The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

About this dataset

Context

The dataset tabulates the Black Earth town Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Black Earth town, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Black Earth town.

Key observations

Among the Hispanic population in Black Earth town, regardless of the race, the largest group is of Mexican origin, with a population of 13 (100% of the total Hispanic population).

https://i.neilsberg.com/ch/black-earth-town-wi-population-by-race-and-ethnicity.jpeg" alt="Black Earth town Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Black Earth town
• Population: The population of the specific origin for Hispanic or Latino population in the Black Earth town is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Black Earth town total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth town Population by Race & Ethnicity. You can refer the same here

Baltimore City Child Health

An Exploration of 2010 Birth, Prenatal Visit, Lead Exposure and Teen Birth Rates

By City of Baltimore [source]

About this dataset

This Baltimore City Child and Family Health Indicators dataset provides us with crucial information that can support the health and well-being of Baltimore City residents. It contains 13 indicators such as low birth weight, prenatal visits, teen births, and more. This data is sourced from the Maryland Department of Health & Mental Hygiene (DHMH), Baltimore Substance Abuse Systems (BSAS), theBaltimore City Health Department, and the US Census Bureau. Through this data set we can gain a better understanding of how Baltimore City citizens’ health compares to other areas and how it has changed over time. By investigating this dataset we are given an opportunity to create potential strategies for providing better care for our community. With discoveries from these indicators, together as a city we can bring about lasting change in protecting public health within Baltimore

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How to use the dataset

This dataset provides valuable information about the health and wellbeing of children and families in Baltimore City in 2010. The data is organized by CSA (Census Statistical Area) and includes stats on term births, low birth weight births, prenatal visits, teen births, and lead testing. This dataset can be used to analyze trends in children's health over time as well as identify potential areas that need more attention or resources.

To use this dataset: - Read through the data dictionary to understand what each column represents.
- Choose which columns you would like to explore further.
- Filter or subset the data as you see fit then visualize it with graphs or maps to better understand how conditions vary across neighborhoods in Baltimore City.
- Consider comparing the data from this year with prior years if available for deeper analysis of changes over time.
- Look for correlations among columns that could help explain disparities between neighborhoods and create strategies for improving outcomes through policy interventions or other programs designed specifically for those areas needs

Research Ideas

• Mapping health disparities in high-risk areas to target public health interventions.
• Identifying neighborhoods in need of additional resources for prenatal care, infant care, and lead testing and create specific programs to address these needs.
• Creating an online dashboard that displays real time data on Baltimore City’s population health indicators such as birth weight, teenage pregnancies, and lead poisoning for the public to access easily

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BNIA_Child_Fam_Health_2010.csv | Column name | Description | |:---------------|:----------------------------------------------------------| | the_geom | Geometry of the Census Statistical Area (CSA) (Geometry) | | CSA2010 | Census Statistical Area (CSA) (String) | | termbir10 | Total number of term births in 2010 (Integer) | | birthwt10 | Total number of low birth weight births in 2010 (Integer) | | prenatal10 | Total number of prenatal visits in 2010 (Integer) | | teenbir10 | Total number of teen births in 2010 (Integer) | | leadtest10 | Total number of lead tests conducted in 2010 (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Baltimore.

Vietnam VN: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 13.400 % in 2022. This records a decrease from the previous number of 13.900 % for 2020. Vietnam VN: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 12.800 % from Dec 1992 (Median) to 2022, with 13 observations. The data reached an all-time high of 14.000 % in 2018 and a record low of 7.100 % in 1997. Vietnam VN: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample excludes towns of Baalbek, Bint Jbeil,and Hermel under the control of Hezbollah aswell as the Beirut suburb of ahiyeh. The excluded areas represent about 13% of the population. Excluded zones were replaced by areas within the same governorate.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

About this dataset

Context

The dataset tabulates the Lincoln township Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Lincoln township, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Lincoln township.

