About this dataset

Context

The dataset tabulates the Reform population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Reform. The dataset can be utilized to understand the population distribution of Reform by age. For example, using this dataset, we can identify the largest age group in Reform.

Key observations

The largest age group in Reform, AL was for the group of age Under 5 years years with a population of 197 (12.04%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Reform, AL was the 80 to 84 years years with a population of 14 (0.86%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Reform is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Reform total 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 Reform Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Reform by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Reform across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.42% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Reform is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Reform total 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 Reform Population by Race & Ethnicity. You can refer the same here

While recent research on the origins of proportional representation (PR) in Europe has focused on domestic political explanations, we bring international trade back as an economic explanation for the politics of electoral system choice. Spurred by Rogowski’s (1987) theory of the trade origins of PR and the political economy literature on trade policy, we argue that political conflict over trade shaped the struggle over electoral reform during the first globalization. Because tariffs were a central and contested issue, economic interests hurt by rising tariffs under the old electoral system have economic motives to support the introduction of PR. To conduct a missing test of the theory, we leverage district level popular votes in Switzerland using a within-country research design. We find support for the core mechanism of the trade theory: demand for protectionism entailed stronger opposition to the introduction of PR. Using panel data, we demonstrate that changes in the relative size of the agricultural sector, the central pillar of support for protectionism, were closely related to changes in support for PR. We also examine legislative voting in Germany, and find that protectionism was linked to subsequent opposition to electoral reform. Altogether, our analysis highlights the relatively overlooked importance of trade in conflict over electoral institutions.

About this dataset

Context

The dataset tabulates the Reform population by age. The dataset can be utilized to understand the age distribution and demographics of Reform.

Content

The dataset constitues the following three datasets

• Reform, AL Age Group Population Dataset: A complete breakdown of Reform age demographics from 0 to 85 years, distributed across 18 age groups
• Reform, AL Age Cohorts Dataset: Children, Working Adults, and Seniors in Reform - Population and Percentage Analysis
• Reform, AL Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

Participation Autonomy Statute Reform Referendum per census

Abstract copyright UK Data Service and data collection copyright owner.

The paper Udny Yule read to the Royal Statistical Society at the end of the nineteenth century (Yule, 1899) was a landmark in social statistics. He applied multiple regression analysis to a question of social policy, namely reforms to the 19th century system of poverty alleviation in England. To do this, Yule created a dataset from administrative and Census data. Yule’s original dataset was not preserved, but because his data were drawn from public sources, it is possible to reconstruct it, albeit with some slight differences from the original. This report provides a description of how the dataset was reconstructed and how it varies from the one used in the 1899 paper.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Reform. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Reform. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Reform, where there exist only two delineated age groups, the median household income is $37,813 for householders within the 45 to 64 years age group, compared to $34,167 for the 65 years and over age group.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Reform median household income by age. You can refer the same here

Abstract

The Romania Public Administration Employee Survey was conducted to provide a quantitative diagnostic of the de facto experiences of workers in the public administration, and to establish baseline metrics against which the impact of reforms can be assessed. The survey was conducted in 81 central, territorial and local public institutions across the country.

Survey modules broadly followed the main HRM reform priorities of the Romanian Government, such as recruitment, promotion, performance management, compensation, as well as employees’ attitudes and perceptions of these HRM practices and their general work environment. The survey design also incorporated innovative methodological approaches to reduce potential social desirability bias in responses – providing answers that would be expected by management, instead of honest views. The survey was implemented through two modalities, face-to-face interviews and an online questionnaire. The sampling frame was designed to provide a representative picture of the Romanian public administration at the central, territorial and local levels. The sample included both civil servants and contract-based staff across all the surveyed institutions. Around 14% of survey respondents were contract-based, while the rest were civil servants.

Findings can be accessed here: https://www.worldbank.org/en/events/2021/12/01/catalyzing-public-administration-reform-through-surveys-lessons-from-romania-and-uruguay

Geographic coverage

The survey was conducted in 81 central, territorial and local public institutions across Romania.

Analysis unit

Public servants

Universe

The survey was conducted in 81 central, territorial and local public institutions across Romania.

