These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The study includes data collected with the purpose of creating an integrated dataset that would allow researchers to address significant, policy-relevant gaps in the literature--those that are best answered with cross-jurisdictional data representing a wide array of economic and social factors. The research addressed five research questions: What is the impact of gentrification and suburban diversification on crime within and across jurisdictional boundaries? How does crime cluster along and around transportation networks and hubs in relation to other characteristics of the social and physical environment? What is the distribution of criminal justice-supervised populations in relation to services they must access to fulfill their conditions of supervision? What are the relationships among offenders, victims, and crimes across jurisdictional boundaries? What is the increased predictive power of simulation models that employ cross-jurisdictional data?

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

Analysis of ‘Socioeconomic Status of CDC Social Vulnerability Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a70b39b7-1151-42dd-b333-d8923d20f22a on 28 January 2022.

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

Socioeconomic Status is one of the themes of the Social Vulnerability Index from the CDC. This data set is data from the Social Vulnerability Index. Only the Socioeconomic Status columns are represented in this dataset. Years of data in collection 2014, 2016, 2018

ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.

In addition to tract-level rankings, SVI 2018 also has corresponding rankings at the county level. Notes below that describe “tract” methods also refer to county methods.

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

Analysis of ‘Socioeconomic Status Social Vulnerability Index 2014-2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2c5de914-693a-405e-80f7-5abb6b7dd3d3 on 28 January 2022.

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

Socioeconomic Status is one of the themes of the Social Vulnerability Index from the CDC. This data set is data from the Social Vulnerability Index. Only the Socioeconomic Status columns are represented in this dataset.

ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.

In addition to tract-level rankings, SVI 2018 also has corresponding rankings at the county level. Notes below that describe “tract” methods also refer to county methods.

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

Data on county socioeconomic status for 2,132 US counties and each county’s average annual cardiovascular mortality rate (CMR) and total PM2.5 concentration for 21 years (1990-2010). County CMR, PM2.5, and socioeconomic data were obtained from the U.S. National Center for Health Statistics, U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system, and the U.S. Census, respectively. A socioeconomic index was created using seven county-level measures from the 1990 US census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This dataset is associated with the following publication: Wyatt, L., G. Peterson, T. Wade, L. Neas, and A. Rappold. The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 136: 105430, (2020).

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

BackgroundChildren’s quality of life, academic performance, and future achievement can all be negatively affected by poor dental health. The present study aimed to assess the need for dental health services and the factors influencing their utilization using the Andersen health care utilization model among school children.MethodsThe current cross-sectional study was conducted among schoolchildren aged 13 to 15 in Bangalore, India (n = 1100). A questionnaire was developed using the concepts of the Andersen healthcare usage model. The parents of the children filled out the questionnaire. The factors were investigated using bivariate analysis and multivariate logistic regression analysis.ResultsAbout 78.1% of the children did not utilize dental health services. Regarding the reasons for not visiting a dentist, 65.8% said they did not have a dental problem, and 22.2% said they could not afford it. Bivariate analysis showed that age, gender, education level, occupation of the family’s head of household, monthly family income, socioeconomic status, perceived oral health problems, accessibility of dental health facilities, and parental attitudes toward their children’s oral health were significantly associated with using dental health services (p<0.05). Multiple regression analysis showed dental health service utilization was directly related to age (OR = 2.206), education, family size (OR = 1.33), and brushing frequency twice a day (OR = 1.575) with no significant relationship between distance to reach the dental facility, the number of dental visits, and socioeconomic status.ConclusionDental health service utilization was low in the past year. The age, number of family members, parent’s education level, travel time to the dental facility, the child’s oral health behaviors, and positive parental attitude all play a role in a children’s utilization of dental health service.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

