Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together separately from the 2002 datasets.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was,as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households or

Dataset Overview

This dataset contains 255,168 records of Tic-Tac-Toe games. The dataset is structured to represent every possible game sequence where: - Player X always starts first. - The game continues until there is a winner or a draw. - All positions on the board (0,0 through 2,2) are treated as unique—no symmetry optimizations have been applied.

Each row in the dataset represents a complete game, detailing each move and the final outcome.

Columns Description

Note: Moves marked with --- indicate that the game ended before these moves were made.

Key Features

1. Comprehensive Data: Includes every possible unique Tic-Tac-Toe game scenario without symmetry optimizations.
2. Detailed Moves: Captures the exact sequence of moves for both players, providing insights into gameplay strategies.
3. Game Outcomes: Clearly labels each game's result, facilitating analysis of winning patterns or strategies.

Sample Data

Potential Use Cases

1. Game Strategy Analysis: Analyze winning strategies and patterns for Player X and Player O.
2. Machine Learning: Train models to predict the game's outcome based on initial moves.
3. Game Simulations: Use the dataset to simulate Tic-Tac-Toe games for research or educational purposes.
4. Behavioral Analysis: Study move sequences to understand decision-making processes in turn-based games.

Acknowledgments

This dataset represents a complete enumeration of possible Tic-Tac-Toe games and is ideal for research, analysis, and AI development.

Abstract

The Statistical and Forecasting Service has been entrusted with the production of the AC 2010. (SSP) which is the central statistical department of the Ministry in charge of agriculture, (MAAPRAT) the central department is in charge of the design of the operation, the drafting of the questionnaire and instructions, the training of regional services, the final quality control of the data collected and of the first publication of the results. The SSP has relied on its specialised decentralised levels, the services regional statistics (NUTS2) of statistical and economic information (SRISE). The threshold definition of agricultural holding applied has been the same since 1955, and corresponds exactly to the one proposed by the European regulation. The geographical area is the whole of France; for the DOM the territories of Saint-Martin and Saint-Barthélemy are now excluded, Mayotte is not yet included.

For statistical purposes, agricultural censuses in French territories (French Guyana, Guadeloupe, Reunion and Martinique) are recorded separately in the World Census of Agriculture Database. The census results are presented for all of France.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit in the AC 2010 was the agricultural holding, defined as an economic unit that participates in agricultural production and meets the following criteria: · it has an agricultural activity either of production, or of maintenance of the lands in good agricultural and environmental

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The basic list of agricultural holdings was built using the SSP farm register, the SIRENE register (business register), the list of farmers who had applied for aid (area declarations),' and some additional sources for beekeeping, olive oil, aromatic plants. The holding lists were checked at local level by communal commissions.

b. Complete and/or sample enumeration method(s) The AC and SAPM were conducted using complete enumeration.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires were used: one for France in Europe (including questions of regional interest) and two for France's overseas territories: one for Guadeloupe, Martinique and Reunion and another for Guyana. The census covered all 16 core items recommended in the WCA 2010. ie.

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The CAPI interface included controls to ensure that there were responses to all questions. In addition, interactive range and consistency checks were included for each variable so that corrections could be made by the enumerator during the interview. Further edits and imputations were completed at the central office where the census validation and tabulation was completed. To ensure that the list of holdings was complete, several tests were conducted at the end of collection. All available administrative sources were used to verify that existing holdings had been identified and included. The key databases and registers used included that for EU agriculture aid applications, the national database of bovine identification, the computerized vineyard register, organic producer records, and some local registers for small productions. The data, after validation, were archived on secured servers.

b. CENSUS DATA QUALITY To assess the quality of field data collection, completeness checks and feedback were performed at the end of field data collection operation, from March to June 2011. Data checking began during the collection phase on the farmer's premises. It then continued throughout the processing chain. A special effort was made to check the AC's coverage by using the administrative data available. The nonresponse rate was of only 0.96 percent, and the missing data were imputed using the hot deck method.

Data appraisal

The first provisional census results were disseminated in September 2011, ten months after the end of the reference period. The main final results were made available at the end of February 2012, 16 months after the end of the reference period. The AC 2010 results were disseminated online and are available on the SSP website.9 The "ADEL" tool allows web users to build their own tables.

The first table with main results shows the total number and area of holdings broken down by continental France, on one hand, and its overseas territories, on the other. See metadata review tables in external materials.