Kind of data

Aggregate data [agg]

Sampling procedure

The sampling frame was designed to provide a representative picture of the Romanian public administration at both the central and the local levels. To achieve this, 19 central-level institutions were chosen as part of the sampling frame, including 12 ministries and five randomly selected specialized agencies, the NACS and the Court of Accounts (where the World Bank is supporting another project). For data collection in local and territorial level institutions, 14 of Romania’s 41 counties were selected. From each of Romania’s seven development regions two counties were selected randomly. Overall, 19 central-level and 84 local and territorial level institutions formed the institutional sampling frame.

The sample of individual respondents was drawn from the census of employees (civil servants and contract-based staff) within each of the institutions. Given the uneven distribution of employees across institutions, to provide a representative picture, the sample was drawn as a proportion of each category of staff. To ensure a reasonable number of observations within each institution, minimum and maximum constraints were also imposed on the sample size within each category of staff. The final sampling frame included 6,057 staff across 102 institutions. This was evenly split between central (51%) and local-level employees (49%); and weighted towards civil servants (81%) relative to contract-based staff (19%), reflecting the population distribution of civil servants (86%) relative to contract-based staff.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires for the online and in-person surveys in Romanian and English are downloadable as related resources.

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 for NACJD 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 purpose of the study was to examine how court decisions and sentencing policy changes have affected sentencing behavior in federal drug trafficking cases. Changes at the district level and in mandatory minimum sentencing were a particular focus.Data were obtained from the Defendants Sentenced Under the Sentencing Reform Act data from the United States Sentencing Commission from fiscal years 1992-2009. These data were then merged with federal district-level indicators for the 89 federal districts from the Federal Court Management Statistics website, and state level demographic data from the United States Census Bureau. Drug trafficking cases were identified by using the sentencing guideline offense, which resulted in a sample of N=376,637 cases.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Reform: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 15(2.19%) households where the householder is under 25 years old, 214(31.20%) households with a householder aged between 25 and 44 years, 321(46.79%) households with a householder aged between 45 and 64 years, and 136(19.83%) households where the householder is over 65 years old.
• In Reform, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Reform median household income by age. You can refer the same here

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Place in which people are living/ being sheltered, or are present on census day like a detached house; an apartment flat; a prefabricated house; a tent, a shack, etc.; a hotel, motel, hostel; a train, a boat, a bus, a terminal, etc.; a hospital, a health clinic; a military post, garrison, an officer's club, etc.; a boarding school, a dormitory; a child daycare facility, an orphanage, a nursing home; a prison, a reform school, or other places (a factory, an official office, an embassy, etc.). - Households: Social entities made up of one or more persons, whether bound by kinship or not, living in the same dwelling or in a portion of the same dwelling, participating in the provision of service or management to the household, who make no distinctions among themselves in terms of their income or expenses. - Group quarters: Places such as a hotel, a motel, a hostel, a train, a boat, a bus, a train station, a terminal, a port, a hospital, a health clinic, a military post, a garrison, an officer's club, a boarding school, a dormitory, a nursing home, a child daycare facility, an orphanage, a jail, a reform school, and others (a factory, an official office, an embassy, etc.).

Universe

All the persons present at places that constitute a household, that do not constitute a household like dormitories, military quarters, prisons, hospitals, hotels, etc., and the nomadic population (thus all the population within the boundaries of the country on the census day).

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: State Institute of Statistics of Turkey

SAMPLE DESIGN: Systematic random sampling by province

SAMPLE UNIT: Households, otherwise individuals if enumerated in non-household places on census day.

SAMPLE FRACTION: 5%

SAMPLE SIZE (person records): 3,388,218

Mode of data collection

Face-to-face [f2f]

Research instrument

Single form with 4 sections: address information, dwelling type information, household questions, and personal characteristics.

This dataset has been clipped to the Broward County extent from the Census dataset available through the United States Department of Treasury Community Development Financial Institutions (CDFI) Fund.