This paper presents the largest globally comparable panel database of education quality. The database includes 163 countries and regions over 1965-2015. The globally comparable achievement outcomes were constructed by linking standardized, psychometrically-robust international and regional achievement tests. The paper contributes to the literature in the following ways: (1) it is the largest and most current globally comparable data set, covering more than 90 percent of the global population; (2) the data set includes 100 developing areas and the most developing countries included in such a data set to date -- the countries that have the most to gain from the potential benefits of a high-quality education; (3) the data set contains credible measures of globally comparable achievement distributions as well as mean scores; (4) the data set uses multiple methods to link assessments, including mean and percentile linking methods, thus enhancing the robustness of the data set; (5) the data set includes the standard errors for the estimates, enabling explicit quantification of the degree of reliability of each estimate; and (6) the data set can be disaggregated across gender, socioeconomic status, rural/urban, language, and immigration status, thus enabling greater precision and equity analysis. A first analysis of the data set reveals a few important trends: learning outcomes in developing countries are often clustered at the bottom of the global scale; although variation in performance is high in developing countries, the top performers still often perform worse than the bottom performers in developed countries; gender gaps are relatively small, with high variation in the direction of the gap; and distributions reveal meaningfully different trends than mean scores, with less than 50 percent of students reaching the global minimum threshold of proficiency in developing countries relative to 86 percent in developed countries. The paper also finds a positive and significant association between educational achievement and economic growth. The data set can be used to benchmark global progress on education quality, as well as to uncover potential drivers of education quality, growth, and development.

