotion (Belgian Official Journal of 27 April 2015). The smoothed health index, also called smoothed index (the average value of the health indexes of the last 4 months) is used as a basis for the indexation of retirement pensions, social security benefits and some salaries and wages. Public wages and social benefits are indexed as soon as the smoothed index reaches a given value, called the central index. The smoothed index is also called moving average. In order to perform a 2% index jump (laid down in the Law of 23 April 2015 on employment promotion), the smoothed health index has been temporarily blocked at its value of March 2015 (100.66). The smoothed health index was then reduced by 2% from April 2015. When the reduced smoothed health index (also called the reference index) had increased again by 2% or in other words when it had exceeded the value of 100.66, the index was no longer blocked. It occurred in April 2016. Since April 2016 the smoothed health index is calculated in the same manner as the reference index and therefore corresponds to the arithmetical mean of the health indexes of the last 4 months multiplied by a factor of 0.98. More information All health indices from 1994 onward can be found through our index-search. Simply enter the desired year and month to view the consumer price index and the health index on all available bases. Health index: list of excluded products (PDF, 83.29 Kb) More information on the index link to public services wages and pensions can be found on the website of the Wages Service of the FPS Finances:https://persopoint.be/fr/services/administration-des-salaires/principes-generaux-de-l-index. The table below shows the health index of the 13 most recent available months. be.STAT allows you to search from 1994 onward. Purpose and brief description The health index was introduced in January 1994 (by Royal Decree of 24 December 1993 implementing the law of 6 January 1989 on the safeguarding of the country's competitiveness) and is derived from the consumer price index. The value of the health index is obtained by excluding a number of products from the product and service basket from the consumer price index, in particular alcoholic beverages (bought in the shop or consumed in a café), tobacco products and motor fuels (with the exception of LPG). Population Belgian private households Frequency Monthly Timing of publication The results are available on the penultimate working day of the reference period Remarks Remark Confidentiality consumer price indices - Although the headings are published in the index and are therefore generally known, the exact definition of the goods and services is kept secret. This confidentiality is required to prevent attempts to manipulate the index, by resolute actions on certain goods and services. The confidentiality of the definitions guarantees the index objectiveness. Metadata Consumer price index - Health index.pdf

Canada Consumer Price Index (CPI): Core: Trimmed Mean: sa: YoY data was reported at 2.800 % in Mar 2025. This records a decrease from the previous number of 2.900 % for Feb 2025. Canada Consumer Price Index (CPI): Core: Trimmed Mean: sa: YoY data is updated monthly, averaging 1.800 % from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 5.700 % in Jun 2022 and a record low of 0.800 % in Dec 1997. Canada Consumer Price Index (CPI): Core: Trimmed Mean: sa: YoY data remains active status in CEIC and is reported by Bank of Canada. The data is categorized under Global Database’s Canada – Table CA.I009: Core Inflation Index.

Description: From an economic point of view the production encompasses manufacturing, including related ‘industrial services’ as long as they are provided in the production industry. After the guidelines of the official statistics on the measurement of production, all products produced to be sold including repair works, montages and contract processing should be captured. Own consumption and wage work is included. For the calculation of the production indices the primary used data are the monthly production surveys. For this surveys reports of chosen local units of enterprises in the production, in the mining sector and extraction of stones and earth with 50 or more employees are used. Until 2006 the reporting threshold was fixed for 20 or more employees. The manufacturing trade is always included. The production index should demonstrate the development of the quantitative production of the production industry and its sub-areas in Germany, adjusted for chances in prices and structures to provide continuous data. Differences in size and changes in structures can be avoided, by presenting the production output not in total numbers, but in from of index number series orientated towards a basis year. For the calculation of production index numbers, current monthly production values (quantity of sales or sale values) are presented as a ratio of the monthly averages of the base year. Until 1993 the Federal Statistical Office calculated two types of production indices: gross-production indices and net-production indices. From the index system 1991=100 on there is only one production index, defined as e net production index. Both index types differ from one another among other things by the definition of the performance dimensions (value added or value of gross production) and by the way it is structured (net production index by economic sectors, gross production index by types of commodities). Indices of net production in the Federal Republic of Germany exist since 1950. During the past decades the base year changed several times and also the content wise classification economic sectors changed repeatedly trough the introduction of new classification systems. The series with different base years overlap, which gives the opportunity to calculate a continuous series with one single base, if the classification of economic sectors did not change in the entire period. Content-related interlinking of indices with different bases is controversial and the results can only be interpreted with care and under certain assumptions. The net production indices are also used to measure productivity in the production industry. Labor productivity (of a local unit, an enterprise, an economic sector or of the entire national economy) can be defined as the ratio of quantity of production and labor input in a certain period. Interpreting this coefficient, it is important to note that labor productivity also depends on the use of other production factors. The index for labor productivity is defined as the “production results per input component of the working volume”. Two different manifestations of the working volume are used for the calculation of the index: (1) hours of work by employees and (2) number of hours worked. Until 1994 in addition a distinction between “number of workers” and “number of employees” was made. The total national working productivity serves as an indicator for economic performance and competitiveness of an economic sector or of the entire national economy with regard to the entire labor input. Labor productivity (after the results of the national accounts) is apparently the most used productivity notion for the entire economy. It shows how effective the input labor is used in the production process. Anyway, it is important to note that the partial productivity indicator not only depends on the factor work but also on the endowment of a certain sector or the entire economy with machines and their degree of modernity and on the infrastructure, which also has an impact on the production result. Productivity can be measured regarding the following two aspects: production result per worker (per capita productivity) and production result per working hour (hourly productivity). For the entire national economy the labor productivity is measured as the ratio of the gross national product (in constant prices) and the average number of employees. To look at the development of labor productivity of an entire national economy, usua...

