This dataset presents key macroeconomic indicators for the State of Qatar on an annual basis. It includes data on GDP, national income, consumption, investment, inflation, trade, government finance, and monetary aggregates. These indicators provide insight into the performance and structure of the Qatari economy across five years.

Romania's volume index for industrial production decreased by nearly five percent in February 2023 compared to the corresponding month of the previous year. By contrast, the volume index of the services provided to the population marked the highest growth among other macroeconomic indicators, with the growth rate of almost 22 percent year-over-year.

This collection contains an array of economic time series data pertaining to the United States, the United Kingdom, Germany, and France, primarily between the 1920s and the 1960s, and including some time series from the 18th and 19th centuries. These data were collected by the National Bureau of Economic Research (NBER), and they constitute a research resource of importance to economists as well as to political scientists, sociologists, and historians. Under a grant from the National Science Foundation, ICPSR and the National Bureau of Economic Research converted this collection (which existed heretofore only on handwritten sheets stored in New York) into fully accessible, readily usable, and completely documented machine-readable form. The NBER collection -- containing an estimated 1.6 million entries -- is divided into 16 major categories: (1) construction, (2) prices, (3) security markets, (4) foreign trade, (5) income and employment, (6) financial status of business, (7) volume of transactions, (8) government finance, (9) distribution of commodities, (10) savings and investments, (11) transportation and public utilities, (12) stocks of commodities, (13) interest rates, and (14) indices of leading, coincident, and lagging indicators, (15) money and banking, and (16) production of commodities. Data from all categories are available in Parts 1-22. The economic variables are usually observations on the entire nation or large subsets of the nation. Frequently, however, and especially in the United States, separate regional and metropolitan data are included in other variables. This makes cross-sectional analysis possible in many cases. The time span of variables in these files may be as short as one year or as long as 160 years. Most data pertain to the first half of the 20th century. Many series, however, extend into the 19th century, and a few reach into the 18th. The oldest series, covering brick production in England and Wales, begins in 1785, and the most recent United States data extend to 1968. The unit of analysis is an interval of time -- a year, a quarter, or a month. The bulk of observations are monthly, and most series of monthly data contain annual values or totals. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR07644.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

The statistic shows the distribution of the workforce across economic sectors in China from 2014 to 2024. In 2024, around 22.2 percent of the workforce were employed in the agricultural sector, 29 percent in the industrial sector and 48.8 percent in the service sector. In 2022, the share of agriculture had increased for the first time in more than two decades, which highlights the difficult situation of the labor market due to the pandemic and economic downturn at the end of the year. Distribution of the workforce in China In 2012, China became the largest exporting country worldwide with an export value of about two trillion U.S. dollars. China’s economic system is largely based on growth and export, with the manufacturing sector being a crucial contributor to the country’s export competitiveness. Economic development was accompanied by a steady rise of labor costs, as well as a significant slowdown in labor force growth. These changes present a serious threat to the era of China as the world’s factory. The share of workforce in agriculture also steadily decreased in China until 2021, while the agricultural gross production value displayed continuous growth, amounting to approximately 7.8 trillion yuan in 2021. Development of the service sector Since 2011, the largest share of China’s labor force has been employed in the service sector. However, compared with developed countries, such as Japan or the United States, where 73 and 79 percent of the work force were active in services in 2023 respectively, the proportion of people working in the tertiary sector in China has been relatively low. The Chinese government aims to continue economic reform by moving from an emphasis on investment to consumption, among other measures. This might lead to a stronger service economy. Meanwhile, the size of the urban middle class in China is growing steadily. A growing number of affluent middle class consumers could promote consumption and help China move towards a balanced economy.