The oldest societal issue that has ever been is poverty, which is also the hardest to overcome. It is both unmeasurable and multidimensional. Decomposing rural multidimensional poverty is therefore a crucial method of measurement. The majority of poverty studies are typically designed for macroeconomic considerations, are general, and are subject to significant sampling errors. Thus, measuring poverty for a specific locality with various configurations is crucial for economic development. A processed and analyzed dataset from Goa, Camarines Sur's extensive community-based monitoring system is presented in this work. The local is situated in the poorest district, of the poorest province, in the poorest region of Luzon, Philippines. Research about poverty in this area is limited and measuring poverty at specific locality is scarce. The datasets contain the multidimensional poverty indicators, health, and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, peace and order, summary of calamity occurrences experienced by residents, disaster risk reduction preparedness, figures of diagnostic analytics, tables of descriptive analytics, poverty analytics, measurement of decomposed poverty, summary of disaggregated configurations, graphs of predictive and prescriptive analytics, and population dynamics. This work is vital in analyzing poverty in rural and multidimensional approaches through poverty incidence, poverty gap, severity statistics, watts index, and classifications. It may also serve as a basis for measuring poverty from nearby regions and nations that use complete enumeration of its households and members. By utilizing the analyzed and processed data, further classifications and regressions can be done. It can be freely used by the government, private organizations, charitable institutions, businesses, academia, and researchers to target policies. An advantage of utilizing the dataset is to address multifaceted poverty that requires different interventions. It will facilitate the creation of programs to alleviate poverty and promote local economic development.

The Employment Summary Statistics dataset is part of the Census of Governments, a complete enumeration of United States governmental units undertaken every five years. This data collection contains the October, 1982 employment and payroll figures for the governments. Data for full- and part-time employment and payrolls are shown for such functions as administration, education, corrections, police, fire protection, utilities, health, public welfare, parks, libraries, sanitation, highways, and transit. Data are also provided for labor-management relations, employee organizations, employee benefits, and unemployment, health, and life insurance. There are four files in this collection. File A provides detailed statistics for each state and local government, File B has the data for local governmental units aggregated by county, and File C has national and state summaries for the following types of governments: (1) State and Local Government Total, (2) State Government, (3) Local Government, (4) Local Governments in SMSA's, (5) Counties, (6) Municipalities, (7) Townships, (8) School Districts, and (9) Special Districts. In addition, the Name and Address File contains name, address, and corresponding government identification code for all of the local governmental units.

Description: The 2005 HSRC Master Sample was used for SABSSM 2008 and 2012, the SANHANES study in 2012 and SASAS 2007-2010 (adjacent EAs) to obtain an understanding of geographical spread of HIV/AIDS, perceptions and attitudes of people and other health related studies over time. Abstract: A sample can be defined as a subset containing the characteristics of a larger population. Samples are used in statistical testing when population sizes are too large for the test to include all possible members or observations. A sample should represent the whole population and not reflect bias toward a specific attribute.[1] One of the most crucial aspects of sample design in household surveys is its frame. The sampling frame has significant implications on the cost and the quality of any survey, household or otherwise.[2] The sampling frame .... in a household survey must cover the entire target population. When that frame is used for multiple surveys or multiple rounds of the same survey it is known as a master sample frame or .... master sample.[3] A master sample is a sample drawn from a population for use on a number of future occasions, so as to avoid ad hoc sampling on each occasion. Sometimes the master sample is large and subsequent inquiries are based on a sub-sample from it.[4] The HSRC compiles master samples in order to construct samples for various HSRC research studies. The 2005 HSRC Master Sample was used for SABSSM 2008 and 2012, SASAS 2007-2010 and the SANHANES study in 2012 to obtain an understanding of geographical spread of HIV/AIDS, perceptions and attitudes of people and other health related studies over time. The 2005 HSRC Master Sample was created in the following way: South Africa was delineated into EAs according to municipality and province. Municipal boundaries were obtained from the Municipal Demarcation Board. An Enumeration area (EA) is the smallest geographical unit (piece of land) into which the country is divided for census or survey enumeration.[5] The concepts and definitions of terms used for Census 2001 comply in most instances with United Nations standards for censuses. A total of 1,000 census enumeration areas (EAs) from the 2001 population census were randomly selected using probability proportional to size and stratified by province, locality type and race in urban areas from a database of 80 787 EAs that were mapped using aerial photography to develop an HSRC master sample for selecting households. The ideal frame would be complete with respect to the target population if all of its members (the universe) are covered by the frame. Ideal characteristics of a master sample: The master frame should be as complete, accurate and current as practicable. A master sample frame for household surveys is typically developed from the most recent census, just as a regular sample frame is. Because the master frame may be used during an entire intercensal (between census) period, however, it will usually require periodic and regular updating such as every 2-3 years. This is in contrast to a regular frame which is more likely to be up-dated on an ad hoc basis and only when a particular survey is being planned[6] [1] http://www.investopedia.com/terms/s/sample.asp [2] http://unstats.un.org/unsd/demographic/meetings/egm/sampling_1203/docs/no_3.pdf [3] http://unstats.un.org/unsd/demographic/meetings/egm/sampling_1203/docs/no_3.pdf [4] A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical. http://stats.oecd.org/glossary/detail.asp?ID=3708 [5] http://africageodownloads.info/128_mokgokolo.pdf [6] http://unstats.un.org/unsd/demographic/meetings/egm/sampling_1203/docs/no_3.pdf All enumeration areas (80 787 EAs) within the South African borders during the 2001 Census. The whole country was delimited into EAs according to municipality and province. Municipal boundaries were obtained from the Municipal Demarcation Board. A total of 1,000 census enumeration areas (EAs) from the 2001 population census were randomly selected using probability proportional to size and stratified by province, locality type and race in urban areas from a database of 80 787 EAs that were mapped in all surveys using aerial photography to develop all HSRC master sample for selecting households. The first digit represents the province The second and third digits represent the municipality