OPPORTUNITY ZONES RESOURCES: downloaded from Census : https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx

The authority to implement IRC 1400Z-1 and 1400Z-2 has been delegated to the IRS. The CDFI Fund is supporting the IRS with the Opportunity Zone nomination and designation process under IRC 1400Z-1 only. In addition to an initial set of proposed regulations and guidance on how the Qualified Opportunity Zone (QOZ) tax benefits under IRC 1400Z-2 (including the certification of Qualified Opportunity Funds (QOFs) and eligible investments in QOZs) will be administered, Treasury and IRS have issued a second set of proposed regulations relating to gains that may be deferred as a result of a taxpayer's investment in a QOF, special rules for an investment in a QOF held by a taxpayer for at least 10 years, and updates to portions of previously proposed regulations under section 1400Z-2 to address various issues, including: the definition of “substantially all.” You may submit comments on the proposed regulations electronically via the Federal Rulemaking Portal at www.regulations.gov (IRS REG-115420-18 or IRS REG 120186-18).Concurrent with the second set of proposed regulations, Treasury and IRS published a request for information (RFI), asking for detailed comments regarding ways to assess QOF investments including asset class, identification of Qualified Opportunity Zones and the impact and outcomes on those Qualified Opportunity Zones. You may submit comments on the RIF electronically via the Federal Rulemaking Portal at www.regulations.gov (TREAS-DO-2019-0004). IRS also has posted a list of Frequently Asked Questions about Opportunity Zones on the irs.gov Tax Reform pages. You will want to monitor the Tax Reform page at the IRS website for additional Opportunity Zone information and other Tax Reform information. For any other questions, please call (800) 829-1040.

List of designated Qualified Opportunity Zones (QOZs): This spreadsheet was updated December 14, 2018, to include two additional census tracts in Puerto Rico that, based on 2012-2016 American Community Survey data, meet the statutory criteria for a Low-Income Community and are deemed as designated QOZs. Based on nominations of eligible census tracts by the Chief Executive Officers of each State, Treasury has completed its designation of Qualified Opportunity Zones. Each State nominated the maximum number of eligible tracts, per statute, and these designations are final. The statute and legislative history of the Opportunity Zone designations, under IRC § 1400Z, do not contemplate an opportunity for additional or revised designations after the maximum number of zones allowable have been designated in a State or Territory. Based on IRC 1400Z-1, designations are based upon the boundaries of the tract at the time of the designation in 2018, and do not change over the period of the designation, even if the boundaries of an individual census tract are redefined in future Census releases.

Source: United States Census Bureau

Effective Date:

Last Update:12/14/2018Update Cycle: As needed, Census occurs once every decade

This article discusses the paths of school services of the Department of Education of the Federal District from 1931 to 1935 based on the statistical results of school performance. This raises the issue of the production of official statistics to understand the organization of the Census, Registration and frequency services, in the School Division and Statistics and Compulsory School, and Measurement and School Efficiency, in the Educational Research Division, and also the strategies for curb dropout and repetition. The analysis of the documentation shows that the Department of Education of the Federal District projects itself as a strategic instrument of control of the school population.

Abstract

The Government recognizes the fact that Population and Housing Census is the single most important source of demographic and socio-economic data in the country. Population and Housing Census data are important in the preparation of social and economic development policies, in monitoring improvement in the quality of life of the population and in establishment of the system of sustainable development. Population and Housing Census data will also provide a sampling frame for intercensal surveys which will be conducted in order to generate policies which will support the implementation process of the Tanzania Development Vision 2025 and the Zanzibar Development Vision 2020as well as social and economic reforms in a decentralized Government framework.

At the planning level, the Population and Housing Census data will play a central role in the formulation of realistic development of people. In the Tanzania situation where the Government is decentralizing its functions to the district level, reliable and up-to-date population and Housing Census data will help district authorities to prepare development plans, which will reflect the aspirations of the people.

Given the Government goals of, one, reducing the proportion of Tanzanians living in absolute poverty by the year 2010, and two, of eradicating absolute poverty by the year 2025, the 2002 population and Housing Census has enabled the Government to get data which will be used to develop poverty status predictors. As such the 2002 population and Housing Census is an important source of data for poverty monitoring activities. Finally, the 2002 population and Housing Census has enabled the Government to get data on population growth and distribution by age, sex and location and their relationship to the resource base i.e. their impact on the environment.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of census questionnaires namely the long and the short questionnaire. The first eight questions, which appeared in both questionnaires, were name, relation to the head of the household, age, sex, marital status, disability and citizenship.