blockgroupvulnerability OPPORTUNITY The US Centers for Disease Control (CDC) publishes a set of percentiles that compare US geographies by vulnerability across household, socioeconomic, racial/ethnic and housing themes. These Social Vulnerability Indexes (SVI) were originally intended to to help public health officials and emergency response planners identify communities that will need support around an event. They are generally valuable for any public interest that wants to relate themselves to needy communities by geography. The SVI publication and its basis variables are provided at the Census tract level of geographic detail. The Census' American Community Survey is available down the to the block group level, however. Recasting the SVI methods at this lower level of geography allows it to be tied to thousands of other demographic variables available. Because the SVI relies on ACS variables only available at the tract level, a projection model needs to applied to approximate its results using blockgroup level ACS variables. The blockgroupvulnerability dataset casts a prediction for the CDCs logic for a new contribution to the Open Environments blockgroup series available on Harvard's dataverse platform. DATA The CDC's annual SVI publication starts with 23 simple derivations using 50 ACS Census variables. Next the SVI process ranks census geographies to calculate a rank for each, where Percentile Rank = (Rank-1) / (N-1). The SVI themes are then calculated at the tract level as a percentile rank of a sum of the percentile ranks of the first level ACS derived variables. Finally, the overall ranking is taken as the sum of the theme percentile rankings. The SVI data publication is keyed by geography (7 cols) where ultimately the Census Tract FIPS code is 2 State + 3 County + 4 Tract + 2 Tract Decimals eg, 56043000301 is 56 Wyoming, 043 Washakie County, Tract 3.01 republishes Census demographics called 'adjunct variables' including area, population, households and housing units from the ACS daytime population taken from LandScan 2020 estimates derives 23 SVI variables from 50 ACS 5 Year variables with each having an estimate (E_), estimate precentage (EP_), margin of error (M_), margin percentage (MP_) and flag variable (F_) for those greater than 90% or less than 10% provides the final 4 themes and a composite SVI percentile annually vars = ['ST', 'STATE', 'ST_ABBR', 'STCNTY', 'COUNTY', 'FIPS', 'LOCATION'] +\ ['SNGPNT','LIMENG','DISABL','AGE65','AGE17','NOVEH','MUNIT','MOBILE','GROUPQ','CROWD','UNINSUR','UNEMP','POV150','NOHSDP','HBURD','TWOMORE','OTHERRACE','NHPI','MINRTY','HISP','ASIAN','AIAN','AFAM','NOINT'] +\ ['TOTAL','THEME1','THEME2','THEME3','THEME4'] + \ ['AREA_SQMI', 'TOTPOP', 'DAYPOP', 'HU', 'HH'] knowns = vars + \ # Estimates, the result of calc against ACS vars [('E_'+v) for v in vars] + \ # Flag 0,1 whether this geog is in 90 percentile rank (its vulnerable) [('F_'+v) for v in vars] +\ # Margine of error for ACS calcs [('M_'+v) for v in vars] + \ # Margine of error for ACS calcs, as percentage [('MP_'+v) for v in vars] +\ # Estimates of ACS calcs, as percentage [('EP_'+v) for v in vars] + \ # Estimated percentile ranks [('EPL_'+v) for v in vars] + \ # Sum across var percentile ranks [('SPL_'+v) for v in vars]+ \ # Percentile rank of the sum of percentile ranks [('RPL_'+v) for v in vars] [c for c in svitract.columns if c not in knowns] The SVI themes range over [0,1] but the CDC uses -999 as an NA value; this is set for ~800 or 1% of tracts which have no total poulation. The themes are numbered: Socioeconomic Status – RPL_THEME1 Household Characteristics – RPL_THEME2 Racial & Ethnic Minority Status – RPL_THEME3 Housing Type & Transportation – RPL_THEME4 The themes with their variables and ACS sources are as follows: Unlike Census data, the CDC ranks Puerto Rico and Tribal tracts separately from the US otherwise. Theme SVI Variable ACS Table ACS Variables Socioeconomic E_UNINSUR S2701 S2701_C04_001E Socioeconomic E_UNEMP DP03 DP03_0005E Socioeconomic E_POV150 S1701 S1701_C01_040E Socioeconomic E_NOHSDP B06009 B06009_002E Socioeconomic E_HBURD S2503 S2503_C01_028E + S2503_C01_032E + S2503_C01_036E + S2503_C01_040E Household E_SNGPNT B11012 B11012_010E + B11012_015E Household E_LIMENG B16005 B16005_007E + B16005_008E + B16005_012E + B16005_013E + B16005_017E + B16005_018E + B16005_022E + B16005_023E + B16005_029E + B16005_030E + B16005_034E + B16005_035E + B16005_039E + B16005_040E + B16005_044E + B16005_045E Household E_DISABL DP02 DP02_0072E Household E_AGE65 S0101 S0101_C01_030E Household E_AGE17 B09001 B09001_001E Racial & Ethnic E_TWOMORE DP05 DP05_0083E Racial & Ethnic E_OTHERRACE DP05 DP05_0082E Racial & Ethnic E_NHPI DP05 DP05_0081E Racial & Ethnic E_MINRTY DP05 DP05_0071E + DP05_0078E + DP05_0079E + DP05_0080E + DP05_0081E + DP05_0082E + ... Visit https://dataone.org/datasets/sha256%3A3edd5defce2f25c7501953ca3e77c4f15a8c71251352373a328794f961755c1c for complete metadata about this dataset.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Personal Well-Being, April 2011 - March 2014 The ONS have released a combined three-year Personal Well-Being dataset covering April 2011 to March 2014 in response to user demand. The enhanced sample size of this dataset allows for more robust analysis of sub groups in the population and for local areas. Further information may be found in the documentation. The SL access version of the APS April 2011 - March 2014 Personal Well-Being dataset is held under SN 7593. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2011 2014 ACADEMIC ACHIEVEMENT ADULT EDUCATION AGE ANXIETY APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS CHRONIC ILLNESS COHABITATION CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES EMPLOYMENT SERVICES ETHNIC GROUPS FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER HAPPINESS HEADS OF HOUSEHOLD HEALTH HEALTH STATUS HIGHER EDUCATION HOME BASED WORK HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LONGTERM UNEMPLOYMENT Labour and employment MANAGERS MARITAL STATUS MATERNITY BENEFITS NATIONAL IDENTITY NATIONALITY OCCUPATIONAL TRAINING OCCUPATIONS OLD AGE BENEFITS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECREATIONAL EDUCATION RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICK PAY SICKNESS AND DISABI... SMOKING SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELL BEING SOCIETY WORKING CONDITIONS WORKPLACE vital statistics an...