Description:From an economic point of view the production encompasses manufacturing, including related ‘industrial services’ as long as they are provided in the production industry. After the guidelines of the official statistics on the measurement of production, all products produced to be sold including repair works, montages and contract processing should be captured. Own consumption and wage work is included. For the calculation of the production indices the primary used data are the monthly production surveys. For this surveys reports of chosen local units of enterprises in the production, in the mining sector and extraction of stones and earth with 50 or more employees are used. Until 2006 the reporting threshold was fixed for 20 or more employees. The manufacturing trade is always included. The production index should demonstrate the development of the quantitative production of the production industry and its sub-areas in Germany, adjusted for chances in prices and structures to provide continuous data. Differences in size and changes in structures can be avoided, by presenting the production output not in total numbers, but in from of index number series orientated towards a basis year. For the calculation of production index numbers, current monthly production values (quantity of sales or sale values) are presented as a ratio of the monthly averages of the base year. Until 1993 the Federal Statistical Office calculated two types of production indices: gross-production indices and net-production indices. From the index system 1991=100 on there is only one production index, defined as e net production index. Both index types differ from one another among other things by the definition of the performance dimensions (value added or value of gross production) and by the way it is structured (net production index by economic sectors, gross production index by types of commodities). Indices of net production in the Federal Republic of Germany exist since 1950. During the past decades the base year changed several times and also the content wise classification economic sectors changed repeatedly trough the introduction of new classification systems. The series with different base years overlap, which gives the opportunity to calculate a continuous series with one single base, if the classification of economic sectors did not change in the entire period. Content-related interlinking of indices with different bases is controversial and the results can only be interpreted with care and under certain assumptions. The net production indices are also used to measure productivity in the production industry. Labor productivity (of a local unit, an enterprise, an economic sector or of the entire national economy) can be defined as the ratio of quantity of production and labor input in a certain period. Interpreting this coefficient, it is important to note that labor productivity also depends on the use of other production factors. The index for labor productivity is defined as the “production results per input component of the working volume”. Two different manifestations of the working volume are used for the calculation of the index: (1) hours of work by employees and (2) number of hours worked. Until 1994 in addition a distinction between “number of workers” and “number of employees” was made. The total national working productivity serves as an indicator for economic performance and competitiveness of an economic sector or of the entire national economy with regard to the entire labor input. Labor productivity (after the results of the national accounts) is apparently the most used productivity notion for the entire economy. It shows how effective the input labor is used in the production process. Anyway, it is important to note that the partial productivity indicator not only depends on the factor work but also on the endowment of a certain sector or the entire economy with machines and their degree of modernity and on the infrastructure, which also has an impact on the production result.Productivity can be measured regarding the following two aspects: production result per worker (per capita productivity) and production result per working hour (hourly productivity). For the entire national economy the labor productivity is measured as the ratio of the gross national product (in constant prices) and the average number of employees. To look at the development of labor productivity of an entire national economy, usually the real gross domestic product is used. When comparing economic sectors within a country, the added values of the economic sectors can be used in the respective prices with regard to one employee or one hour of work. Data tables in HISTAT:A. Index for the industrial net production A.01 Index for the industrial net production by industry groups, monthly data (1950-1994)A.02 Production index for the production industry (1991-2014) B. Index for the industrial gross production B.01 Index for t...

Redbook Index in the United States increased by 4.50 percent in the week ending June 21 of 2025 over the same week in the previous year. This dataset provides the latest reported value for - United States Redbook Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.