In addition the long questionnaire included extra questions on the following topics. 1. Survivorship of the parents of the person 2. Migration 3. Education for all persons 5 years and above 4. Economic Activity for all persons 5 year and above 5. Fertility of all women aged 12 years and above 6. Mortality 7. Housing conditions

Overall, the long questionnaire comprised 37 questions. Questionnaires were in Swahili Language.

Sampling error estimates

Census data based on the long questionnaire is subject to sampling errors. Sampling errors of estimates for selected variables were estimated. For the sake of simplicity, sampling errors were calculated by using a formula for linear estimates without taking into account the ratio estimation. The detail for the sampling errors found from page 192 to 193 of the analytical report.

About this dataset

Context

The dataset tabulates the Reform household income by gender. The dataset can be utilized to understand the gender-based income distribution of Reform income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Reform, AL annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Reform, AL annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Reform income distribution by gender. You can refer the same here

After the Great Reforms of the 1860s – 1870s the Russian government embarked on the construction of a modern nation-state and was faced with the need to unify all parts ofthe empire administratively, culturally, legally, and socially. The new ethno-confessional policy in Russian historiography is often called Russification because the order establishedafter the Great Reforms in the Great Russian provinces served as a model for the transformation of all parts of the empire. The Russification policy included many aspects, including Russifying [obrusenie] - the introduction of the Russian language as obligatory in the record keeping of public institutions, in court and administration, in education and everyday life. While the policy of Russifying has found ample reflection in the historiography, its results have been insufficiently studied. The purpose of this article is to fill this gap and to try to assess the process of Russifying ethnic minorities at the imperial level, drawing upon the first general census of the Russian Empire in 1897. The analysis has led to the conclusion that the policy of Russifying did notprovide the expected results.

As the next round of redistricting approaches, many states have enacted a number of reforms to their mapmaking practices. One reform that has received increased attention in recent years is a ban on prison gerrymandering-the practice of counting incarcerated individuals in prisons instead of their home addresses. At present, eight states are poised to draw districts while counting incarcerated persons in their homes for the first time after the 2020 Census. Though substantial research has investigated redistricting practices, far less attention has been paid to empirically examining the effect of prison gerrymandering on elections. We seek to fill this void by evaluating the effect of New York’s ban on prison gerrymandering on state legislative elections between 2002 and 2020. We find that altering how the prison population is counted, indeed, altered the electoral dynamics across the state.

Abstract

The transition from socialism to a market economy has transformed the lives of many people. What are people's perceptions and attitudes to transition? What are the current attitudes to market reforms and political institutions?

To analyze these issues, the EBRD and the World Bank have jointly conducted the comprehensive, region-wide "Life in Transition Survey" (LiTS), which combines traditional household survey features with questions about respondents' attitudes and is carried out through two-stage sampling with a random selection of households and respondents.

The LiTS assesses the impact of transition on people through their personal and professional experiences during the first 15 years of transition. LiTS attempts to understand how these personal experiences of transition relate to people’s attitudes toward market and political reforms, as well as their priorities for the future.

The main objective of the LiTS was to build on existing studies to provide a comprehensive assessment of relationships among life satisfaction and living standards, poverty and inequality, trust in state institutions, satisfaction with public services, attitudes to a market economy and democracy and to provide valuable insights into how transition has affected the lives of people across a region comprising 16 countries in Central and Eastern Europe (“CEE”) and 11 in the Commonwealth of Independent State (“CIS”). Turkey and Mongolia were also included in the survey.

Geographic coverage

The LITS was to be implemented in the following 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Poland, Romania, Russia, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 1,000 face-to-face household interviews per country were to be conducted, with adult (18 years and over) occupants and with no upper limit for age. The sample was to be nationally representative. The EBRD’s preferred procedure was a two stage sampling method, with census enumeration areas (CEA) as primary sampling units and households as secondary sampling units. To the extent possible, the EBRD wished the sampling procedure to apply no more than 2 stages.

