Turkey Cost of Living Index: 85=100: Istanbul: Other Expenses data was reported at 2,668,028.400 1985=100 in Oct 2018. This records an increase from the previous number of 2,664,297.400 1985=100 for Sep 2018. Turkey Cost of Living Index: 85=100: Istanbul: Other Expenses data is updated monthly, averaging 774,931.500 1985=100 from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 2,668,028.400 1985=100 in Oct 2018 and a record low of 124.000 1985=100 in Jan 1987. Turkey Cost of Living Index: 85=100: Istanbul: Other Expenses data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.I013: Cost of Living Index: Wage Earners: Istanbul: 1985=100.

Turkey Cost of Living Index: 85=100: Istanbul: Food Expenses data was reported at 3,258,334.900 1985=100 in Oct 2018. This records an increase from the previous number of 3,171,612.700 1985=100 for Sep 2018. Turkey Cost of Living Index: 85=100: Istanbul: Food Expenses data is updated monthly, averaging 616,252.100 1985=100 from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 3,258,334.900 1985=100 in Oct 2018 and a record low of 146.000 1985=100 in Jan 1987. Turkey Cost of Living Index: 85=100: Istanbul: Food Expenses data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.I013: Cost of Living Index: Wage Earners: Istanbul: 1985=100.

Turkey Cost of Living Index: 85=100: Istanbul: Clothing Expenses data was reported at 3,723,370.400 1985=100 in Oct 2018. This records an increase from the previous number of 3,215,209.400 1985=100 for Sep 2018. Turkey Cost of Living Index: 85=100: Istanbul: Clothing Expenses data is updated monthly, averaging 1,110,119.050 1985=100 from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 3,723,370.400 1985=100 in Oct 2018 and a record low of 281.000 1985=100 in Jan 1987. Turkey Cost of Living Index: 85=100: Istanbul: Clothing Expenses data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.I013: Cost of Living Index: Wage Earners: Istanbul: 1985=100.

Formula Systems 1985 reported $557.87M in Cost of Sales for its fiscal quarter ending in June of 2025. Data for Formula Systems 1985 | FORTY - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Abstract

The Côte d'Ivoire Living Standards Survey (LSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (LSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN The principal objective of the sample selection process for the LSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households. A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time. It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

(b) SAMPLE FRAME 1. Sampling Procedures for Block 1 Data The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type. Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

1. Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census. Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.) Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The Household Questionnaire was almost entirely pre-coded, thus reducing errors involved in the coding process. Also, the decentralized data entry system allowed for immediate follow-up on inconsistencies that were detected by the data entry program. Household and personal identification codes were recorded in each section, facilitating merging data across sections

Response rate

During the first year of the survey, a total of 124 of the 1600 original sample households (7.8 percent) were not interviewed and were thus replaced by other households. The most common reason for non-response in the first year was the inability to locate the address or housing unit. Only 14 households (0.9 percent of the sample) were found but refused to participate, and these were all in Abidjan.

Sampling error estimates

(a) ACCURACY The general consensus is that the quality of the LSS household data is very good. An informal review of data quality conducted by Ainsworth and Mehra (1988) assessed the 1985 and 1986 LSS data in terms of their accuracy, completeness, and internal consistency. The LSS household data were found to score high marks on each of these three counts. One measure of data quality is the extent to which individuals in question respond for themselves during the interview, rather than having proxy responses provided for them by other household members. The investigation of CILSS household survey data for 1985 and 1986 showed that 93 percent of women responded for themselves to the fertility section and that 79 to 80 percent of all adult household members responded for themselves to the employment module. The percent of children responding for themselves to the employment module was far less, 43 to 45 percent. Nevertheless, these rates were found to be higher than for the Peru Living Standards Survey (29 percent).