The Comparative Political Economy Database (CPEDB) began at the Centre for Learning, Social Economy and Work (CLSEW) at the Ontario Institute for Studies in Education at the University of Toronto (OISE/UT) as part of the Changing Workplaces in a Knowledge Economy (CWKE) project. This data base was initially conceived and developed by Dr. Wally Seccombe (independent scholar) and Dr. D.W. Livingstone (Professor Emeritus at the University of Toronto). Seccombe has conducted internationally recognized historical research on evolving family structures of the labouring classes (A Millennium of Family Change: Feudalism to Capitalism in Northwestern Europe and Weathering the Storm: Working Class Families from the Industrial Revolution to the Fertility Decline). Livingstone has conducted decades of empirical research on class and labour relations. A major part of this research has used the Canadian Class Structure survey done at the Institute of Political Economy (IPE) at Carleton University in 1982 as a template for Canadian national surveys in 1998, 2004, 2010 and 2016, culminating in Tipping Point for Advanced Capitalism: Class, Class Consciousness and Activism in the ‘Knowledge Economy’ (https://fernwoodpublishing.ca/book/tipping-point-for-advanced-capitalism) and a publicly accessible data base including all five of these Canadian surveys (https://borealisdata.ca/dataverse/CanadaWorkLearningSurveys1998-2016). Seccombe and Livingstone have collaborated on a number of research studies that recognize the need to take account of expanded modes of production and reproduction. Both Seccombe and Livingstone are Research Associates of CLSEW at OISE/UT. The CPEDB Main File (an SPSS data file) covers the following areas (in order): demography, family/household, class/labour, government, electoral democracy, inequality (economic, political & gender), health, environment, internet, macro-economic and financial variables. In its present form, it contains annual data on 725 variables from 12 countries (alphabetically listed): Canada, Denmark, France, Germany, Greece, Italy, Japan, Norway, Spain, Sweden, United Kingdom and United States. A few of the variables date back to 1928, and the majority date from 1960 to 1990. Where these years are not covered in the source, a minority of variables begin with more recent years. All the variables end at the most recent available year (1999 to 2022). In the next version developed in 2025, the most recent years (2023 and 2024) will be added whenever they are present in the sources’ datasets. For researchers who are not using SPSS, refer to the Chart files for overviews, summaries and information on the dataset. For a current list of the variable names and their labels in the CPEDB data base, see the excel file: Outline of SPSS file Main CPEDB, Nov 6, 2023. At the end of each variable label in this file and the SPSS datafile, you will find the source of that variable in a bracket. If I have combined two variables from a given source, the bracket will begin with WS and then register the variables combined. In the 14 variables David created at the beginning of the Class Labour section, you will find DWL in these brackets with his description as to how it was derived. The CPEDB’s variables have been derived from many databases; the main ones are OECD (their Statistics and Family Databases), World Bank, ILO, IMF, WHO, WIID (World Income Inequality Database), OWID (Our World in Data), Parlgov (Parliaments and Governments Database), and V-Dem (Varieties of Democracy). The Institute for Political Economy at Carleton University is currently the main site for continuing refinement of the CPEDB. IPE Director Justin Paulson and other members are involved along with Seccombe and Livingstone in further development and safe storage of this updated database both at the IPE at Carleton and the UT dataverse. All those who explore the CPEDB are invited to share their perceptions of the entire database, or any of its sections, with Seccombe generally (wallys@blackcreekfarm.ca) and Livingstone for class/labour issues (davidlivingstone@utoronto.ca). They welcome any suggestions for additional variables together with their data sources. A new version CPEDB will be created in the spring of 2025 and installed as soon as the revision is completed. This revised version is intended to be a valuable resource for researchers in all of the included countries as well as Canada.

Due to the rise of global supply chains, gross exports do not accurately measure the amount of value added exchanged between countries. I highlight five facts about differences between gross and value-added exports. These differences are large and growing over time, currently around 25 percent, and manufacturing trade looks more important, relative to services, in gross than value-added terms. These differences are also heterogenous across countries and bilateral partners, and changing unevenly across countries and partners over time. Taking these differences into account enables researchers to obtain better quantitative answers to important macroeconomic and trade questions. I discuss how the facts inform analysis of the transmission of shocks across countries; the mechanics of trade balance adjustments; the impact of frictions on trade; the role of endowments and comparative advantage; and trade policy.

A monthly and quarterly data set spanning July 1995 to December 2016 of the following macro-economic variables 1. South African stock market 2. South African GDP3. United States GDP 4. South African interest rate 5. US interest rate 6. South African inflation rate 7. US inflation rate 8. South African Money Supply 9. Rand/Dollar Exchange 10. FTSE

The industrial production index measures the monthly evolution of the volume of industrial production, excluding construction. As of September 2024, the seasonally adjusted industrial production index stood at 94, remaining stable compared to the previous months. Due to the COVID-19 pandemic, the index plummeted between March and August 2020.