The first stage of selection was to use as a sampling frame the list of CEA's generated by the most recent census. Ideally, 50 primary sampling units (PSU's) were to be selected from that sample frame, with probability proportional to size (PPS), using as a measure of size either the population, or the number of households.

The second sampling stage was to select households within each of the primary sampling units, using as a sampling frame a specially developed list of all households in each of the selected PSU's defined above. Households to be interviewed were to be selected from that list by systematic, equal probability sampling. Twenty households were to be selected in each of the 50 PSU's.

The individuals to be interviewed in each household were to be selected at random, within each of the selected households, with no substitution if possible.

ESTABLISHMENT OF THE SAMPLE FRAME OF PSU’s

In each country we established the most recent sample frame of PSU’s which would best serve the purposes of the LITS sampling methodology. Details of the PSU sample frames in each country are shown in table 1 (page 10) of the survey report.

In the cases of Armenia, Azerbaijan, Kazakhstan, Serbia and Uzbekistan, CEA’s were used. In Croatia we also used CEA’s but in this case, because the CEA’s were very small and we would not have been able to complete the targeted number of interviews within each PSU, we merged together adjoining CEA’s and constructed a sample of 1,732 Merged Enumeration Areas. The same was the case in Montenegro.

In Estonia, Hungary, Lithuania, Poland and the Slovak Republic we used Eurostat’s NUTS area classification system.

[NOTE: The NUTS (from the French "Nomenclature des territoriales statistiques" or in English ("Nomenclature of territorial units for statistics"), is a uniform and consistent system that runs on five different NUTS levels and is widely used for EU surveys including the Eurobarometer (a comparable survey to the Life in Transition). As a hierarchical system, NUTS subdivides the territory of the country into a defined number of regions on NUTS 1 level (population 3-7 million), NUTS 2 level (800,000-3 million) and NUTS 3 level (150,000-800,000). At a more detailed level NUTS 3 is subdivided into smaller units (districts and municipalities). These are called "Local Administrative Units" (LAU). The LAU is further divided into upper LAU (LAU1 - formerly NUTS 4) and LAU 2 (formerly NUTS 5).]

Albania, Bulgaria, the Czech Republic, Georgia, Moldova and Romania used the electoral register as the basis for the PSU sample frame. In the other cases, the PSU sample frame was chosen using either local geographical or administrative and territorial classification systems. The total number of PSU sample frames per country varied from 182 in the case of Mongolia to over 48,000 in the case of Turkey. To ensure the safety of our fieldworkers, we excluded from the sample frame PSU’s territories (in countries such as Georgia, Azerbaijan, Moldova, Russia, etc) in which there was conflict and political instability. We have also excluded areas which were not easily accessible due to their terrain or were sparsely populated.

In the majority of cases, the source for this information was the national statistical body for the country in question, or the relevant central electoral committee. In establishing the sample frames and to the extent possible, we tried to maintain a uniform measure of size namely, the population aged 18 years and over which was of more pertinence to the LITS methodology. Where the PSU was based on CEA’s, the measure was usually the total population, whereas the electoral register provided data on the population aged 18 years old and above, the normal voting age in all sampled countries. Although the NUTS classification provided data on the total population, we filtered, where possible, the information and used as a measure of size the population aged 18 and above. The other classification systems used usually measure the total population of a country. However, in the case of Azerbaijan, which used CEA’s, and Slovenia, where a classification system based on administrative and territorial areas was employed, the measure of size was the number of households in each PSU.

The accuracy of the PSU information was dependent, to a large extent, on how recently the data has been collected. Where the data were collected recently then the information could be considered as relatively accurate. However, in some countries we believed that more recent information was available, but because the relevant authorities were not prepared to share this with us citing secrecy reasons, we had no alternative than to use less up to date data. In some countries the age of the data available makes the figures less certain. An obvious case in point is Bosnia and Herzegovina, where the latest available figures date back to 1991, before the Balkan wars. The population figures available take no account of the casualties suffered among the civilian population, resulting displacement and subsequent migration of people.

