This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at: https://data.cityofchicago.org/api/views/fwb8-6aw5/files/A5KBlegGR2nWI1jgP6pjJl32CTPwPbkl9KU3FxlZk-A?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\ECONOMIC_INDICATORS\Dataset_Description_socioeconomic_indicators_2012_FOR_PORTAL_ONLY.pdf

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2007 – 2011. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at https://data.cityofchicago.org/api/assets/8D10B9D1-CCA3-4E7E-92C7-5125E9AB46E9.

This dataset includes overall socio-economic indicators such as GDP and GDP growth rate, population, and land area (corresponding to Total Surface Area).

The Subnational Government Structure and Finance Dashboard compiles several datasets with comparable data on institutional organisation and public finance at subnational government level. It provides data for the year 2023 (or latest year available), for all 38 OECD member countries, and the average for the European Union. It includes data for the subnational government sector (for both state and local government levels).

The data is also available in PDF format and via an Interactive dashboard.

An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

Selection of indicators for measuring and monitoring socio-economic impacts of COVID-19 pandemic on economies of the Pacific region.

Find more Pacific data on PDH.stat.

This dataset contains country level economic and social measures for 183 countries. Part 1, World Tables (1980 File), contains, where available, measures of (1)population, (2)national accounts and price data for 1950, 1955, 1960 through 1977, (3)data on external trade for 1962, 1965, 1970, and 1977, (4)data on balance of payments, debt, central government finance and trade indices for 1970-1977, and (5)social data for 1960, 1970, and (estimated) 1977. More specifically, the groupings include population, GDP by industrial origin and expenditures in constant local prices and current local prices, exchange rates and indices, balance of payments and external debt ($US), central government finance in local currency, social indicators, and external trade. Part 2, World Tables (1982 File), contains data on national accounts, prices, exchange rates and population for 1960-1981. The groupings include GDP by industrial origin as well as expenditure in current local prices and constant local prices, area, population, exchange rates, and indices and savings.

The socio-economic indicators of each county from 1992 to 2019 were derived from the " Compendium of Socio-economic Statistics of Counties (Cities) in China (2000)", "China County Statistical Yearbook (2001-2013)", and "China Statistical Yearbook (County-level) (2014-2020)"

Analysis of ‘Socio-Economic Country Profiles’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nishanthsalian/socioeconomic-country-profiles on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

There can be multiple motivations for analyzing country specific data, ranging from identifying successful approaches in healthcare policy to identifying business investment opportunities, and many more. Often, all these various goals would have to analyze a substantially overlapping set of parameters. Thus, it would be very good to have a broad set of country specific indicators at one place.

This data-set is an effort in that direction. Of-course there are still plenty more parameters out there. If anyone is interested to integrate more parameters to this dataset, you are more than welcome.

Content

This dataset contains about 95 statistical indicators of the 66 countries. It covers a broad spectrum of areas including

General Information Broader Economic Indicators Social Indicators Environmental & Infrastructure Indicators Military Spending Healthcare Indicators Trade Related Indicators e.t.c.

This data-set for the year 2017 is an amalgamation of data from SRK's Country Statistics - UNData, Numbeo and World Bank.

The entire data-set is contained in one file described below:

soci_econ_country_profiles.csv - The first column contains the country names followed by 95 columns containing the various indicator variables.

Acknowledgements

This is a data-set built on top of SRK's Country Statistics - UNData which was primarily sourced from UNData.

Additional data such as "Cost of living index", "Property price index", "Quality of life index" have been extracted from Numbeo and a number of metrics related to "trade", "healthcare", "military spending", "taxes" etc are extracted from World Bank data source. Given that this is an amalgamation of data from three different sources, only those countries(about 66) which have sufficient data across all the three sources are considered.

Please read the Numbeo terms of use and policieshere Please read the WorldBank terms of use and policies here Please read the UN terms of use and policies here

Photo Credits : Louis Maniquet on Unsplash

--- Original source retains full ownership of the source dataset ---

Get statistical data for rural and urban Ontario on key socioeconomic variables. The data identifies: * demographic information for rural and urban Ontario by census year (population change and age breakdown, immigrants, visible minorities, educational attainment, average household income) * economic indicators for rural and urban Ontario (monthly and annual employment by industry, monthly and annual labour force characteristics, number of businesses by industries and employee number) * rural and urban Ontario census profiles (for all census of population variables starting with 2001) Find more resources with socioeconomic data and information about Rural Ontario