This paper investigates both the effects of domestic monetary policy and external shocks on fundamental macroeconomic variables in six fast growing emerging economies: Brazil, Russia, India, China, South Africa and Turkey—denoted hereafter as BRICS_T. The authors adopt a structural VAR model with a block exogeneity procedure to identify domestic monetary policy shocks and external shocks. Their research reveals that a contractionary monetary policy in most countries appreciates the domestic currency, increases interest rates, effectively controls inflation rates and reduces output. They do not find any evidence of the price, output, exchange rates and trade puzzles that are usually found in VAR studies. Their findings imply that the exchange rate is the main transmission mechanism in BRICS_T economies. The authors also find that that there are inverse J-curves in five of the six fast growing emerging economies and there are deviations from UIP (Uncovered Interest Parity) in response to a contractionary monetary policy in those countries. Moreover, world output shocks are not a dominant source of fluctuations in those economies.

China’s export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China’s macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China’s financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China’s macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China’s and the United States’ economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model’s sampling results are good. China’s pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China’s pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market.

Part I: Germany By means of a statistical analysis, this study examines the question whether there are long-term fluctuations in the course of the economic development in Germany, and if so, why they occur. On the grounds of a discussion about the hypothesises of lack of capital, overproduction and innovation, an explanatory model for the growth waves has been developed. This model, which is based on an empirical analysis, can be summarised as follows: The long-term development of the national product is mainly determined by the development of investments, which depend on the development of the profit expectations in their turn. In this respect, the development of wages, national consumption, and protection are considered important factors for the definition of long-term profit expectations. Hereby the above-mentioned model is empirically tested. Eventually some economic conclusions are drawn.

Part II: An International Comparison In the 1970s, the process of global economic growth weakened considerably as compared to the two preceding decades. This development provoked several explanatory attempts. Within the scope of an empiric study for Germany, the slowed growth of the 1970s has been understood as being the downswing phase of a long-term cycle of development. In doing so, the diagnosed development of the national product was mainly explained by long-term fluctuations of the (functional) distribution of income and the governmental activity, which, on their part, caused long-term ups and downs concerning investment activities due to their influence on profit expectations. In fact, the article faced harsh criticism, which was directed at both the explanatory approach and the under-lying empirical method. This study calculates the deviations of streamlined national product series from the long-term trend; its results show that there have been long-term, more or less distinct fluctuations in the development of the national product of several free-market countries other than Germany. According to the available data, different index numbers were applied to the respective national production. The period examined in this study for every country reaches as far back as data are available.

With regard to the results of the empirical analysis of the long-term economic development of Germany, France, Italy, Sweden, the United Kingdom, the United States, and the Soviet Union, it can be stated that - long-term fluctuations of the economic development are not merely restricted to Germany, and that a socialistic economic system presumably does not guarantee a continuous growth either; - the cyclical pattern differs from country to country; - there were parallel developments at the international level; however, these do not develop in a synchronous way.

Factual classification of the tables in HISTAT: Part I: Germany Part I: 1. Macroeconomic indicators for the Federal Republic of Germany (1960-1990) Part I: A.1 Net national product and net investments in the Federal Republic of Germany (1850-1990) Part I:A.2 Net national product, net investments, foreign trade values and national consumption (in million D-marks) in Germany (1850-1990) Part I: A.3 Stock yields and profit expectations (in percent) in Germany (1926-1977) Part I: A.4 Actual earnings of employees and unemployment rate (in percent) in Germany (1925-1990) Part I: A.5 The population (in 1,000) in the Federal Republic of Germany and in the German Reich (1850-1913)

Part II: International comparison Part II: A.1.Macroeconomic annual production of selected states (1830-1979) Part II: A.2 Investments of selected states (1830-1979) Part II: A.3 Unemployment rate of selected states (in percent) (1887-1979)