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The IPUMS microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1940 census data was collected in April 1940. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.
• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT40, reconstructed using the variable SERIAL40, and the original count is found in the variable NUMPREC40.
• Some variables are missing from this data set for specific enumeration districts. The enumeration districts with missing data can be identified using the variable EDMISS. These variables will be added in a future release.
• Coded variables derived from string variables are still in progress. These variables include: occupation, industry and migration status.
• Missing observations have been allocated and some inconsistencies have been edited for the following variables: Missing observations have been allocated and some inconsistencies have been edited for the following variables: SURSIM, SEX, SCHOOL, RELATE, RACE, OCC1950, MTONGUE, MBPL, FBPL, BPL, MARST, EMPSTAT, CITIZEN, OWNERSHP. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.
• Most inconsistent information was not edited for this release, thus there are observations outside of the universe for many variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next r

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED

Between February and March 2016, the World Bank, in collaboration with Somali statistical authorities conducted the first wave of the Somali High Frequency Survey to monitor welfare and perceptions of citizens in all accessible areas of 9 regions within Somalia's pre-war borders including Somaliland which self-declared independence in 1991. The survey interviewed 2,882 urban households, 822 rural and 413 households in Internally Displaced People (IDP) settlements. The sample was drawn randomly based on a multi-level clustered design. This dataset contains information on economic conditions, education, employment, access to services, security and perceptions. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology as detailed in Pape and Mistiaen (2014).

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of urban, rural and IDP settlements areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED

The sample employs a stratified two-staged clustered design with the Primary Sampling Unit (PSU) being the enumeration area. Within each enumeration area, 12 households were selected for interviews.

Two different listing approaches were used. In 2 strata with more volatile security as well as for IDP camps, a multi-stage cluster design was employed (micro-listing). Each selected enumeration area was divided into multiple segments and each segment was further divided into blocks. Within each enumeration area, one segment was randomly selected and within the segment 12 blocks were chosen. In each block, all structures were listed before selecting randomly one structure. Within the selected structure, all households were listed and one household randomly selected for interview. In strata less volatile (14 strata), the complete enumeration area was listed before 12 households were randomly selected for interviews (full-listing).

Sampling deviation

EAs were replaced if security rendered field work unfeasible. Replacements were approved by the project manager. Replacement of households were approved by the supervisor after a total of three unsuccessful visits of the household.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaire Modules - Household Roster (110 questions) - Household Characteristics (38 questions) - Consumption - Food (30 questions per item) - Non-Food (14 questions per item) - Livestock (39 questions per item) - Durables (16 questions per item) - Perception (24 questions) - Food Security* (24 questions) - Income and Remittances* (14 questions) - Household Enterprise* (172 questions) - Shocks* (15 questions)

Cleaning operations

----> Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute /recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

For the survey sample, the response rate was 95.9% (92.8% in urban areas and 98.5% in rural areas).

The National Survey of Tribal Court Systems (NSTCS) is the first complete enumeration of tribal court systems operating in the United States and gathers administrative and operational information from tribal court systems, prosecutors' offices, and indigent defense providers operating in the United States. The NSTCS helps fulfill BJS's legislative mandate under the Tribal Law and Order Act of 2010 (TLOA; P.L. 111-211, 124 Stat. 2258 § 251(b)) to establish and implement a tribal crime data collection system. Data for the NSTCS were collected by Kauffman & Associates, Inc., an American Indian- and woman-owned management consulting firm, in collaboration with the Tribal Law and Policy Institute. The National Survey of Tribal Court Systems (NSTCS) consists of three surveys specific to tribal court systems in the lower 48 states, Alaska Native villages, and the Code of Federal Regulations Courts (CFR Courts) operated by the Bureau of Indian Affairs (BIA). Due to data collection challenges and the limited number of Alaska Native villages and CFR Courts that participated in this collection, the Tribal Courts in United States, 2014, report, data file and documentation include information only on tribal court systems in the lower 48 states. Data for the 2014 NSTCS were collected through mail, email, and telephone nonresponse follow-up. Data on the number and type of tribal court systems were obtained from all eligible federally recognized tribes. The final universe of eligible respondents in the lower 48 states included 234 tribal court systems, of which 196 (83.8%) participated in the survey.

Abstract

The State Ambulatory Surgery Databases (SASD), State Inpatient Databases (SID), and State Emergency Department Databases (SEDD) are part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP).

HCUP's state-specific databases can be used to investigate state-specific and multi-state trends in health care utilization, access, charges, quality, and outcomes. PHS has several years (2008-2011) and datasets (SASSD, SED and SIDD) for HCUP California available.