Abstract

Socio-Economic Conditions Survey 2020 is a key Palestinian official statistical aspects; it also falls within the mandate of the Palestinian Central Bureau of Statistics (PCBS) to provide updated statistical data on the society conditions and provide data on the most important changes in socio-economic indicators and its trends. The survey came in response to users' needs for social and economic statistical data, and in line with the national policy agenda and the sustainable development agenda. The indicators of Socio-Economic Conditions Survey 2020 covers many socio-economic and environmental aspects, and establishes a comprehensive database on those indicators. its coverage of a set of sustainable development indicators that are considered as a national and international entitlement. The objective of this survey is to provide a comprehensive database on the most important changes that have taken place in the system of social and economic indicators that PCBS works on, which covers many socio-economic and environmental indicators. It also responds to the needs of many partners and users.The indicators that have been worked on in this survey cover the Demographic characteristics of household members, Characteristics of the housing unit where household lives, Household income, expenses, and consumption, Agricultural and economic activities of households, Methods used by households to withstand and adapt to their economic conditions, Availability of basic services to Palestinian households, Assistance received by households and assessment of such assistance, the needs of the Palestinian households to be able to withstand the conditions, the reality of the Palestinian individual's suffering and the quality of life, Sustainable development objectives. for the survey's relevant indicators.

Geographic coverage

National level: State of Palestine. Region level: (West Bank, and Gaza Strip).

Analysis unit

Households, and individuals

Universe

The target population includes all Palestinian households and individuals with regular residency in Palestine during the survey's period (2020). The focus was given to individuals aged 18 years and above to complete an annex to the questionnaire, designed for this age group.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Sample of the survey is a three-stage stratified cluster systematic random sample of households residing in Palestine

Sampling Framework The sampling frame consist of the Rule of Law and Access to Justice Survey in Palestine 2018 which originally based on the list of enumeration areas of the Population, Housing and Establishments Census 2017, with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first sampling selection stage.

Sample Size 3,623 families were reached at the national level, 2,461 households in the West Bank, and 1,162 households in the Gaza Strip. These households were contacted using the phone, 3,122 households responded to the survey.

Sample Design Three-stage stratified cluster systematic random sample:

Stage I: Selection of a stratified cluster systematic random sample consisting of (161) enumeration areas. Stage II: Selection (09-25) households from each enumeration area in the first stage in a stratified cluster systematic random. (Lists of the heads of households).

Stage III: A male and female member of each household in stage II were selected for among members aged 18 years and above, using Kish (multivariate) tables to fill in the questionnaire for household members aged 18 years and above. Taking into account that the household whose number is an even number in the sample of the enumeration area, we choose a female and the family whose number is an odd number we choose a male.

In Jerusalem (j1) area, a survey sample of 25 households is selected from each enumeration area in the first stage.

Sample Strata The population was divided into the following strata: 1. Governorate (16 Governorates in the West Bank including those parts of Jerusalem, which were annexed by Israeli occupation in 1967 (J1) as a separated stratum, and the Gaza Strip). 2. Locality type (urban, rural, camp). 3. Area C (class C, non-C) as an implicit stratum.

Domains 1. Region level: (North of the West Bank, Middle of the West Bank and South of the West Bank). 2. The location of the Annexation wall and Isolation (inside the wall, outside the wall). 3. Locality type (urban, rural, camp). 4. Refugee status (refugee, non-refugee). 5. Sex (male, female). 6. Area C (class C, non-C).

Sampling deviation

There are no deviations in the proposed sample design

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is the key tool for data collection. It must be conforming to the technical characteristics of fieldwork to allow for data processing and analysis. The survey questionnaire comprised the following parts:

· Part one: Identification data. · Part two: Quality control · Part three: Data of households' members and social data. · Part four: Housing unit data · Part five: Assistance and Coping Strategies Information · Part six: Expenditure and Consumption · Part seven: Food Variation and Facing Food Shortage · Part eight: Income · Part nine: Agricultural and economic activities. · Part ten: Freedom of mobility · In addition to a questionnaire for individuals (18 years old and above): it includes questions related to the Food Insecurity Experience Scale (FIES), assessment of health, education, administration (Ministry of the Interior) services, and tobacco use.