Structure: I) General information on the social indicator systemIa) Background II) The dimension of life: Housing I) General information on the social indicator system The time series of the European System of Social Indicators (EUSI) are´social indicators´ used to measure social welfare and social change. The conceptual framework builds on the theoretical discussion of welfare, quality of life and the goals of social development oriented towards them.The basis for defining these indicators is a concept of quality of life that encompasses different areas of life in society. Each area of life can be divided into several target areas. Target dimensions have been defined for the individual target areas, for each of which a set of social indicators (= time series, statistical measures) has been defined. The EUSI indicator time series combine objective living conditions (factual living conditions such as working conditions, income development) and subjective well-being (perceptions, assessments, evaluations) of the population.The time series starts in 1980 and end in 2013.They make it possible to understand social developments by reliable and, over time, comparable data between European countries.They are an important complement to national accounts indicators.EUSI indicators are part of an ongoing debate at European level on measuring welfare and quality of life, which has led to various initiatives by statistical offices in Europe. Ia) Background The social indicator system is the result of a discussion sparked off in the 1970s to measure a country´s prosperity development. Hans-Jürgen Krupp and Wolfgang Zapf initiated this discussion. Together they pointed out in 1972 in an expert opinion for the German Council of Economic Experts that the gross domestic product in particular and the parameters of national accounts (NA) in general are not sufficient to measure social welfare or ignore important aspects. (see:Krupp, H.-J. and Zapf, W. (1977), The role of alternative indicators of prosperity in assessing macroeconomic development. Council for Social and Economic Data, Working Paper No. 171, reprint of the report for the Council of Economic Experts of September 1972: 2011) They developed a multidimensional concept of quality of life in which, in addition to national accounts, the individual development possibilities and the possibilities perceived by individuals for satisfying their needs in different areas of life are also taken into account.The authors define the quality of life as ´the extent to which individuals perceive the satisfaction of their needs´ (1977, reprint: 2011, p. 4). Thus, the purely national economic concept of growth and prosperity is supplemented by categories of sociology and political science, in which ´quality of life is (represents) a positive objective against which efforts to measure and evaluate performance and deficits in the individual areas of life and for different social groups should be oriented´. (Krupp/Zapf, 1977, reprint: 2011, p. 5) In this way, the authors promote comprehensive social reporting that measures the achievement of welfare goals in society.The authors explain the concept of social indicators as follows: ´Social indicators are statistics that differ from usual statistics in several ways.They should measure performance, not the expenditure.They should primarily refer to the welfare of individuals and certain social groups, not to the activities of authorities; however, a whole range of aggregate sizes cannot be dispensed with.They should inform about change processes, i.e., be presented in the form of time series.They should be in a theoretical context, i.e., their causal relationship to the´indicator date´ should be as clear as possible. (… )Social indicators are statistics that often lie far outside the official survey programmes (...)´. (Krupp/ Zapf, 1977, p. 14) Compared to official government reporting, the system of social indicators represents independent reporting (cf. Krupp/Zapf 1977, p. 7) and also includes survey research in addition to official data. Based on the theoretical concept of quality of life, the structural parameters of the indicator system were defined. This means that the areas of life and the target and measurement dimensions belonging to them are operationalized. This initially results in a multidimensional structure with the following levels:1) The current ten areas of life are the highest level.They have published in histat under the topic ´SIMon: Social Indicators Monitor 1950-2013´.as individual studies.2) The second level is the target areas.Several target areas are assigned to each area of life. They appear as tables in the respective studies.3) The third level is the target dimensions (also called measurement dimensions). This is a subarea that is meaningful for the higher-level life area and for which data is collected for the corresponding target area. For example, a table on the´objective living conditions´ is offered for t...