(b) COMPLETENESS

Investigation of several variables and modules

Turkey Cost of Living Index: 85=100: Istanbul: Health Care Expenses data was reported at 5,444,142.200 1985=100 in Nov 2018. This records an increase from the previous number of 5,442,162.900 1985=100 for Oct 2018. Turkey Cost of Living Index: 85=100: Istanbul: Health Care Expenses data is updated monthly, averaging 747,976.400 1985=100 from Jan 1987 (Median) to Nov 2018, with 383 observations. The data reached an all-time high of 5,444,142.200 1985=100 in Nov 2018 and a record low of 121.000 1985=100 in Jan 1987. Turkey Cost of Living Index: 85=100: Istanbul: Health Care Expenses data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.I013: Cost of Living Index: Wage Earners: Istanbul: 1985=100.

Formula Systems 1985 stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Abstract

The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey.

The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique.

The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National coverage. Domains: Urban/rural; Regions (East Forest, West Forest, East Savannah, West Savannah)

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample selection process for the CILSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households.

A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time.

It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

Sampling Procedures for Block 1 Data

The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.

The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type.

Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census.

Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.)

Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Bias in the Selection of Households within PSUs, Block 1 Data

Analysis of the four years of the CILSS data revealed that household size (unweighted), dropped by 24 percent between 1985 and 1988. Three possible explanations were considered: (1) area l demographic change; (2) non-sampling measurement errors were involved; or (3) some sort of sampling bias. Investigation ruled out the first two possibilities. The third possibility clearly was an issue because the sampling frame and listing procedures had indeed changed in midstream and this was likely to have had an effect. In fact, the investigation found that the substantial part of the drop in household size over the years occurred between the first and second panel data sets in 1987, i.e. the tail end of Block 1 data and the start of Block 2 data. From this, it is reasonable to assume that differences in the sampling frame and sampling procedures between the two blocks were indeed responsible.

The listing procedures for Block 1 data indicate d that the selection of households within PSUs was likely to have been biased toward the selection of larger dwellings. Based on a discussion with Christopher Scott, statistical consultant, Demery and Grootaert explain as follows: "In the selected primary sampling units, where the listing of households was to occur, enumerators were instructed to start the listing process at a random location in the primary sampling unit and from this point to select every nth household, that is, with a given fixed "step" until sixty-four households were listed. There are two sources of potential bias in this listing procedure. First, the selection of the starting point might not have been random, but subject to motivated bias on the part of the enumerator (such as the selection of a point where there are numerous dwellings or that is easily accessible). Second, in practice, enumerators counted doors to achieve the "step", rather than counting actual households. This method leads to

Turkey Cost of Living Index: 85=100: Istanbul: Dwelling Expenses data was reported at 6,048,033.000 1985=100 in Oct 2018. This records an increase from the previous number of 5,958,190.200 1985=100 for Sep 2018. Turkey Cost of Living Index: 85=100: Istanbul: Dwelling Expenses data is updated monthly, averaging 1,287,540.450 1985=100 from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 6,048,033.000 1985=100 in Oct 2018 and a record low of 185.000 1985=100 in Jan 1987. Turkey Cost of Living Index: 85=100: Istanbul: Dwelling Expenses data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.I013: Cost of Living Index: Wage Earners: Istanbul: 1985=100.

Formula Systems 1985 reported $10.23M in Interest Expense on Debt for its fiscal quarter ending in June of 2025. Data for Formula Systems 1985 | FORTY - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