Usage

The State Ambulatory Surgery and Services Databases (SASD) are State-specific files that include data for ambulatory surgery and other outpatient services from hospital-owned facilities. In addition, some States provide ambulatory surgery and outpatient services from nonhospital-owned facilities. The uniform format of the SASD helps facilitate cross-State comparisons. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within geographic areas or States.

The State Inpatient Databases (SID) are State-specific files that contain all inpatient care records in participating states. Together, the SID encompass more than 95 percent of all U.S. hospital discharges. The uniform format of the SID helps facilitate cross-state comparisons. In addition, the SID are well suited for research that requires complete enumeration of hospitals and discharges within geographic areas or states.

The State Emergency Department Databases (SEDD) are a set of longitudinal State-specific emergency department (ED) databases included in the HCUP family. The SEDD capture discharge information on all emergency department visits that do not result in an admission. Information on patients seen in the emergency room and then admitted to the hospital is included in the State Inpatient Databases (SID)

SASD, SID, and SEDD each have **Documentation **which includes:

• Description of the Database
• Restrictions on Use
• File Specifications and Load Program
• Data Elements
• Additional Resources for Data Elements
• ICD-10-CM/PCS Data Included in the Dataset Starting with 2015
• Known Data Issues
• HCUP Tools: Labels and Formats
• HCUP Supplemental Files
• Obtaining HCUP Data

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Documentation

The HCUP California inpatient files were constructed from the confidential files received from the Office of Statewide Health Planning and Development (OSHPD). OSHPD excluded inpatient stays that, after processing by OSHPD, did not contain a complete and “in-range” admission date or discharge date. California also excluded inpatient stays that had an unknown or missing date of birth. OSHPD removes ICD-9-CM and ICD-10-CM diagnoses codes for HIV test results. Beginning with 2009 data, OSHPD changed regulations to require hospitals to report all external cause of injury diagnosis codes including those specific to medical misadventures. Prior to 2009, OSHPD did not require collection of diagnosis codes identifying medical misadventures.

**Types of Facilities Included in the Files Provided to HCUP by the Partner **

California supplied discharge data for inpatient stays in general acute care hospitals, acute psychiatric hospitals, chemical dependency recovery hospitals, psychiatric health facilities, and state operated hospitals. A comparison of the number of hospitals included in the SID and the number of hospitals reported in the AHA Annual Survey is available starting in data year 2010. Hospitals do not always report data for a full calendar year. Some hospitals open or close during the year; other hospitals have technical problems that prevent them from reporting data for all months in a year.

**Inclusion of Stays in Special Units **

Included with the general acute care stays are stays in skilled nursing, intermediate care, rehabilitation, alcohol/chemical dependency treatment, and psychiatric units of hospitals in California. How the stays in these different types of units can be identified differs by data year. Beginning in 2006, the information is retained in the HCUP variable HOSPITALUNIT. Reliability of this indicator for the level of care depends on how it was assigned by the hospital. For data years 1998-2006, the information was retained in the HCUP variable LEVELCARE. Prior to 1998, the first

Abstract

The Census of Agriculture investigates information on agricultural establishments and agricultural activities developed inside them, including characteristics of the producers and establishments, economy and employment in the rural area, livestock, cropping and agribusiness. Its data collection unit is every production unit dedicated, either entirely or partially, to agricultural, forest or aquaculture activities, subordinated to a single administration – producer or administrator –, regardless of its size, legal nature or location, aiming at producing either for living or sales.

The first Census of Agriculture dates back to 1920, and it was conducted as part of the General Census. It did not take place in the 1930s due to reasons of political and institutional nature. From 1940 onward, the survey was decennial up to 1970 and quinquennial later on, taking place in the beginning of the years ending in 1 and 6 and relating to the years ending in 0 and 5. In the 1995-1996 Census of Agriculture, the information was related to the crop year (August 1995 to July 1996). In the 2006 Census of Agriculture, the reference for the data returned to be the calendar year. The 2006 edition was characterized both by the technological innovation introduced in the field operation, in which the paper questionnaire was replaced by the electronic questionnaire developed in Personal Digital Assistants - PDAs and by the methodological refinement, particularly concerning the redesign of its contents and incorporation of new concepts. That edition also implemented the National Address List for Statistical Purposes - Cnefe, which gathers the detailed description of the addresses of housing units and agricultural establishments, geographic coordinates of every housing unit and establishment (agricultural, religious, education, health and other) in the rural area, bringing subsidies for the planning of future IBGE surveys. The 2017 Census of Agriculture returned to reference the crop year – October 2016 to September 2017 –, though in a different period than that adopted in the 1995-1996 Census of Agriculture. New technologies were introduced in the 2017 survey to control the data collection, like: previous address list, use of satellite images in the PDAs to better locate the enumerator in relation to the terrain, and use of coordinates of the address and location where the questionnaire is open, which allowed a better coverage and assessment of the work.