Structure: I) General information on the social indicator systemIa) Background II) The Dimension of life: Income, Standard of Living, and Consumption Patterns I) General information on the social indicator system The time series of the European System of Social Indicators (EUSI) are´social indicators´ used to measure social welfare and social change. The conceptual framework builds on the theoretical discussion of welfare, quality of life and the goals of social development oriented towards them.The basis for defining these indicators is a concept of quality of life that encompasses different areas of life in society. Each area of life can be divided into several target areas. Target dimensions have been defined for the individual target areas, for each of which a set of social indicators (= time series, statistical measures) has been defined. The EUSI indicator time series combine objective living conditions (factual living conditions such as working conditions, income development) and subjective well-being (perceptions, assessments, evaluations) of the population.The time series starts in 1980 and end in 2013.They make it possible to understand social developments by reliable and, over time, comparable data between European countries.They are an important complement to national accounts indicators.EUSI indicators are part of an ongoing debate at European level on measuring welfare and quality of life, which has led to various initiatives by statistical offices in Europe. Ia) Background The social indicator system is the result of a discussion sparked off in the 1970s to measure a country´s prosperity development. Hans-Jürgen Krupp and Wolfgang Zapf initiated this discussion. Together they pointed out in 1972 in an expert opinion for the German Council of Economic Experts that the gross domestic product in particular and the parameters of national accounts (NA) in general are not sufficient to measure social welfare or ignore important aspects. (see:Krupp, H.-J. and Zapf, W. (1977), The role of alternative indicators of prosperity in assessing macroeconomic development. Council for Social and Economic Data, Working Paper No. 171, reprint of the report for the Council of Economic Experts of September 1972: 2011) They developed a multidimensional concept of quality of life in which, in addition to national accounts, the individual development possibilities and the possibilities perceived by individuals for satisfying their needs in different areas of life are also taken into account.The authors define the quality of life as ´the extent to which individuals perceive the satisfaction of their needs´ (1977, reprint: 2011, p. 4). Thus, the purely national economic concept of growth and prosperity is supplemented by categories of sociology and political science, in which ´quality of life is (represents) a positive objective against which efforts to measure and evaluate performance and deficits in the individual areas of life and for different social groups should be oriented´. (Krupp/Zapf, 1977, reprint: 2011, p. 5) In this way, the authors promote comprehensive social reporting that measures the achievement of welfare goals in society.The authors explain the concept of social indicators as follows: ´Social indicators are statistics that differ from usual statistics in several ways.They should measure performance, not the expenditure.They should primarily refer to the welfare of individuals and certain social groups, not to the activities of authorities; however, a whole range of aggregate sizes cannot be dispensed with.They should inform about change processes, i.e., be presented in the form of time series.They should be in a theoretical context, i.e., their causal relationship to the´indicator date´ should be as clear as possible. (… )Social indicators are statistics that often lie far outside the official survey programmes (...)´. (Krupp/ Zapf, 1977, p. 14) Compared to official government reporting, the system of social indicators represents independent reporting (cf. Krupp/Zapf 1977, p. 7) and also includes survey research in addition to official data. Based on the theoretical concept of quality of life, the structural parameters of the indicator system were defined. This means that the areas of life and the target and measurement dimensions belonging to them are operationalized. This initially results in a multidimensional structure with the following levels:1) The current 10 areas of life are the highest level.They have published in histat under the topic ´SIMon: Social Indicators Monitor 1950-2013´.as individual studies.2) The second level is the target areas.Several target areas are assigned to each area of life. They appear as tables in the respective studies.3) The third level is the target dimensions (also called measurement dimensions). This is a subarea that is meaningful for the higher-level life area and for which data is collected for the corresponding target area. For example, a table on the´o...

A Computable General Equilibrium (CGE) model in a bottom-up approach - based on microfoundations - and a Social Accounting Matrix (SAM) for the regional economy of Chiapas are built. Methodology: This research applies a Computable General Equilibrium (CGE) model. It is a system of equations that describes an entire economy and all the interactions between productive sectors, commodity and factor markets, and institutions. All of the equations are solved simultaneously to find an economy-wide equilibrium in which demand and supply quantities are equal in every market at a certain level of prices (Burfisher, 2011). Two of the features of this model are that, on one hand, it implements a “bottom-up” approach, that is, it is focused on individual markets and economic agents. On the other hand, it is partially synthetic. In other words, most parameters can be calibrated with data from the SAM. Data framework: A Social Accounting Matrix (SAM) is a balanced square matrix that represents all income and expenditure flows between productive sectors, markets, and economic agents of an economy at a given period of time (Müller, Perez & Hubertus, 2009). It is based on the double entry bookkeeping in accounting, which requires that total revenue equals total expenditure in each single account included in the SAM (Breisinger, Thomas & Thurlow, 2010). The main features of the Chiapas SAM are that production activities are broken down in 10 sectors, according to the North American Industry Classification System (NAICS). There is one commodity per economic activity. Factors of production are disaggregated into formal and informal labor, and capital. Direct taxes are broken up into activity tax, social security contributions, household and corporate income taxes, ‘tenencia’ tax (ownership tax, i.e. a tax associated with the possession or use of vehicles), and regional payroll tax (‘nomina’). Indirect taxes, in turn, are value-added, sales and export taxes, and import tariffs. Subsidies on production by economic activity are also included. Households are disaggregated by income quintiles. Social transfers are split in non-conditional (Procampo, universal pension, PAL-Sin Hambre , temporary employment program, and the regional program Amanecer ) and Oportunidades. The latter is also broken down into its five components: food, elderly, education, child, and energy. The introduction of conditional cash transfers in the SAM is particularly relevant because it allows assessing the impact of changes in their amount and distribution on household income, poverty reduction, income inequality, and economic growth at the regional level. Data sources: - National Institute of Statistics and Geography (INEGI): 2012 National Employment and Occupation Survey 2013 Chiapas Statistical Yearbook 2012 National Household Income-Expenditure Survey 2012 Chiapas Statistical Perspective 2003-2012 Goods and Services Accounts (SCNM) 2003-2012 Institutional Sector Accounts (SCNM) 2008 Input-Output Table 2008 Supply and Use Tables - Chiapas State Committee of Statistical and Geographical Information (CEIEG): 2012 Chiapas Employment and Occupation Survey 2012 Chiapas Monthly Statistical Reports of IMSS-insured Workers - Federal Ministry of Labor and Social Welfare (STYPS): 2012 IMSS-registered Daily Salary by Economic Activity 2012 IMSS-insured Workers Quality/Lineage: With the raw data a Social Accounting Matrix for the regional economy of Chiapas was built Features: - Oportunidades broken down by component - Other non-conditional social transfers such as Procampo, PAL-Sin Hambre, Employment program, Universal pension, and the regional program 'Amanecer' - Informal wages - Satellites tables of formal and informal employment - Productive activities according to the North American Industry Classification System (NAICS) used in Mexico, Canada, and the United States of America - 10 economic activities - 10 Commodities (one per economic activity) - Factors of production: formal and informal labor and capital Purpose: 1. To assess the opportunity cost of financing "Oportunidades", Mexico's conditional cash transfers program, and its implications for rural development and rural economic growth in the regional setting of Chiapas. Moreover, 2. Pro-growth and pro-poor tax structures are also evaluated by applying standard economic analysis tools and modeling to substantially raise the federal non-oil tax revenue to finance social policy for poverty and inequality reduction. Dissertation: Viveros Añorve, J. L. (2015): The opportunity cost of financing "Oportunidades": a general equilibrium assessment for poverty reduction in Mexico. Ph.D. dissertation. Center for Development Research, Faculty of Agriculture, University of Bonn