The Data-compilation is a selection of time-series on wage- and salary development as well as on the development of the national income in Germany from 1850 to 1985. The following studies has been included: - Walther G. Hoffmann (1965): Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts.- Rüdiger Hohls (1991): Arbeit und Verdienst. Entwicklung und Struktur der Arbeitseinkommen im Deutschen Reich und in der Bundesrepublik.- Pierenkemper, Toni (1987): Arbeitsmarkt und Angestellte im deutschen Kaiserreich 1880-1913. Interessen und Strategien als Elemente der Integration eines segmentierten Arbeitsmarktes.- Wiegand, Erich/Zapf, Wolfgang (1982): Wandel der Lebensbedingungen in Deutschland. Wohlfahrtsentwicklung seit der Industrialisierung. Tables in ZA-Online-Database HISTAT: A. Hoffmann, Walther G.: The Growth of the German Economy since the mid of the 19th centuryA.1 The average earned income per annum by industrial sector (1850-1959)A.2 The average earned income per annum in mining and saline (1850-1959)A.3 The average earned income per annum in industry and craft (1850-1959)A.4 The average earned income per annum in transport (1850-1959)A.5 The average earned income per annum in other services (1850-1959)A.6 Net national product (NNP) in factor costs in current prices and national income per capita according to Hoffmann (1850-1959)A.7 Gross value added and real national income per capita in prices of 1913 according to Hoffmann (1850-1959)A.8 The development of average earned income of employees in industry and craft, Index 1913 = 100 (1850-1959) B. Hohls, Rüdiger: The Sectoral Structure of Earnings in GermanyB.1 Nominal annual earnings of employees by industrial sector in Germany in Mark, 1885-1985B.2 Nominal earnings of white collar workers and blue collar workers in Germany, 1890-1940 C. Living costs, prices and earnings, consumer price indexC.1 Development of living costs (index) of medium employees’ households (1924-1978)C.2 Preices and earnings, index 1962 = 100 (1820-2001)C.3 Living costs, consumer price index (1820-2001) D. Pierenkemper, Toni: Employment market and employees in the German ‘Reich’ 1880-1913.D.1 Income of selected white collar categories in Mark (1880-1913)D.2 Real income of selected white collar categories (1880-1913) E. Wiegand, E.: Historical Development of Wages and Living Costs in Germany.E.1 Development of real gross income of blue collar workers in industry, index 1970 = 100 (1925-1978)E.2 Development of real gross income of blue collar workers in industry (1925-1978)E.3 Development of nominal and real national income per capita (1950-1978) E.4 Development of nominal and real national income per capita (1925-1939)E.5 National income: monthly income from dependent personal services per employee (1925-1971)E.6 Overlook: Development of wages, employed workers and gross income from dependent personal services in Germany (1810-1989)

When adjusted for inflation, the 2024 federal minimum wage in the United States is over 40 percent lower than the minimum wage in 1970. Although the real dollar minimum wage in 1970 was only 1.60 U.S. dollars, when expressed in nominal 2024 dollars this increases to 13.05 U.S. dollars. This is a significant difference from the federal minimum wage in 2024 of 7.25 U.S. dollars.

Turkey Cost of Living Index: 85=100: Istanbul: Cultural,Edu & Entertainment data was reported at 3,870,327.700 1985=100 in Oct 2018. This records an increase from the previous number of 3,792,269.300 1985=100 for Sep 2018. Turkey Cost of Living Index: 85=100: Istanbul: Cultural,Edu & Entertainment data is updated monthly, averaging 533,538.300 1985=100 from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 3,870,327.700 1985=100 in Oct 2018 and a record low of 143.000 1985=100 in Jan 1987. Turkey Cost of Living Index: 85=100: Istanbul: Cultural,Edu & Entertainment data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.I013: Cost of Living Index: Wage Earners: Istanbul: 1985=100.

Formula Systems 1985 reported $675.48M in Operating Expenses for its fiscal quarter ending in June of 2025. Data for Formula Systems 1985 | FORTY - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Turkey Cost of Living Index: 85=100: Istanbul: Transport & Com Exp data was reported at 2,696,414.100 1985=100 in Nov 2018. This records a decrease from the previous number of 2,747,387.100 1985=100 for Oct 2018. Turkey Cost of Living Index: 85=100: Istanbul: Transport & Com Exp data is updated monthly, averaging 748,187.400 1985=100 from Jan 1987 (Median) to Nov 2018, with 383 observations. The data reached an all-time high of 2,747,387.100 1985=100 in Oct 2018 and a record low of 121.000 1985=100 in Jan 1987. Turkey Cost of Living Index: 85=100: Istanbul: Transport & Com Exp data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.I013: Cost of Living Index: Wage Earners: Istanbul: 1985=100.