Food insecurity problems still exist among people in low-to-middle income countries. The long-term disadvantages of socioeconomic status may contribute to chronic food insecurity. However, whether childhood socioeconomic status factors are related to food insecurity in adulthood remains unclear. Thus, the aim of this study was to test the association between childhood socioeconomic status factors and one of the proxies for adulthood food security, dietary diversity. This study used the 2014 RAND Indonesia Family Life Survey dataset with 22,559 adult participants as study samples. The childhood socioeconomic status factors consisted of 16 questions about the participants’ conditions when they were 12 years old. Adult dietary diversity was assessed using the United Nations World Food Programme’s food consumption score. A linear regression model was used to analyze the association between variables. This study found that the number of owned books (β coef.: 3.713–7.846, p < 0.001), the use of safe drinking-water sources (β coef.: 0.707–5.447, p < 0.001–0.009) and standard toilets (β coef.: 1.263–4.955, p < 0.001–0.002), parents with the habit of alcohol consumption (β coef.: 2.983, p = 0.044) or the combination with smoking habits (β coef.: 1.878, p < 0.001), self-employed with the permanent worker (β coef.: 2.904, p = 0.001), still married biological parents (β coef.: 1.379, p < 0.001), the number of rooms (β coef.: 0.968, p < 0.001), people (β coef.: 0.231, p < 0.001), and younger siblings (β coef.: 0.209–0.368, p < 0.001–0.039) in the same house were positively and significantly associated with the outcome variable. Furthermore, in the order of childhood socioeconomic status factors, self-employment without permanent workers and casual work types (β coef.: –9.661 to –2.094, p < 0.001–0.001), houses with electricity facilities (β coef.: –4.007, p < 0.001), and parents with smoking habits (β coef.: –0.578, p = 0.006) were negatively and significantly associated with the food security proxy. In conclusion, childhood and early socioeconomic disadvantage is related to adult food security status and may lead to poor health.

Background: The positive learning effects of academic risk taking (ART) in higher education has been discussed since the 1980's. However, this may not apply equally for all social groups. Men and women may differ in the way they use ART to construct their gender identity. Students with different socioeconomic status (SES) may differ in their ability to navigate academic risks due to differences in available cultural capital. Aims: This study examines gender and SES disparities in ART and their impact on learning success. It explores if ART mediates and is moderated by gender and SES effects. Additionally, it assesses if ART directly predicts learning success. Sample: A sample of N = 381 German university students was used. Methods: Data was analyzed following a structural equation modeling approach. Results: Men show more ART on the seminar group dimension, whereas women show more ART on the peer dimension. Being male indirectly predicts higher learning success via the seminar group dimension of ART. Furthermore, SES and gender moderate the effect between ART and learning success. Both ART dimensions directly predict students’ learning success. Conclusions: Our research contributes to understanding the mechanisms of social disparities within higher education and offers implications for the development of inclusive teaching strategies and research on aspects of intersectionality.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2021 2023 ADULT EDUCATION AGE APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESSES CARE OF DEPENDANTS CHRONIC ILLNESS COHABITATION COMMUTING CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS FAMILIES FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER HEADS OF HOUSEHOLD HEALTH HIGHER EDUCATION HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS Labour and employment MANAGERS MARITAL STATUS NATIONAL IDENTITY NATIONALITY OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the second edition (October 2024), smoking variables CIGEVER, CIGNOW and CIGSMK16 have been added to the data file. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2023 ADULT EDUCATION AGE ANXIETY APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESSES CARE OF DEPENDANTS CHRONIC ILLNESS COHABITATION CONDITIONS OF EMPLO... COVID 19 DEBILITATIVE ILLNESS DEGREES DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS FAMILIES FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER HAPPINESS HEADS OF HOUSEHOLD HEALTH HIGHER EDUCATION HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS Labour and employment MANAGERS MARITAL STATUS NATIONAL IDENTITY NATIONALITY OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELL BEING HEALTH WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

Weather Conditions and Economic Analysis

Investigating the Relationship between Weather and Economic Activity

By [source]