Abstract

The Central Bureau of Statistics (CBS) has conducted the third census of real estate business in 2019, following previous surveys in 2003/04 and 2013. The census was carried out by the Trade Statistics Section of the bureau, which is involved in collecting data on social, economic, and environmental statistics, including trade in services and business surveys.

The main objective of the real estate census was to collect information on macroeconomic indicators such as output, intermediate consumption, compensation of employees, change in inventories, number of employees, and capital formation. The results of the census provide important data for estimating gross domestic product (GDP) and other macroeconomic indicators, and contribute to the strengthening of the national statistical system.

The report is based on data collected from 210 real estate establishments, although the National Economic Census in 2018 had shown 207 establishments in the country. The total compensation of employees received by the employers for work done in real estate establishments during the accounting year 2018/19 was estimated to be NRs. 1.2 billion with Bagmati province accounting for the highest portion at 96% of total salaries and wages.

The census also revealed the structure of input and output in the real estate sector. The intermediate consumption in the main activities of real estate was estimated at NRs. 5.27 billion which was used to produce output worth NRs. 9.8 billion. Bagmati province had the highest share of 44% in output compared to other provinces during the census year.

The value added generated from real estate establishments was estimated to be NRs. 4,561,658 thousands, with a ratio of input to output of 0.53, indicating that input generates 1.86 times the output in the real estate sector. This suggests that the real estate sector generates higher profits compared to other sectors of the economy, and entrepreneurs should consider investing a significant amount at the initial stage of their business.

According to the national accounts estimate of CBS for the fiscal year 2018/19, the value added generated from the real estate sector was NRs. 295.7 billion with output of NRs. 406.8 billion and intermediate consumption of NRs. 111 billion. This data provides interesting macroeconomic indicators, including a value added of NRs. 18.9 million per establishment, output of NRs. 40.8 million thousand per real estate establishment, and 5.11 persons engaged per establishment.

Geographic coverage

National level, Provincial level.

Analysis unit

Establishment

Universe

All the real estate establishment of Nepal.

Kind of data

Census/enumeration data [cen]

Sampling deviation

According to NEC, 2018 there were 207 registered real estate establishments in Nepal. During the field work, data from 210 real estate establishments were collected. The census of real estate business (CERB), 2019 could not collect data from any units of establishments from the Madhesh province and Karnali province due to Covid-19 pandemic lockdown. Therefore, the number of establishments for Madhesh and Karnali province were extracted in accordance with NEC,2018. As a result, CERB,2019 has altogether 241 real estate establishments for its purpose.