In January 2025, the unadjusted consumer price index (CPI) of all items for urban consumers in the United States amounted to about 317.67. The data represents U.S. city averages. The base period was 1982-84=100. The CPI is defined by the United States Bureau of Labor Statistics as “a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services”. The annual consumer price index for urban consumers in the U.S. can be accessed here. Consumer Price Index The Consumer Price Index (CPI) began in 1919 under the Bureau of Labor Statistics and is published every month. The CPI for all urban consumers includes urban households in Metropolitan Statistical Areas and regions with over 2,500 inhabitants, as well as non-farm consumers living in rural regions. This index was established in 1978 and includes about 80 percent of the U.S. population. The monthly CPI of urban consumers in the United States increased from 292.3 in May 2022 to 304.13 in 2023. Inflation tends not to impact everyone equally for a variety of reasons, including geography - CPI often differs between regions, with a high of 287.49 in the Western region as of 2021. There are also disparities in inflation between income quartiles, in which inflation is generally felt more heavily by lower income households. The annual CPI in the United States has increased steadily over the past two decades, from 140.3 in 1992 to 292.56 in 2022. A forecast of the CPI expects this positive trend to continue, reaching 325.6 by 2027. As of March 2023, the CPI of the nation’s education had increased by 3.5 percent. Further, in the same month costs of recreation, rent, housing, medical care, and food and beverages, gasoline, and transportation increased. Comparatively, the CPI in Hong Kong reached 103.3 in 2022.

In November 2024, the seasonally adjusted consumer price index for all urban consumers (CPI-U) in the United States increased *** percent from the previous month. The data represents city averages in the United States. The defined base period is: 1982-84=100. The CPI is defined by the United States Bureau of Labor Statistics as “a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services”.

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.

This paper investigates whether gold and silver can be considered safe havens by examining their long-run linkages with 13 stock price indices. More specifically, the stochastic properties of the differential between gold/silver prices and 13 stock indices are analysed applying fractional integration/cointegration methods to daily data, first for a sample from January 2010 until December 2019, then for one from January 2020 until June 2022 which includes the Covid-19 pandemic. The results can be summarised as follows. In the case of the pre-Covid-19 sample ending in December 2019, mean reversion is found for the gold price differential only vis-à-vis a single stock index (SP500). whilst in seven other cases, although the estimated value of d is below 1, the value 1 is inside the confidence interval and thus the unit root null hypothesis cannot be rejected. In the remaining cases the estimated values of d are significantly higher than 1. As for the silver differential, the upper bound is 1 only in two cases, whilst in the others mean reversion does not occur. Thus, the evidence is mixed on whether these precious metals can be seen as safe havens, though it appears that this property characterises gold in a slightly higher number of cases. By contrast, when using the sample starting in January 2020, the evidence in favour of gold and silver as possible safe havens is pretty conclusive since mean reversion is only found in a single case, namely that of the gold differential vis-à-vis the New Zealand stock index.

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

Characteristics of opioid analgesic dispensings, including mean number, mean time of opioid supply, mean number of prescribers, mean index of diversity of opioid prescribers (higher index = more prescribers and more variance in number of dispensings per prescriber), mean oral morphine equivalents per day during period of opioid dispensings, and co-dispensing of other medicines during period of opioid supply by treatment group (see methods for details).

The 20 countries with the highest number of citations in the Essential Science Indicators of the ISI Web of Knowledge. The x-index is calculated from the mean of the yearly values of N1 and N0.1 from 2003 to 2007, and the z-index is the x-index divided by the mean of the number of national articles in these years. The number of citations per paper is taken from the Essential Science Indicators in All Fields.

Total and average expenditure by number of employed members in the household in the last week and index of the mean average expenditure per person. National. total expenditure by number of employed members in the last week.

total and average expenditure by number of active members in the household in the last week and index on the mean of the average expenditure per person. National. Total expenditure by number of active members in the last week.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

Treatment strategies for preventing liver fibrosis have not yet been established. Letrozole, widely used for breast cancer, has recently been reported to suppress liver fibrosis in murine models. Therefore, we aimed to validate the suppressive effects of letrozole on liver fibrosis in the clinical setting. From 2006 to 2020, 23 consecutive patients who received continuous letrozole treatment for 24 months or more and had a liver fibrosis marker FIB-4 index of ≥ 2.30, were included. Forty-three patients who underwent anastrozole treatment for 24 months or more and had a liver fibrosis marker FIB-4 index of ≥ 2.30, were also included as controls. The Fisher exact, chi-square, unpaired Student t, and paired Student t test were used to analyze the data. The patient characteristics were similar between the letrozole- and anastrozole-treated patient groups. Among the letrozole-treated patients, the mean FIB-4 index tended to decline during letrozole treatment; a significant decrease was observed at 18 and 24 months compared with the baseline values (p = 0.044 and p = 0.013). In addition, the mean aspartate aminotransferase-to-platelet ratio index (APRI) decreased during letrozole treatment; the values at 18 and 24 months were significantly lower than those at baseline (p = 0.024 and p = 0.026). In contrast, among anastrozole-treated patients, the mean FIB-4 index and APRI did not change during anastrozole treatment. When changes in the FIB-4 index were further examined in a limited number of patients with a FIB-4 index ≥ 2.67, a significant reduction in the FIB-4 index at 24 months compared with baseline was also observed in letrozole-treated patients (p = 0.023), but not in anastrozole-treated patients. In conclusion, our findings support a possible suppressive effect of letrozole on liver fibrosis in the clinical setting. Further studies are required to better understand the pharmacological effects of letrozole.