The survey provides information on the total agricultural establishments; total area of those establishments; characteristics of the producers; characteristics of the establishments (use of electricity, agricultural practices, use of fertilization, use of agrotoxins, use of organic farming, land use, existence of water resources, existence of warehouses and silos, existence of tractors, machinery and agricultural implements, and vehicles, among other aspects); employed personnel; financial transactions; livestock (inventories and animal production); aquaculture and forestry (silviculture, forestry, floriculture, horticulture, permanent crops, temporary crops and rural agribusiness).

The periodicity of the survey is quinquennial, though the surveys in 1990, 1995, 2000 and 2005, 2010 and 2015 were not carried out due to budget restrictions from the government; the 1990 Census of Agriculture did not take place; the 1995 survey was carried out in 1996 together with the Population Counting; the 2000 survey did not take place; that of 2005 was carried out in 2007, together with the Population Counting once again; that of 2010 did not take place and that of 2015 was carried out in 2017. Its geographic coverage is national, with results disclosed for Brazil, Major Regions, Federation Units, Mesoregions, Microregions and Municipalities. The results of the 2006 Census of Agriculture, which has the calendar year as the reference period, are not strictly comparable with those from the 1995-1996 Census of Agriculture and 2017 Census of Agriculture, whose reference period is the crop year in both cases.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as any production unit dedicated wholly or partially to agricultural, forestry and aquaculture activities, subject to a single management, with the objective of producing for sale or subsistence, regardless of size, legal form (own, partnership, lease, etc.) or location (rural or urban). The agricultural holdings were classified according to the legal status of the producer as: individual holder, condominium, consortium or partnership; cooperative; incorporated or limited liability company; public utility institutions (church, NGO, hospital), or government.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Frame The 2000 Population and Housing Census and the cartographic documentation constituted the source of the AC 2006 frame. No list frames were available in digital media with georeferenced addresses of the holdings. Census coverage was ensured on the basis of the canvassing of the EAs by enumerators.

(b) Complete and/or sample enumeration methods The AC 2006 was a complete enumeration operation of all agricultural holdings in the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

An electronic questionnaire was used for data collection on:

Total agricultural establishments Total area of agricultural establishments Total area of crops Area of pastures Area of woodlands Total tractors Implements Machinery and vehicles Characteristics of the establishment and of the producer Total staff employed Total cattle, buffallo, goats, Sheep, pigs, poultry (chickens, fowls, chickens and chicks) Other birds (ducks, geese, teals, turkeys, quails, ostriches, partridges, pheasants and others) Plant production

The AC 2006 covered all 16 items recommended by FAO under the WCA 2010.

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING The entire data collection and supervision software was developed in house by IBGE, using the Visual Studio platform in the Microsoft Operations Manager 2005 environment and Microsoft SQL Server 2000, with the assistance of Microsoft Brazil consulting. In addition, the GEOPAD application was installed to view, navigate and view maps and use GPS guidance. Updated versions of the software were installed automatically as soon as census enumerators connected the PDAs to the central server to transmit the data collected. Once internally validated by the device, the data were immediately transmitted to the database at the IBGE state unit. The previous AC (1996) served as the basis for defining the parameter values for the electronic editing process.

(b) CENSUS DATA QUALITY Automatic validation was incorporated into PDAs. Previously programmed skip patterns and real-time edits, performed during enumeration, ensured faster and more reliable interviews. In addition, the Bluetooth® technology incorporated into the PDAs allowed for direct data transmission to IBGE's central mainframe by each of enumerators on a weekly basis.

Data appraisal

The preliminary census results were published in 2007. The final results were released in 2009 through a printed volume and CD-ROMs. The census results were disseminated at the national and subnational scope (country, state and municipality) and are available online at IBGE's website.

This study is to examine the experiences of BEED students in new normal education with blended learning approaches Amidst Covid-19 Pandemic. To achieve this, quantitative-inferential and descriptive research method was adopted. This study focuses on the BEED students of College of Teacher Education at Sultan Kudarat State University. Total-enumeration sampling technique is utilized as it will be dealing with only 35 students, a total respondent which is less than 1000 research sample population. Research finding shows that new normal education implementation responded by BEED students shows that the topics delivered in modular learning approach cannot be easily understood solely by the students. Online learning using messenger chatting or texting with the teachers’ messages were sometimes confusing and limiting the meaning of the message(s) taught which in returns had limited as well the understanding of the students about the topic received. Virtual classes and topic discussions in an online classroom meeting ---most students were just connecting via Pesonet and that the internet connectivity is not consistent. Learning experiences of the students in the new normal education specifically in the answering of the students’ learning activities had been observed by the study as neither easy nor difficult. The availability of educational information technology devices for the online class communication are not similarly true to all due to economic deficiency. Thus, students find difficulties in attending classes. The internet connectivity of the student-teacher and their communication to receive updates about the class and in complying to the class requirements is very irregular and not consistent