Mode of data collection

Face-to-face [f2f]

Research instrument

Structure of questionnaire The questionnaires were structured according to the international recommendation of questionnaire in economic statistics. Some questionnaires were added to capture the present situation of the business. The comments and suggestions were also collected to fulfill the requirement of policymakers and researchers so that immediate action can be imposed. The questionnaire was designed to fulfill the requirement of national accounts statistics. There are ten important blocks in questionnaire such as first block is introduction which is obvious in any questionnaire. The details information of employee, compensation of employees, expenses, income, capital formation and stock etc are also collected for national accounts estimate. The census of real estate, 2019 collected the following information: 1. Introduction 2. Legal status 3. Business status 4. Main activities 5. Employment status 6. Salaries and wages 7. Expenditure or intermediate consumption 8. Income 9. Other income 10. Financial intermediate 11. Tax paid 12. Stocks 13. Financial intermediate 14. Capital Formation 15. Comments and suggestions

Cleaning operations

The data were collected in the hard paper and collected in the cetral office. All the filled up questionnaire were entered in the CS Pro program. Then the data were exported in SPSS program for cleaning and editing.

The Gross Domestic Product (GDP) in China was worth 17794.78 billion US dollars in 2023, according to official data from the World Bank. The GDP value of China represents 16.88 percent of the world economy. This dataset provides - China GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Question “Why unemployment?” is one of the most central topics of economic theory since the great depression. Unemployment remains one of the most important problems of economic policies in industrial countries. Unemployment has different causes and therefore also different countermeasures are required. “Together with the destruction of environment unemployment and inflation are in the focus of economic and political discussions on macroeconomic problems and are considered as the greatest challenges of economic policy. Depending on the level of unemployment there is a higher focus on inflation or on unemployment, if both are on an alarming level at the same time they are in the shot simultaneously. In anyway both issues need to be analyzed together because they are not independent from each other. Experiences from the recent years have shown that combating inflation leads to an increase in unemployment, at least temporarily but probably also permanently. The other way around; combating unemployment may under certain circumstances also lead to an increase in inflation… Unemployment and inflation are macroeconomic problems. The level of both undesirable developments is determined by the relations in the entire economy. Therefor it is necessary to use macroeconomic theory which deals the general economic context for the analysis. Both problems are enhanced by structural factors which also need to be analyzed. In contrast to microeconomic theory which focuses on different individual decision makers, in macroeconomic theory decision makers and decisions are summarized in macroeconomic aggregates. The common procedure is to summarize decision makers into aggregates like “private households”, “enterprises” and “the state” and the decision makers concerning the use of income into “private consumption”, “investments” and “public expenditure” (Kromphardt, Jürgen, 1998: Arbeitslosigkeit und Inflation (unemployment and inflation). 2., newly revised A. Göttingen: Vandenhoeck & Ruprecht, p. 17-18). Macroeconomic approaches on the explanation of unemployment and inflation are highly controversial in economic theory. Therefore the author starts with the attempt to present different explanations for unemployment and inflation from different macroeconomic positions. There are different unemployment: classical unemployment (reason: real wages to high), Keynesian unemployment (reason: demand for goods to low), unemployment due to a lack of working places (reason: capital stock to low). These positions give conflicting explanations and recommendations because they are based on different perceptions of the starting position. Therefor the author confronts central positions with empirical data on the macro level with the following restriction: “It is impossible to prove theories as correct (to verify). This is a reason for the fact that macroeconomic controversies do not come to a conclusion but are continued in a modified way. Furthermore economic statements in this field always affect social and political interests as all economic policies favor or put as a disadvantage interests of distinct social groups in a different way.“ (Kromphardt, a.a.O., S. 20).

Data tables in HISTAT (1) Development of employment: Presented by the development of annual average unemployment rates and the balance of labor force of the institute for labor market and occupation research (IAB, Nuremberg) after the domestic concept(employment with Germany as the place of work) For characterizing the overall economic developments, those values are used which play an important role in the reports of the German central bank: (2) Inflation: Rate of differences in the price index for costs of living compared to the previous year (3) Currency reserves of German federal banks and the German central bank: measure for foreign economic situation and the payment balance of the central bank (4) Development of economic growth: Presented by the nominal and real growth rate of the GDP (5) Inflation rate of the GDP, money supply, growth rate of the price index of the GDP (6) Labor productivity (= GDP per employee, domestic concept) (7) Real wage per employee (8) Exchange rate: DM/$ (monthly averages) (9) Growth of DGP, productivity, economically active population, real incomes, unemployment rate and adjusted wages (10) Time series connected with labor demand (11) GDP, labor volume, employees, working hours and labor productivity (12) Employee compensation, wages and ...