Equally there have been cases where countries have experienced economic migration in recent years, as in the case of those countries that acceded to the European Union in May, 2004, such as Hungary, Poland and the Baltic states, or to other countries within the region e.g. Armenians to Russia, Albanians to Greece and Italy; the available figures may not accurately reflect this. And, as most economic migrants tend to be men, the actual proportion of females in a population was, in many cases, higher than the available statistics would suggest. People migration in recent years has also occurred from rural to urban areas in Albania and the majority of the Asian Republics, as well as in Mongolia on a continuous basis but in this case, because of the nomadic population of the country.

SAMPLING METHODOLOGY

Brief Overview

In broad terms the following sampling methodology was employed: · From the sample frame of PSU’s we selected 50 units · Within each selected PSU, we sampled 20 households, resulting in 1,000 interviews per country · Within each household we sampled 1 and sometimes 2 respondents The sampling procedures were designed to leave no free choice to the interviewers. Details on each of the above steps as well as country specific procedures adapted to suit the availability, depth and quality of the PSU information and local operational issues are described in the following sections.

Selection of PSU’s

The PSU’s of each country (all in electronic format) were sorted first into metropolitan, urban and rural areas (in that order), and within each of these categories by region/oblast/province in alphabetical order. This ensured a consistent sorting methodology across all countries and also that the randomness of the selection process could be supervised.

To select the 50 PSU’s from the sample frame of PSU’s, we employed implicit stratification and sampling was done with PPS. Implicit stratification ensured that the sample of PSU’s was spread across the primary categories of explicit variables and a better representation of the population, without actually stratifying the PSU’s thus, avoiding difficulties in calculating the sampling errors at a later stage. In brief, the PPS involved the

Abstract (en): In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the 16th round of the survey (third quarter 2018) include self-reported health status, health insurance coverage, access to and use of health care, out-of-pocket health care costs, health care affordability, work experience, awareness of Medicaid work requirements, experiences with health care and social service providers, and health plan choice. Additional information collected by the survey includes age, gender, sexual orientation, marital status, education, race, Hispanic origin, United States citizenship, housing type, home ownership, internet access, income, employment status, and employer size. This study was conducted to provide information on health insurance coverage, access to and use of health care, health care affordability, and self-reported health status, as well as timely data on important implementation issues under the Affordable Care Act (ACA). The Health Reform Monitoring Survey (HRMS) provides data on health insurance coverage, access to and use of health care, health care affordability, and self-reported health status. Beginning in the second quarter of 2013, each round of the HRMS also contains topical questions focusing on timely ACA policy issues. In the first quarter of 2015, the HRMS shifted from a quarterly fielding schedule to a semiannual schedule. The variables include original survey questions, household demographic profile data, and constructed variables which can be used to link panel members who participated in multiple rounds. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Response Rates: The HRMS response rate is roughly five percent each round. Datasets:DS0: Study-Level FilesDS1: Public-Use DataDS2: Restricted-Use Data Household population aged 18-64 Smallest Geographic Unit: Census region For each HRMS round a stratified random sample of adults ages 18-64 is drawn from the KnowledgePanel, a probability-based, nationally represented Internet panel maintained by Ipsos. The approximately 55,000 adults in the panel include households with and without Internet access. Panel members are recruited from an address-based sample frame derived from the United States Postal Service Delivery Sequence File, which covers 97 percent of United States households. The HRMS sample includes a random sample of approximately 9,500 nonelderly adults per quarter, including oversamples of adults with family incomes at or below 138 percent of the federal poverty line. Additional funders have supported oversamples of adults from individual states or subgroups of interest. However, the data file only includes data for adults in the general national sample and the income oversample. web-based survey

This report details population and socioeconomic statistics of Hakha township. This report is a series of Township level reports published as part of the 2014 Population and Housing Census.The 2014 Population and Housing Census - the country’s first national census in 30 years – was undertaken by the Ministry of Immigration and Population with technical support from UNFPA between 30th March and 10th April 2014. 110,000 enumerators visited over 12 million households to gather data to provide social, economic and demographic characteristics of people and households for the purpose of on-going reforms, development planning and good governance. The results will be made public progressively as each stage of analysis is completed. Further information is available through http://www.dop.gov.mm/moip/ and http://countryoffice.unfpa.org/myanmar/census/