Niger (Niamey) Round 3 Service Delivery Point (SQ) survey used a sampling strategy stratified by Niamey’s 5 communes to select a total of 33 enumeration areas (EAs), drawn from the sampling frame provided by the 2012 General Census of Population and Housing conducted by Niger’s National Statistics Institute (INS). Each EA was listed and mapped. Public facilities were included if a selected EA fell within the catchment area. Private facilities were included if they fell within the boundaries of the EA. Data collection was conducted between November and December 2016. The final sample is 27 complete SDP surveys. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42

Usage

The State Ambulatory Surgery and Services Databases (SASD) are State-specific files that include data for ambulatory surgery and other outpatient services from hospital-owned facilities. In addition, some States provide ambulatory surgery and outpatient services from nonhospital-owned facilities. The uniform format of the SASD helps facilitate cross-State comparisons. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within geographic areas or States.

Usage

The State Ambulatory Surgery and Services Databases (SASD) are State-specific files that include data for ambulatory surgery and other outpatient services from hospital-owned facilities. In addition, some States provide ambulatory surgery and outpatient services from nonhospital-owned facilities. The uniform format of the SASD helps facilitate cross-State comparisons. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within geographic areas or States.

The State Emergency Department Databases (SEDD) are part of the family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The SEDD are a set of databases that capture discharge information on all emergency department visits that do not result in an admission. The SEDD combined with SID discharges that originate in the emergency department are well suited for research and policy questions that require complete enumeration of hospital-based emergency departments within market areas or states. Data may not be available for all states across all years.

This dataset contains detailed vulnerability information extracted from the CISA vulnrichment repository, including CVE details, CVSS v3.1 metrics, SSVC decision scores, and affected products. The data is automatically updated daily to provide the most current vulnerability intelligence.

Dataset Contents

• CVE Identifiers: Unique vulnerability identifiers
• CVSS v3.1 Metrics: Complete scoring including base score, attack vector, complexity, etc.
• SSVC Scores: Stakeholder-Specific Vulnerability Categorization decisions
• Impact Analysis: Technical impact assessments and exploitability information
• Product Information: Affected vendors, products, and vulnerable versions
• CWE Classifications: Common Weakness Enumeration mappings
• KEV Status: CISA Known Exploited Vulnerabilities inclusion

Data Schema

The CSV contains the following columns: - cve_id: CVE identifier (e.g., CVE-2023-12345) - published_date: Initial publication date - updated_date: Last modification date - base_score: CVSS v3.1 base score (0-10) - base_severity: Severity rating (NONE, LOW, MEDIUM, HIGH, CRITICAL) - attack_vector: Attack vector (NETWORK, ADJACENT_NETWORK, LOCAL, PHYSICAL) - attack_complexity: Attack complexity (LOW, HIGH) - privileges_required: Required privileges (NONE, LOW, HIGH) - user_interaction: User interaction requirement (NONE, REQUIRED) - scope: Scope (UNCHANGED, CHANGED) - confidentiality_impact: CIA impact (NONE, LOW, HIGH) - integrity_impact: CIA impact (NONE, LOW, HIGH) - availability_impact: CIA impact (NONE, LOW, HIGH) - ssvc_exploitation: SSVC exploitation status - ssvc_automatable: SSVC automatability assessment - ssvc_technical_impact: SSVC technical impact - ssvc_decision: SSVC final decision (Track, Attend, Act) - cisa_kev: Known Exploited Vulnerability status (TRUE/FALSE) - cisa_kev_date: Date added to KEV catalog - impacted_vendor: Primary affected vendor - impacted_products: List of affected products - vulnerable_versions: List of vulnerable versions - cwe_number: Common Weakness Enumeration ID - cwe_description: CWE description

Update Frequency

This dataset is automatically updated daily using GitHub Actions, ensuring you always have access to the latest vulnerability data from CISA's vulnrichment repository.

Data Source

Data is extracted from the official CISA vulnrichment repository: https://github.com/cisagov/vulnrichment

Use Cases

• Vulnerability research and analysis
• Risk assessment and prioritization
• Security dashboard development
• Threat intelligence integration
• Academic research in cybersecurity
• Compliance and audit activities

License

This dataset is provided under the Creative Commons CC0 1.0 Universal license, allowing for unrestricted use.

Abstract

The Pakistan Demographic and Health Survey PDHS 2017-18 was the fourth of its kind in Pakistan, following the 1990-91, 2006-07, and 2012-13 PDHS surveys.