Russia's gross domestic product (GDP) was estimated to have increased by 1.9 percent in April 2025 compared to the same month of the previous year. In April 2023, the monthly GDP growth was positive for the first time since March 2022. In April 2020, the country’s GDP fell by nearly 10 percent as a result of the crisis caused by the coronavirus (COVID-19) pandemic as well as the oil price crash. Russian economy outlook for 2025 Russia’s annual GDP was projected to increase by 1.35 percent in 2025. The level of prices in the country was expected to continue growing, with the inflation rate forecast at 4.7 percent in that year. Post-pandemic economic recovery in selected countries Countries across the world saw a sharp decrease in GDP in 2020 due to the COVID-19 pandemic. In 2023, the European Commission foresaw an increase in all European Union (EU) members' GDP, ranging from the lowest of 1.1 percent in Sweden and Italy to the highest of 5 percent in Ireland. In Latin America, the most significant increase in GDP was recorded in Peru, at 5.2 percent in 2022.

Abstract

Moldova Household Budget Survey provides detailed information on social and economic aspects of the welfare of the population and households. The information contained in the sections of the survey allows for in-depth analysis of socio-economic status of households and individual members at the moment or over time. Thus, the survey reveals poor households and individuals, or those households that were unprotected by social policies. The results of household surveys are also used in analyzing the impact of various economic effects of the transition period, the socio-economic status of the population.

Information obtained from the Household Budget Survey is used in the calculation of some macroeconomic indicators: final consumption of households, the Consumer Price Index, the informal trade, etc. Each year, the main indicators of income and expenditure and living conditions are presented to various international organizations: International Labor Organization, World Bank, Food and Agriculture Organization of the United Nations, etc.

Data is collected through face-to-face interviews and the households' income and expenditure diary.

Geographic coverage

National

Analysis unit

• Households,
• Individuals.

Universe

The HBS covers all households/individuals - citizens of the Republic of Moldova who have their permanent residence in the selected survey centers.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling methodology for Moldova HBS was re-designed in 2006.

The updated HBS sample was selected using information from the Population Census 2004 and a joint database of electricity consumers in the whole country. 129 Primary Sampling Units (PSU) are now covered by HBS (before 2006, 45 PSUs were surveyed).

A larger number of PSUs improves regional estimates and ensures a representative sample not only for cities, towns and rural areas as did the previous survey, but also at the level of four statistical zones (North, Centre, South, and Chisinau). Moreover, both Chisinau and Balti municipialities are treated as a large primary sampling unit, from where households are selected, whereas before only some districts were included in the sample.

Two-stage probability sample is used to select households:

1) Sampling procedure: 1st stage - sampling with probabilities proportional to size (PPS) in each strata; 2nd stage - simple random sample (SRS) of households in each PSU.

2) Rotation scheme: 1st stage - about 20% of PSUs will be annually replaced; 2nd stage - one half of households will participate in the survey during five consecutive years and the rest of households will be replaced with the new ones.

Updated HBS sampling design does not use substitution in case of non-response.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Main Household Questionnaire and the Household Diary are used to collect information within the HBS.

The Household Income Expenditure Survey (HIES) collected detailed information at the household level on the following topics: education, health, employment, water and sanitary practices, household resources, grants, crime, conflicts and recent shocks to household wealth. The survey was also expected to provide reliable and policy relevant agricultural statistics and served as a baseline of information for the “Agenda for Transformation” set by the Government of Liberia. Other components of the HIES included capacity building and cross-country knowledge, sharing alongside efforts to improve survey methodologies in Liberia. Among other features were the design and implementation of a household survey that focused on the household income and expenditure that fed into Consumer Price Index (CPI) construction. The data collection exercise for the survey was conducted from January to December 2014.The survey covered 8,360 randomly selected households over the 12-month period. The objectives of the HIES were: 1. Evaluation and analysis of poverty levels and quality of life at the household level. 2. Analysis of primary indicators on economic productivity, employment, and social welfare. 3. Preparation of a 'weighting system' for a Consumer Price Index. 4. Generation of general economic (macroeconomic) indicators; e.g. estimates of national income. (Gross Domestic Product GDP) 5. Analysis of household ownership of productive assets and their linkages with household income activities.