About this dataset

This dataset consists of detailed information about the weather conditions in different cities from one of the official weather websites. It includes several variables including temperature, humidity, pressure, wind speed and direction, precipitation levels, cloud cover etc. which can be used to analyze the correlation between economic activities in these cities and their weather conditions. For example, this data can help us understand how certain types of business like tourism, retail or leisure activities are affected by changes in temperature and humidity levels. Additionally, it allows us to identify which specific kind of weather has more economic impact in a certain region and thus create accurate forecasts which could further improve commercial performances. All in all, this dataset is an invaluable source of information for people interested in understanding the relation between climate dynamics and economic outcomes

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How to use the dataset

• City Name: This column provides the name of the cities covered in this dataset.
• Weather Condition: This column lists the weather conditions associated with each city, such as sunny, cloudy, windy, etc.
• Temperature (C): This column provides the temperature (in Celsius) of each city as provided by official weather sources.
• Population: This column lists the population size (in millions) of each city covered in this dataset.
• GDP Per Capita: This column presents GDP per capita (measured in US Dollars) for each city included in our dataset 6 Economic Activity Index: This index measures economic activity levels for a particular state or region and can be used to analyze how different weather conditions affect economic activities such as tourism, retail, and leisure activities

How to use this dataset?

This dataset can be used to explore relationships between different factors that might influence economic activity levels at a regional level—namely population size and wealth as well as weather condition—or across countries over time and certain seasons or months to identify trends in regional differences between regions regarding their respective economics activities levels due to varying climates or meteorological events . Some specific analysis that could be done includes:

• Use City Name & Weather Condition columns together to calculate correlations between types of weather patterns/conditions seen throughout different locales; temperatures could also potentially be included for more comprehensive data exploration/analysis on climate dynamics - research on how “cold” vs “warm” periods affect local economies overall would also benefit from including these two columns together;

• Analyze Population & Economic Activity Index together - use these variables together to see if any correlation exists between populations sizes within a given region versus their respective economic performance level; other related variables such as GDP Per Capita could also potentially provide valuable insight into how economic activity varies depending on population density;

• Using all 6 columns together would enable even more comprehensive analysis e..g comparing temperatures & storm information versus expected tourist visits data or analyzing effects/correlations between strong winds & droughts versus changes seen within agricultural outputs . With careful combination of all 6 columns you could easily create some interesting models & computations for understanding broad implications which climate dynamics have upon global economics ; conversely you may explore individual cities too!

Research Ideas

• Use this dataset to analyze the correlation between weather conditions and consumer sentiment by comparing customer purchasing decisions in different cities under different weather conditions.
• Use this dataset to identify the optimal temperature for selling certain products, so that retailers can optimize their prices accordingly.
• Use this dataset to study how changes in weather influencers the types of transportation used by the population of a certain city, and help suggest improvements to public systems for better customer experience in changing climate situations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

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Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

The Armenian Household Budget Survey (HBS) 1996 was designed to be a nationally representative survey capable of measuring the standard of living in the Republic of Armenia (ROA) through the collection of data on the family, demographic, socio-economic and financial status of households. The survey was conducted in November - December 1996, on the whole territory of the republic by the State Department of Statistics (SDS) of ROA with technical and financial assistance from the World Bank.The data collected included information on household composition, housing conditions, education level of household members, employment and income, savings, borrowing, as well as details on levels of expenditure including those on food, non-food, health, tourism and business. The survey covered about 100 villages and 28 towns. The size of the sample was 5,040 households of which 4,920 responded which makes the survey the largest carried out in Armenia to date and one with a very high response rate for a transition economy. The expenditure part of the data was collected using two different methods administered for different households. The methods are: recall method in which households were asked, during the interview, about their expenditures made during the last 30 days preceding the date of the interview; and a diary method where households were given a diary they used to record details about their income and expenditure on a daily basis for 30 days during the interview period. About 25% of the total sample of interviewed households used diaries and 75% used the recall method. The unit of study in the survey was the household, defined as a group of co-resident individuals with a common living budget. As will be explained in detail, the AHBS 96 was generally designed as a two stage stratified sampling, but for large urban areas with an almost definite probability of being selected, a one stage sampling was adopted.The Armenian HBS 1996 is not a standard Living Standards Measurement Study (LSMS) survey - the questionnaire used is more limited in scope and much different in format from a typical LSMS. This survey used no community or price questionnaires; it did not use most of LSMS’ prototypical fieldwork and data quality procedures, and the technical assistance did not come from the LSMS group in the World Bank. Nonetheless, the goals are some what LSMS-like and the data is certainly worth archiving. They are therefore being entered into the LSMS archives to guarantee their future accessibility to World Bank and other users.