The language used in the questionner is Arabic with an English questionner

Cleaning operations

Data processing was done in different ways including:

Programming Consistency Check 1. Tablet applications were developed in accordance with the questionnaire's design to facilitate collection of data in the field. The application interfaces were made user-friendly to enable fieldworkers collect data quickly with minimal errors. Proper data entry tools were also used to concord with the question including drop down menus/lists. 2. The application was examined by all members of the technical committee, and all comments were modified in addition to updates, and the transition between questions. It was also ensured that all audit rules were applied to the survey program, and the final version of the application was provided on time. 3. Develop automated data editing mechanism consistent with the use of technology in the survey and uploading the tools for use to clean the data entered into the database and ensure they are logic and error free as much as possible. The tool also accelerated conclusion of preliminary results prior to finalization of results. 4. In order to work in parallel with Jerusalem (J1) in which the data was collected in paper, the same application that was designed on the tablets was used to enter their data as the software was downloaded on the devices after the completion of the editing of the questionnaires.

Data Cleaning 1. Concurrently with the data collection process, a weekly check of the data entered was carried out centrally and returned to the field for modification during the data collection phase and follow-up. The work was carried out thorough examination of the questions and variables to ensure that all required items are included, and the check of shifts, stops and range was done too. 2. Data processing was conducted after the fieldwork stage, where it was limited to conducting the final inspection and cleaning of the survey databases. Data cleaning and editing stage focused on: · Editing skips and values allowed. · Checking the consistency between different the questions of questionnaire based on logical relationships. · Checking on the basis of relations between certain questions so that a list of non-identical cases was extracted, and reviewed toward identifying the source of the error case by case, where such errors were immediately modified and corrected based on the source of the error with the documentation process for the checks occurred on the questionnaire. 3. The SPSS program was used to extract and modify errors and discrepancies, to prepare clean and accurate data ready for scheduling and publishing.

Tabulation After finishing from checking and cleaning any errors of data, tabulation was prepared for this purpose and extracted accordingly.

Response rate

3,623 representative households was reached. Number of responded households (3,122) including (2,104) in the West Bank and (1,018) in Gaza Strip. Weights were adjusted with the design strata to compensate for the rate of refusal and non-response.

Sampling error estimates

Those errors result from studying part (sample) of the society and not all society units. Since the socio-economic conditions survey 2020 was conducted on a sample, sampling errors are expected to occur. To minimize sampling errors, a properly designed probability sample was used to calculate errors throughout the process. This means that for every unit of the society there is a probability to be selected in the sample. The variance was calculated to measure the impact on sample design for Palestine.

Data appraisal

This standard is linked to the statistical product, since statistics must have comparative advantage with other sources and with other time periods. Many analyses are based on comparison. The data of the survey of 2020 were compared to the previous

This table provides statistics on Family Composition, Family Income, Housing Mobility, Language, Immigration, Educational Attainment, Household and Dwelling Characteristics for selected indicators. This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer - North, Calgary - West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. The Federal Census (2016) and National Household Survey (2016) information is custom extracted by Statistics Canada at the local geographic area level. The population of these areas varies from very small in rural areas to large in metropolitan centers. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published August 2022.

Socio-economic statistics data for China's poverty assessment and analysis under the framework of the UN SDGs

Available data for gross domestic product (GDP) and population density are useful for defining divisions in socio-economic gradients across Europe, since economic power and human population pressure are recognised as two of the most critical factors causing ecosystem changes. To overcome both the limitations in data availability and in the distortions caused by using administrative regions, we decided to base the socio-economic dimension on an economic density indicator, defined as the income generated per square kilometre (EUR km-2), which can be mapped at a 1km2 spatial resolution. Economic density forms an integrative indicator that is based on two key drivers that were identified above: economic power and human population pressure. The indicator, which has been used to rank countries by their level of development, can be considered a crude measure for impacts on the environment caused by economic activity. An economic density map (EUR km-2) at 1 km2 spatial resolution was constructed by multiplying economic power (EUR person-1) with population density (person km-2). Subsequent logarithmic divisions resulted in an aggregated map of four economic density zones. Although the map has a fine spatial resolution it has to be realised that they form a spatial disaggregation of coarser census statistics. Importantly, the finer resolution discerns regional gradients in human activity that are required for many environmental studies, whilst broad gradients in economic activity is also treated consistently across Europe. GDP and population density data used were for the year 2001. The dataset consists of GeoTiff files of the economic density map and the four economic density zones.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