The primary objective of the 2017-18 PDHS is to provide up-to-date estimates of basic demographic and health indicators. The PDHS provides a comprehensive overview of population, maternal, and child health issues in Pakistan. Specifically, the 2017-18 PDHS collected information on:

• Key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and within the country’s eight regions
• Direct and indirect factors that determine levels and trends of fertility and child mortality
• Contraceptive knowledge and practice
• Maternal health and care including antenatal, perinatal, and postnatal care
• Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of children under age 5 and women age 15-49
• Key aspects of family health, including vaccination coverage and prevalence of diseases among infants and children under age 5
• Knowledge and attitudes of women and men about sexually transmitted infections (STIs), including HIV/AIDS, and potential exposure to risk
• Women's empowerment and its relationship to reproductive health and family planning
• Disability level
• Extent of gender-based violence
• Migration patterns

The information collected through the 2017-18 PDHS is intended to assist policymakers and program managers at the federal and provincial government levels, in the private sector, and at international organisations in evaluating and designing programs and strategies for improving the health of the country’s population. The data also provides information on indicators relevant to the Sustainable Development Goals.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2017-18 PDHS is a complete list of enumeration blocks (EBs) created for the Pakistan Population and Housing Census 2017, which was conducted from March to May 2017. The Pakistan Bureau of Statistics (PBS) supported the sample design of the survey and worked in close coordination with NIPS. The 2017-18 PDHS represents the population of Pakistan including Azad Jammu and Kashmir (AJK) and the former Federally Administrated Tribal Areas (FATA), which were not included in the 2012-13 PDHS. The results of the 2017-18 PDHS are representative at the national level and for the urban and rural areas separately. The survey estimates are also representative for the four provinces of Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan; for two regions including AJK and Gilgit Baltistan (GB); for Islamabad Capital Territory (ICT); and for FATA. In total, there are 13 secondlevel survey domains.

The 2017-18 PDHS followed a stratified two-stage sample design. The stratification was achieved by separating each of the eight regions into urban and rural areas. In total, 16 sampling strata were created. Samples were selected independently in every stratum through a two-stage selection process. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at different levels, and by using a probability-proportional-to-size selection at the first stage of sampling.

The first stage involved selecting sample points (clusters) consisting of EBs. EBs were drawn with a probability proportional to their size, which is the number of households residing in the EB at the time of the census. A total of 580 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters, and a fixed number of 28 households per cluster was selected with an equal probability systematic selection process, for a total sample size of approximately 16,240 households. The household selection was carried out centrally at the NIPS data processing office. The survey teams only interviewed the pre-selected households. To prevent bias, no replacements and no changes to the pre-selected households were allowed at the implementing stages.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the 2017-18 PDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, Biomarker Questionnaire, Fieldworker Questionnaire, and the Community Questionnaire. The first five questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Pakistan. The Community Questionnaire was based on the instrument used in the previous rounds of the Pakistan DHS. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. The survey protocol was reviewed and approved by the National Bioethics Committee, Pakistan Health Research Council, and ICF Institutional Review Board. After the questionnaires were finalised in English, they were translated into Urdu and Sindhi. The 2017-18 PDHS used paper-based questionnaires for data collection, while computerassisted field editing (CAFE) was used to edit the questionnaires in the field.

Cleaning operations

The processing of the 2017-18 PDHS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via IFSS to the NIPS central office in Islamabad. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing was carried out in the central office, which involved resolving inconsistencies and coding the openended questions. The NIPS data processing manager coordinated the exercise at the central office. The PDHS core team members assisted with the secondary editing. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage as it maximised the likelihood of the data being error-free and accurate. The secondary editing of the data was completed in the first week of May 2018. The final cleaning of the data set was carried out by The DHS Program data processing specialist and completed on 25 May 2018.

Response rate

A total of 15,671 households were selected for the survey, of which 15,051 were occupied. The response rates are presented separately for Pakistan, Azad Jammu and Kashmir, and Gilgit Baltistan. Of the 12,338 occupied households in Pakistan, 11,869 households were successfully interviewed, yielding a response rate of 96%. Similarly, the household response rates were 98% in Azad Jammu and Kashmir and 99% in Gilgit Baltistan.

In the interviewed households, 94% of ever-married women age 15-49 in Pakistan, 97% in Azad Jammu and Kashmir, and 94% in Gilgit Baltistan were interviewed. In the subsample of households selected for the male survey, 87% of ever-married men age 15-49 in Pakistan, 94% in Azad Jammu and Kashmir, and 84% in Gilgit Baltistan were successfully interviewed.

Overall, the response rates were lower in urban than in rural areas. The difference is slightly less pronounced for Azad Jammu and Kashmir and Gilgit Baltistan. The response rates for men are lower than those for women, as men are often away from their households for work.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017-18 Pakistan Demographic and Health Survey (2017-18 PDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017-18 PDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that

Abstract

Malawi Conditional Cash Transfer Program (CCT) is a randomized cash transfer intervention targeting young women in Zomba region. The program provides incentives to current schoolgirls and recent dropouts to stay in or return to school. The incentives include average payment of US$10 a month conditional on satisfactory school attendance and direct payment of secondary school fees.

The CCT program started at the beginning of the Malawian school year in January 2008 and continued until November 2009. The impact evaluation study was designed to evaluate the impact of the program on various demographic and health outcomes of its target population, such as nutritional health, sexual behavior, fertility, and subsequent HIV risk.