Formula Systems 1985 reported $93.78M in Selling and Administration Expenses for its fiscal quarter ending in June of 2025. Data for Formula Systems 1985 | FORTY - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Formula Systems 1985 reported $15.05M in Net Income for its fiscal quarter ending in June of 2025. Data for Formula Systems 1985 | FORTY - Net Income including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Due to the recent hyperinflation crisis in Venezuela, the average inflation rate in Venezuela is estimated to be around 225 percent in 2026. However, this is well below the peak of 63,000 percent observed in 2018.What is hyperinflation?In short, hyperinflation is a very high inflation rate that accelerates quickly. It can be caused by a government printing huge amounts of new money to pay for its expenses. The subsequent rapid increase of prices causes the country’s currency to lose value and shortages in goods to occur. People then typically start hoarding goods, which become even more scarce and expensive, money becomes worthless, financial institutions go bankrupt, and eventually, the country’s economy collapses. The Venezuelan descent into hyperinflationIn Venezuela, the economic catastrophe began with government price controls and plummeting oil prices, which caused state-run oil companies to go bankrupt. The government then starting printing new money to cope, thus prices rose rapidly, unemployment increased, and GDP collapsed, all of which was exacerbated by international sanctions. Today, many Venezuelans are emigrating to find work and supplies elsewhere, and population growth is at a decade-low. Current president Nicolás Maduro does not seem inclined to steer away from his course of price controls and economic mismanagement, so the standard of living in the country is not expected to improve significantly anytime soon.

The Data-compilation is a selection of time-series on wage- and salary development as well as on the development of the national income in Germany from 1850 to 1985. The following studies has been included:

• Walther G. Hoffmann (1965): Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts.
• Rüdiger Hohls (1991): Arbeit und Verdienst. Entwicklung und Struktur der Arbeitseinkommen im Deutschen Reich und in der Bundesrepublik.
• Pierenkemper, Toni (1987): Arbeitsmarkt und Angestellte im deutschen Kaiserreich 1880-1913. Interessen und Strategien als Elemente der Integration eines segmentierten Arbeitsmarktes.
• Wiegand, Erich/Zapf, Wolfgang (1982): Wandel der Lebensbedingungen in Deutschland. Wohlfahrtsentwicklung seit der Industrialisierung.

Tables in ZA-Online-Database HISTAT:

A. Hoffmann, Walther G.: The Growth of the German Economy since the mid of the 19th century A.1 The average earned income per annum by industrial sector (1850-1959) A.2 The average earned income per annum in mining and saline (1850-1959) A.3 The average earned income per annum in industry and craft (1850-1959) A.4 The average earned income per annum in transport (1850-1959) A.5 The average earned income per annum in other services (1850-1959) A.6 Net national product (NNP) in factor costs in current prices and national income per capita according to Hoffmann (1850-1959) A.7 Gross value added and real national income per capita in prices of 1913 according to Hoffmann (1850-1959) A.8 The development of average earned income of employees in industry and craft, Index 1913 = 100 (1850-1959)

B. Hohls, Rüdiger: The Sectoral Structure of Earnings in Germany B.1 Nominal annual earnings of employees by industrial sector in Germany in Mark, 1885-1985 B.2 Nominal earnings of white collar workers and blue collar workers in Germany, 1890-1940

C. Living costs, prices and earnings, consumer price index C.1 Development of living costs (index) of medium employees’ households (1924-1978) C.2 Preices and earnings, index 1962 = 100 (1820-2001) C.3 Living costs, consumer price index (1820-2001)

D. Pierenkemper, Toni: Employment market and employees in the German ‘Reich’ 1880-1913. D.1 Income of selected white collar categories in Mark (1880-1913) D.2 Real income of selected white collar categories (1880-1913)

E. Wiegand, E.: Historical Development of Wages and Living Costs in Germany. E.1 Development of real gross income of blue collar workers in industry, index 1970 = 100 (1925-1978) E.2 Development of real gross income of blue collar workers in industry (1925-1978) E.3 Development of nominal and real national income per capita (1950-1978) E.4 Development of nominal and real national income per capita (1925-1939) E.5 National income: monthly income from dependent personal services per employee (1925-1971) E.6 Overlook: Development of wages, employed workers and gross income from dependent personal services in Germany (1810-1989)