Abstract

Socio-Economic Conditions Survey 2018 is a key Palestinian official statistical aspects; it also falls within the mandate of the Palestinian Central Bureau of Statistics (PCBS) to provide updated statistical data on the society conditions and provide data on the most important changes in socio-economic indicators and its trends. The survey came in response to users' needs for social and economic statistical data, and in line with the national policy agenda and the sustainable development agenda. The indicators of Socio-Economic Conditions Survey 2018 covers many socio-economic and environmental aspects, and establishes a comprehensive database on those indicators. its coverage of a set of sustainable development indicators that are considered as a national and international entitlement. The objective of this survey is to provide a comprehensive database on the most important changes that have taken place in the system of social and economic indicators that PCBS works on, which covers many socio-economic and environmental indicators. It also responds to the needs of many partners and users.The indicators that have been worked on in this survey cover the Demographic characteristics of household members, Characteristics of the housing unit where household lives, Household income, expenses, and consumption, Agricultural and economic activities of households, Methods used by households to withstand and adapt to their economic conditions, Availability of basic services to Palestinian households, Assistance received by households and assessment of such assistance, the needs of the Palestinian households to be able to withstand the conditions, the reality of the Palestinian individual's suffering and the quality of life, Sustainable development objectives. for the survey's relevant indicators.

Geographic coverage

National level: State of Palestine. Region level: (West Bank, and Gaza Strip).

Analysis unit

Households, and individuals

Universe

The target population includes all Palestinian households and individuals with regular residency in Palestine during the survey's period (2018)

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling and Frame The Sample of the survey is a three-stage stratified cluster systematic random sample of households residing in Palestine. Target Population The target population includes all Palestinian households and individuals with regular residency in Palestine during the survey's period (2018). Focus was given to individuals aged 18 years and above to complete an annex to the questionnaire, designed for this age group. Sampling Framework In previous survey rounds, sampling was based on census 2007, which includes a list of enumeration areas. An enumeration area is a geographic region with buildings and housing units averaging 124 housing units. In the survey design, they are considered as Primary Sampling Units (PSUs) at the first stage of selecting the sample. Enumeration areas of 2007 were adapted to the enumeration areas of 2017 to be used in future survey rounds. Target sample buildings were set up in 2015 electronically by using Geographic Information Systems (GIS), where the geospatial join tool was used within Arc Map 10.6 to identify the buildings selected in the first stage of the sample design of 8,225 households taken from the general frame buildings for enumeration areas of 2007 which falls within the boundaries of enumeration areas that were updated during the population, housing and establishments census 2017. Only the buildings for the year 2017 were used to link the sites of the sample buildings to the targeted enumeration areas, to ensure tracking households that moved after 2015. Sample Size The survey sample comprised 11,008 households at the total level, where 9,926 households responded, they are divided as follows: 1. Fixing the sample of the survey on the Impact of Israeli Aggression on Gaza Strip in 2014 and Socio-Economic Conditions of the Palestinian Households - Main Findings, which was conducted in 2015, with a sample of 8,225 households in the previous round (household-panel),where 7,587 households responded. 2. Sample of new households that consisted of separated individuals (split households) totaled 2,783 households, where 2,339 households responded. Sample Design Three-stage stratified cluster systematic random sample: Stage I: Selection of enumeration areas represented in the previous round of the survey on the socioeconomic conditions 2015 including 337 enumeration areas, in addition to enumeration areas in which individuals separated from their households and formed new households and households that changed their place of residence and address to other enumeration areas. Stage II: Visit the same households from previous round of survey on socioeconomic conditions 2015(25 households in each enumeration area). Households that changed their place of residence or registered address will be tracked in the existing database to search for the updated data registered in questionnaire. Individuals separated from their households from the previous round and formed new households or joined new households were tracked. Stage III: A male and female member of each household in the sample (old and new) were selected for stage III among members aged 18 years and above, using Kish (multivariate) tables to fill in the questionnaire for household members aged 18 years and above. Taking into account that the household whose number is an even number in the sample of the enumeration area, we choose a female and the family whose number is an odd number we choose a male. Sample Strata The population was divided into the following strata: 1. Governorate (16 Governorates in the West Bank including those parts of Jerusalem, which were annexed by Israeli occupation in 1967 (J1) as a separated stratum, and the Gaza Strip). 2. Locality type (urban, rural, camp). 3. Area C (class C, non-C) as an implicit stratum. Domains 1. National level: State of Palestine. 2. Region level: (West Bank, and Gaza Strip). 3. Governorate (16 Governorates in the West Bank including those parts of Jerusalem, which were annexed by Israeli occupation in 1967, and Gaza Strip). 4. The location of the Annexation wall and Isolation (inside the wall, outside the wall). 5. Locality type (urban, rural, camp). 6. Refugee status (refugee, non-refugee). 7. Sex (male, female). 8. Area C (class C, non-C).