Abstract

The World Bank and UNHCR in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform a targeted response. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets refugee household and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection, and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing. The data is uploaded in three files. The first is the hh file, which contains household level information. The 'hhid', uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'adult_id'. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the 'child_id'. The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 1,328 refugee households Wave 2: July 16 to September 18, 2020; 1,699 refugee households Wave 3: September 28 to December 2, 2020; 1,487 refugee households Wave 4: January 15 to March 25, 2021; 1,376 refugee households Wave 5: March 29 to June 13, 2021; 1,562 refugee households Wave 6: July 14 to November 3, 2021; 1,407 refugee households Wave 7: November 15, 2021, to March 31, 2022; 1,281 refugee households Wave 8: May 31 to July 8, 2022: 1,355 refugee households The same questionnaire is also administered to nationals in Kenya, with the data available in the WB microdata library: https://microdata.worldbank.org/index.php/catalog/3774

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless, where sampling approaches differ across strata. For refugees in Kakuma and Kalobeyei, as well as for stateless people, recently conducted Socioeconomic Surveys (SES), were used as sampling frames. For the refugee population living in urban areas and the Dadaab camp, no such household survey data existed, and sampling frames were based on UNHCR's registration records (proGres), which include phone numbers. For Kakuma, Kalobeyei, Dadaab and urban refugees, a two-step sampling process was used. First, 1,000 individuals from each stratum were selected from the corresponding sampling frames. Each of these individuals received a text message to confirm that the registered phone was still active. In the second stage, implicitly stratifying by sex and age, the verified phone number lists were used to select the sample. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in. For the stateless population, all the participants of the Shona socioeconomic survey (n=400) were included in the RRPS, because of limited sample size. The sampling frames for the refugee and Shona stateless communities are thus representative of households with active phone numbers registered with UNHCR.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included 12 sections Section 1: Introduction Section 2: Household background Section 3: Travel patterns and interactions Section 4: Employment Section 5: Food security Section 6: Income Loss Section 7: Transfers Section 8: Subjective welfare (50% of sample) Section 9: Health Section 10: COVID Knowledge Section 11: Household and Social Relations (50% of sample) Section 12: Conclusion