Baseline data collection was administered from September 2007 to January 2008. The research targeted girls and young women, between the ages of 13 and 22, who were never married. Overall, 3,810 girls and young women were surveyed in the first round. The follow-up survey was carried out from October 2008 to February 2009. The third round was conducted between March and September 2010, after Malawi Conditional Cash Transfer Program was completed. The fourth round started in April 2012 and will continue until September 2012.

Datasets from the baseline round are documented here.

Enumeration Areas (EAs) in the study district of Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. 176 enumeration areas were randomly sampled out of a total of 550 EAs using three strata: urban areas, rural areas near Zomba Town, and rural areas far from Zomba Town.

Baseline schoolgirls in treatment enumeration areas were randomly assigned to receive either conditional or unconditional transfers, or no transfers at all. A multi-topic questionnaire was administered to the heads of households, where the selected sample respondents resided, as well as to girls and young women.

Geographic coverage

Zomba district.

Zomba district in the Southern region was chosen as the site for this study for several reasons. First, it has a large enough population within a small enough geographic area rendering field work logistics easier and keeping transport costs lower. Zomba is a highly populated district, but distances from the district capital (Zomba Town) are relatively small. Second, characteristic of Southern Malawi, Zomba has a high rate of school dropouts and low educational attainment. Third, unlike many other districts, Zomba has the advantage of having a true urban center as well as rural areas. As the study sample was stratified to get representative samples from urban areas (Zomba town), rural areas near Zomba town, and distant rural areas in the district, we can analyze the heterogeneity of the impacts by urban/rural areas. Finally, while Southern Malawi, which includes Zomba, is poorer, has lower levels of education, and higher rates of HIV than Central and Northern Malawi, these differences are relative considering that Malawi is one of the poorest countries in the world with one of the highest rates of HIV prevalence.

Analysis unit

• Households;
• Girls and young women.

Universe

The survey covers never married girls and young women between the ages of 13 and 22 in Zomba district.

Kind of data

Sample survey data [ssd]

Sampling procedure

First, 176 enumeration areas (EA) were randomly sampled out of a total of 550 EAs using three strata in the study district of Zomba. Each of these 176 EAs were then randomly assigned treatment or control status. The three strata are urban, rural areas near Zomba Town, and rural areas far from Zomba Town. Rural areas were defined as being near if they were within a 16-kilometer radius of Zomba Town. Researchers did not sample any EAs in TA Mbiza due to safety concerns (112 EAs).

Enumeration areas (EAs) in Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. The sample of EAs was stratified by distance to the nearest township or trading centre. Of the 550 EAs in Zomba, 50 are in Zomba town and an additional 30 are classified as urban (township or trading center), while the remaining 470 are rural (population areas, or PAs). The stratified random sample of 176 EAs consisted of 29 EAs in Zomba town, eight trading centers in Zomba rural, 111 population areas within 16 kilometers of Zomba town, and 28 EAs more than 16 kilometers from Zomba town.

After selecting sample EAs, all households were listed in the 176 sample EAs using a short two-stage listing procedure. The first form, Form A, asked each household the following question: "Are there any never-married girls in this household who are between the ages of 13 and 22?" This form allowed the field teams to quickly identify households with members fitting into the sampling frame, thus significantly reducing the costs of listing. If the answer received on Form A was a "yes", then Form B was filled to list members of the household to collect data on age, marital status, current schooling status, etc.

From this researchers could categorize the target population into two main groups: those who were out of school at baseline (baseline dropouts) and those who were in school at baseline (baseline schoolgirls). These two groups comprise the basis of our sampling frame. In each EA, enumerators sampled all eligible dropouts and 75%-100% of all eligible school girls, where the percentage depended on the age of the baseline schoolgirl. This sampling procedure led to a total sample size of 3,810 (in the first round, and 3,805 in follow-up rounds) with an average of 5.1 dropouts and 16.7 schoolgirls per EA.

Mode of data collection

Face-to-face [f2f]

Research instrument

The annual household survey consists of a multi-topic questionnaire administered to the households in which the selected sample respondents reside. The survey consists of two parts: one that is administered to the head of the household and another that is administered to the core respondent - the sampled girl from the target population. The former collects information on the household roster, dwelling characteristics, household assets and durables, shocks and consumption. The core respondent survey provides information about her family background, her education and labor market participation, her health, her dating patterns, sexual behavior, marital expectations, knowledge of HIV/AIDS, her social networks, as well as her own consumption of girl-specific goods (such as soaps, mobile phone airtime, clothing, braids, sodas and alcoholic drinks, etc.).

For the Niger (Niamey) Round 5 Household and Female (HQFQ) survey, a total of 33 enumeration areas (EAs) were selected using probability promotional to size, from a sampling frame provide by the Fourth General Census of Population and Housing, conducted by Niger’s National Statistics Institute in 2012. Structures, households, and service delivery points (SDPs) were enumerated in each selected EA. Thirty-five households were selected randomly from each EA. All eligible women, aged 15 to 49, in the sampled households were interviewed. Data collection occurred from June to August 2018. In Niamey, a total of 5,847 households and 1,296 women were interviewed. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/b88s-zx32