Sampling deviation

There are no deviations in the proposed sample design

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is the key tool for data collection. It must be conforming to the technical characteristics of fieldwork to allow for data processing and analysis. The survey questionnaire comprised the following parts: · Part one: Identification data. · Part two: Quality control · Part three: Data of households' members and social data. · Part four: Housing unit data · Part five: Assistance and Coping Strategies Information · Part six: Expenditure and Consumption · Part seven: Food Variation and Facing Food Shortage · Part eight: Income · Part nine: Agricultural and economic activities. · Part ten: Freedom of mobility · In addition to a questionnaire for individuals (18 years old and above): Questions on suffering and life quality, assessment of health, education, administration (Ministry of the Interior) services and information technology.

The language used in the questionner is Arabic with an English questionner

Cleaning operations

Data Processing Data processing was done in different ways including: Programming Consistency Check 1.Tablet applications were developed in accordance with the questionnaire's design to facilitate collection of data in the field. The application interfaces were made user-friendly to enable fieldworkers collect data quickly with minimal errors. Proper data entry tools were also used to concord with the question including drop down menus/lists. 2.Develop automated data editing mechanism consistent with the use of technology in the survey and uploading the tools for use to clean the data entered into the database and ensure they are logic and error free as much as possible. The tool also accelerated conclusion of preliminary results prior to finalization of results. 3.GPS and GIS were used to avoid duplication and omission of counting units (buildings, and households). In order to work in parallel with Jerusalem (J1) in which the data was collected in paper, the same application that was designed on the tablets was used and some of its properties were modified, there was no need for maps to enter their data as the software was downloaded on the devices after the completion of the editing of the questionnaires Data Cleaning 1.Concurrently with the data collection process, a weekly check of the data entered was carried out centrally and returned to the field for modification during the data collection phase and follow-up. The work was carried out thorough examination of the questions and variables to ensure that all required items are included, and the check of shifts, stops and range was done too. 2.Data processing was conducted after the fieldwork stage, where it was limited to conducting the final inspection and cleaning of the survey databases. Data cleaning and editing stage focused on: ·Editing skips and values allowed. ·Checking the consistency between different the questions of questionnaire based on logical relationships. ·Checking on the basis of relations between certain questions so that a list of non-identical cases was extracted,

This dataset enables studying the relationship between a country's economic and social factors — such as GDP per capita, government health expenditure, Human Development Index (HDI), World Happiness Index, and density of doctors per population — with several key health indicators, like alcohol consumption, life expectancy, child mortality, non-communicable disease mortality, obesity prevalence, and undernourishment rates. It covers 50 countries from 2002 to 2021.A dashboard is also provided to facilitate the study, including plots for comparison of any selected variables for any of the available years, countries and geographic regions.

This dataset and dashboard has been created as part of a data management project for university IQS, Ramon Llull.

Dataset accompanying the paper: Schild, J.E.M., Vermaat, J.E., de Groot, R.S., Quatrini, S., van Bodegom, P.M., 2018a. A global meta-analysis on the monetary valuation of dryland ecosystem services: The role of socioeconomic, environmental and methodological indicators. Ecosyst. Serv. 32. doi:10.1016/j.ecoser.2018.06.004

Sources: Stats SA (www.statssa.gov.za), *SAPS (www.saps.gov.za).

As part of the research work "Altai vector of Eurasian economic integration: cross-border challenges, effects, strategic objectives and priorities for the Altai Krai" (FZMW-2023-0015), the task was to develop a database of indicators of development of cross-border regions of Russia, Kazakhstan, Mongolia and China for the period 1990-2022. To solve the task, statistical data from official sources were used. The database contains 68 indicators of socio-economic development of the Great Altai region, grouped into 10 structural blocks. The temporal resolution of the data is 1 year. The data were preliminarily analyzed for outliers and inconsistencies. On the basis of econometric methods, processing was performed to restore short-term gaps in the time series of the data. The generated database represents a unique set of socio-economic and climatic indicators for the transboundary study area, allowing to solve the problems of modeling and analysis of agricultural production dynamics in the post-Soviet period.

According to Ipsos Consolidated Economic Indicators based on monthly surveys conducted by Ipsos, the national index score for Japan stood at **** points in April 2020, down from **** points in the previous month. The index reflects respondent perceptions of current and future local economy, current and future financial situation, major purchase comfort, household purchase comfort, job security, investment confidence, job loss experience, and job loss expectations in their country.