Cleaning operations

Variable names were kept constant across survey waves. For questions that remained exactly the same across survey waves, data points for all waves can be found under one variable name. For questions where the phrasing changed (even in a minimal way) across waves, variable names were also changed to reflect the change in phrasing. Extended missing values are used to indicate why a value is missing for all variables. The following extended missing values are used in the dataset: · .a for 'Don't know' · .b for 'Refused to respond' · .c for 'Outliers set to missing' · .d for 'Inconsistency set to missing' (used for employment data as explained below) · .e for 'Field Skipped' (where an error in the survey tool caused the question to be missed) · .z for 'Not administered' (as the variable was not relevant to the observation) More detailed data on children was collected between waves 3 and 7, compared to waves 1, 2 and 8. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the 'hh' data for waves 1 and 2. Between waves 3 and 7, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the 'child' data set. The household level weights can be used for analysis of the children's data. In wave 8, detailed information on children was dropped, as the questionnaire focused on other topics. The education status of household members, except for the respondent, was imputed for rounds 1 and 2. For rounds 1 and 2, only the education status of the respondent was elicited, while for later rounds the education status for each household member was asked. In order to evaluate outcomes by the household member's education status, information on education was imputed for waves 1 and 2, using the information provided for all household members in waves 3, 4, and 5. This resulted in additional information on the education status for household members in round 1 and 2, which was not yet available for earlier versions of this data. Some questions are not asked repeatedly across waves such that their values were imputed. For some questions, answers are not possible or unlikely to change within two months between survey waves such that households were not asked about them in all waves. The questions on assets owned before March 2020 were only asked to households when they are interviewed for the first time. The questions on the dwelling's wall and floor material as well as the household's connection to the power grid was not asked for all households in wave 2 and 3, where only new households and those who moved were covered by these questions. Questions on the main source of electricity in the households and types of assets owned were not asked in wave 8. The missing values those variables have when they were not asked, are imputed from the answers given in earlier waves. Improved quality insurance algorithms lead to minor revisions to wave 1 to 5 data. Based on additional data checks, the team has made minor refinements to wave 1 to 5 data. The identification of the household members that were the respondent or the household head was refined in the rare cases where it was not possible to interview the same respondent as in previous waves for a given household such that another adult was interviewed. For this reason, for about 2 percent of observations the household head status was assigned to an incorrect household member, which was corrected. For <1 percent of households the respondent did not appear in adult level dataset. For about 1 percent of observations in wave 5 the respondent appeared twice in the adult level dataset. Data from questions on COVID-19 vaccinations from wave 7 was dropped from the dataset. Due to significantly higher self-reported vaccination rates compared to official administrative records, data on vaccinations was deemed unreliable, most likely due to social desirability bias. Consequently, questions on vaccination status and questions using the vaccination data as a validation criterion were dropped from the datasets.

These datasets contain many economic variables related to agriculture like crop output value, profit and several others. These datasets can be used for testing several hypotheses related to agricultural economics, both at plot level and household level.

Users can also reproduce these datasets using the STATA 14 do file ‘VDSA data management for agricultural performance’. This STATA program file uses the Village Dynamics in South Asia (VDSA) raw data files in excel format. The resulting output will be two data files in stata format, one at plot level and other at household level.

These plot level and household level data sets are also included in this repository. The word file ‘guidelines’ contain instructions to extract VDSA raw data from VDSA knowledge bank and use them as inputs to run the STATA do file ‘VDSA data management for agricultural performance’

The VDSA raw data files in excel format needed to run the stata do file are also available in this repository for users convenience

The raw VDSA data were generated by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in partnership with Indian Council of Agricultural Research (ICAR) Institutes and the International Rice Research Institute (IRRI) and funded by the Bill & Melinda Gates Foundation (BMGF) (Grant ID: 51937). The data were acquired in surveys by resident field investigators. Data collection was mostly through paper based questionnaires and Samsung tablets were also used since 2012. The survey instruments used for different modules are available at http://vdsa.icrisat.ac.in/vdsa-questionaires.aspx

Study sites were selected using a stepwise purposive sampling covering agro-ecological diversity of the region. Three districts within each zone were selected based on soil, climate parameters as well as the share of agricultural land under ICRISAT mandate crops. On similar lines, one typical sub-district within each district and two villages within each sub-district were selected. Within each village, ten random households from four landholding groups were selected.

Selected farmers were visited by well trained, agriculture graduate, resident field investigators, once every three weeks to collect information related to various socioeconomic indicators. Some of the data modules like details on crop cultivation activities including plot wise input, output was collected every three weeks while others like general endowments were collected once at the beginning of every agricultural year.

The compiled data, source data, data descriptions and data management code are all published in a public repository at http://dataverse.icrisat.org/dataverse/socialscience at https://doi.org/10.21421/D2/HDEUKU]

Some of the several benefits of these data are:

Scientists, students, development practitioners can benefit from these data to track changes in the livelihood options of the rural poor as this data provides long-term, multi-generational perspective on agricultural, social and economic change in rural livelihoods.

The survey sites provide a socio-economic field laboratory for teaching and training students and researchers

This dataset can be used for diverse agricultural, development and socio-economic analysis and to better understand the dynamics of Indian agriculture.

The data helps to provide feedback for designing policy interventions, setting research priorities and refining technologies.

Shed light on the pathways in which new technologies, policies, and programs impact poverty, village economies, and societies