Key observations

Among the Hispanic population in Lincoln township, regardless of the race, the largest group is of Mexican origin, with a population of 13 (50% of the total Hispanic population).

https://i.neilsberg.com/ch/lincoln-township-blue-earth-county-mn-population-by-race-and-ethnicity.jpeg" alt="Lincoln township Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Lincoln township
• Population: The population of the specific origin for Hispanic or Latino population in the Lincoln township is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Lincoln township total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lincoln township Population by Race & Ethnicity. You can refer the same here

BackgroundTotal hip arthroplasty (THA) is a common procedure following which postoperative visits are important to optimize outcomes. The associated global billing period includes the 90 postoperative days (or approximately 13 weeks), during which professional billing is included with the surgery itself. The current study assessed clinical practice patterns relative to the global billing period.MethodsUsing the PearlDiver M91Ortho dataset, the incidence and timing of Evaluation and Management (E&M) codes in the 26 weeks following THA were assessed. The follow-up visits within and beyond the global billing period, and those conducted by surgeons versus non-surgeon advanced practice providers (APPs) were determined.Results77,843 THAs were identified. Follow-up visits peaked at postoperative weeks 3, 7, and 14. The greatest week-to-week variation in the number of follow-ups was from weeks 13 to 14 immediately following the global billing period (representing a greater than 4-fold increase in visits.) During the first 13 postop weeks, 73.8% of patients were seen by orthopedic surgeons (as opposed to APPs). In the following 13 weeks, a significantly greater percentage of visits were with surgeons (86.8%, p

The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Transnistria (Prednestrovie) excluded for safety of interviewers. The excluded area represents approximately 13 percent of the total population.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Moldova is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

AZ: Literacy Rate: Adult: % of People Aged 15 and Above data was reported at 100.000 % in 2023. This stayed constant from the previous number of 100.000 % for 2019. AZ: Literacy Rate: Adult: % of People Aged 15 and Above data is updated yearly, averaging 100.000 % from Dec 1999 (Median) to 2023, with 13 observations. The data reached an all-time high of 100.000 % in 2023 and a record low of 99.000 % in 1999. AZ: Literacy Rate: Adult: % of People Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Azerbaijan – Table AZ.World Bank.WDI: Social: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

About this dataset

Context

The dataset tabulates the White Earth township Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of White Earth township, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of White Earth township.

Key observations

Among the Hispanic population in White Earth township, regardless of the race, the largest group is of Mexican origin, with a population of 54 (71.05% of the total Hispanic population).

https://i.neilsberg.com/ch/white-earth-township-mn-population-by-race-and-ethnicity.jpeg" alt="White Earth township Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the White Earth township
• Population: The population of the specific origin for Hispanic or Latino population in the White Earth township is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of White Earth township total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth township Population by Race & Ethnicity. You can refer the same here

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

This dataset captures multi-market financial indicators that can be used to study financial crises, market stress, and economic stability. It integrates simulated data from stock, bond, and foreign exchange (forex) markets, along with volatility metrics and a binary crisis label.

The dataset provides a comprehensive view of cross-market behavior and is suitable for tasks such as crisis detection, financial risk analysis, and market interdependence studies.

Key Features Time Series Coverage:

Daily data over ~1,000 days for multiple countries

Stock Market Indicators:

Stock_Index → Simulated stock market index values

Stock_Return → Daily percentage change in stock index

Stock_Volatility → 5-day rolling standard deviation of stock returns

Bond Market Indicators:

Bond_Yield → Simulated 10-year government bond yield

Bond_Yield_Spread → Difference between long-term and short-term yields

Bond_Volatility → Simulated volatility in bond yields

Forex Market Indicators:

FX_Rate → Simulated currency exchange rate

FX_Return → Daily percentage change in exchange rate

FX_Volatility → 5-day rolling standard deviation of forex returns

Global Market Stress Indicator:

VIX → Simulated volatility index representing market stress

Target Variable:

Crisis_Label → Binary flag indicating market condition (0 = Normal, 1 = Crisis)

File Information Format: CSV

Rows: ~3,000 (1,000 days × 3 countries)

Columns: 13 (including target label)

Use Cases:

Financial crisis detection

Market stress and contagion analysis

Cross-market economic studies

The Electoral Integrity Project at Harvard University and the University of Sydney (www.electoralintegrityproject.com) developed the AVE data, release 1.0. The dataset contains information from a three-wave panel survey designed to gather the views of a representative sample of ordinary Australians just before and after the 2nd July 2016 Australian federal elections. The survey monitored Australian voters’ experience at the polls, perceptions of the integrity and convenience of the registration and voting process, patterns of civic engagement, public confidence in electoral administration, and attitudes towards reforms, such as civic education campaigns and convenience voting facilities. Respondents were initially contacted in the week before the election between 28 June and 1 July and completed an online questionnaire lasting approximately 15 minutes. This forms the pre-election base line survey (wave 1). The same individuals were contacted again after the election to complete a longer survey, an average of 25 minutes in length. Respondents in wave 2 were contacted between 4 July and 19 July, with two thirds completing the survey after the first week. About six weeks later, the same respondents were interviewed again (wave 3) beginning on 23 August and ending on 13 September. The initial sample contains 2,139 valid responses for the first wave of questionnaires, 1,838 for the second wave (an 86 percent retention rate), and 1,543 for the third wave (84 percent retention rate). Overall, 72 percent of the respondents were carried over from the pre-election wave to the final wave. The following files can be accessed: a) dataset in Stata and SPSS formats; b) codebook; c) questionnaire. The EIP acknowledges support from the Kathleen Fitzpatrick Australian Laureate from the Australian Research Council (ARC ref: FL110100093). **** EIP further publications: BOOKS • LeDuc, Lawrence, Richard Niemi and Pippa Norris. Eds. 2014. Comparing Democracies 4: Elections and Voting in a Changing World. London: Sage Publications. • Nai, Alessandro and Walter, Annemarie. Eds. 2015 New Perspectives on Negative Campaigning: Why Attack Politics Matters. Colchester: ECPR Press. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. Eds. 2014. Advancing Electoral Integrity. New York: Oxford University Press. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. Eds. 2015. Contentious Elections: From Ballots to the Barricades. New York: Routledge. • Norris, Pippa. 2014. Why Electoral Integrity Matters. New York: Cambridge University Press. • Norris, Pippa. 2015. Why Elections Fail. New York: Cambridge University Press. • Norris, Pippa and Andrea Abel van Es. Eds. 2016. Checkbook Elections? Political Finance in Comparative Perspective. Oxford University Press. ARTICLES AND CHAPTERS • W. Frank. 2013. ‘Assessing the quality of elections.’ Journal of Democracy. 24(4): 124-135.• Lago, Ignacio and Martínez i Coma, Ferran. 2016. ‘Challenge or Consent? Understanding Losers’ Reactions in Mass Elections’. Government and Opposition doi:10.1071/gov.3015.31 • Martínez i Coma, Ferran and Lago, Ignacio. 2016. 'Gerrymandering in Comparative Perspective’ Party Politics DOI: 10.1177/1354068816642806 • Norris, Pippa. 2013. ‘Does the world agree about standards of electoral integrity? Evidence for the diffusion of global norms.’ Special issue of Electoral Studies. 32(4):576-588. • Norris, Pippa. 2013. ‘The new research agenda studying electoral integrity’. Special issue of Electoral Studies. 32(4): 563-575.57 • Norris, Pippa. 2014. ‘Electoral integrity and political legitimacy.’ In Comparing Democracies 4. Lawrence LeDuc, Richard Niemi and Pippa Norris. Eds. London: Sage. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. 2014. ‘Measuring electoral integrity: A new dataset.’ PS: Political Science & Politics. 47(4): 789-798. • Norris, Pippa. 2016 (forthcoming). ‘Electoral integrity in East Asia.’ Routledge Handbook on Democratization in East Asia. Tun-jen Cheng and Yun-han Chu. Eds. Routledge: New York. • Norris, Pippa. 2016 (forthcoming). ‘Electoral transitions: Stumbling out of the gate.’ In Rebooting Transitology – Democratization in the 21st Century. Mohammad-Mahmoud Ould Mohamedou and Timothy D. Sisk. Eds. • Pietsch, Juliet; Michael Miller and Jeffrey Karp. 2015. ‘Public support for democracy in transitional regimes.’ Journal of Elections, Public Opinion and Parties. 25(1): 1–9. DOI: 10.1080/17457289.2014. • Smith, Rodney. 2016 (forthcoming). ‘Confidence in paper-based and electronic voting channels: Evidence from Australia.’ Australian Journal of Political Science. ID: 1093091 DOI: 10.1080/10361146.2015.1093091 dx.doi.org/10.1080/07907184.2015.1099097 • Van Ham, Carolien and Staffan Lindberg. 2015. ‘From sticks to carrots: Electoral manipulation in Africa, 1986-2012’, Gover... Visit https://dataone.org/datasets/sha256%3A9efcfe40123531a7f785369bae96a30beb0f41c1ce7334bc7c398a54be5e69f5 for complete metadata about this dataset.

Dataset description: This dataset contains the information needed to replicate the results presented in the article “Optimizing recruitment in PPGIS – is it worth the time and the costs?”. The data were collected as part of a study investigating recruitment strategies for a large-scale online public participation GIS (PPGIS) platform in coastal areas of Northern Norway. To investigate different recruitment strategies, we reviewed previous environmental PPGIS studies using random sampling and methods to increase response rates. We compared the attained results with our large-scale PPGIS in Northern Norway, where we used both random and volunteer (traditional and social media) sampling. The dataset includes response rates for the 5% of the population (13 regions in Northern Norway) recruited by mail to participate in an online PPGIS survey, response rates from volunteers recruited through traditional and social media, synthetic demographic data, and the code necessary for processing demographic data to obtain the results presented in the article. Original demographic data is not shared due to privacy legislation. We furthermore calculated time spent and costs used for recruiting both randomly sampled persons and volunteers. Article abstract: Public participation GIS surveys use both random and volunteer sampling to recruit people to participate in a self-administered mapping exercise online. From random sampling designs, the participation rate is known to be relatively low, and biased to specific segments (e.g., mid-aged, educated men). Volunteer sampling provides the opportunity to reach a large crowd at reasonable costs, but generally suffers from unknown sampling biases and lower data quality. The low participation rates and the quality of mapping question the validity and generalizability of the results, limiting its use as a democracy tool for enhancing participation in development and planning. We therefore asked: How can we increase participation in online PPGIS surveys? Is it worth the time and the costs? We reviewed environmentally related, online PPGIS surveys (N=51) and analyzed the sampling biases and recruitment strategies utilized in a large scale online PPGIS platform in coastal areas of Northern Norway using both random sampling (16978 invited participants) and volunteer sampling. We found the time, effort, and costs spent to increase participation rates to yield meager results. We discuss the time and cost efficiency of different recruitment methods, as well as the implications of the low participation levels notwithstanding the recruitment methods used.

Abstract

Since 2018, the Listening to the Citizens of Uzbekistan Survey (L2CU) has been implemented by the World Bank with the support of the UK Government. Around 1,500 households across the country regularly participate in the phone survey that is carried out every month. The L2CU collects information on public’s perception of overall socio-economic conditions and policy reforms, migration, employment, access to public service, household income, savings, food security, and coping mechanisms. That helps to inform the Government’s crucial decision-making for delivering essential social and economic reforms, implementing poverty reduction initiatives, and improving the well-being of citizens.

Geographic coverage

National, urban and rural

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the L2CU is a subsample of the 2018 baseline survey. The 2018 baseline survey was conducted face-to-face interviews with a nationally and urban/rural representative sample of 4,000 households from 200 PSU (mahallas – lowest administrative unit) in Uzbekistan. The baseline survey took the two-stage sampling approach, and the sample was stratified for all 14 regions, taking into consideration settlement type (urban and rural) within each region, except for Tashkent city which fully refers to urban area. As a result, 27 strata were formed where 1 stratum was dedicated to Tashkent city and 26 strata to 13 regions. In the first stage, 200 PSU were selected with probability proportion to the population size of 27 strata and in the second stage, 20 households are randomly selected in each PSU. In the baseline, households were asked to provide phone number of their most knowledgeable member for future contact during short phone interviews (CATI - Computer Assisted Telephone Interview).

After completion of the 2018 baseline survey, L2CU monthly interviewers began regularly calling a randomly selected panel of 1,500 households over the phone to conduct short interviews, following a set monthly schedule agreed to by the participating household. For the phone L2CU survey, 7-8 households are randomly selected in each of 200 clusters in the Baseline.

To ensure that non-response in the first round (and attrition in subsequent rounds) did not affect the required sample size for survey representativeness, households that refused to participate were replaced with other households drawn from the same PSU in the baseline data.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was administered in local languages (i.e. Uzbek and Russian) with about varying length (about 20 minutes).

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the NBT with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

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