The Input Survey dataset, managed by the Department of Agriculture, Cooperation & Farmers Welfare (DAC&FW), is an essential tool that captures data related to agricultural inputs across India. This survey delves into various aspects of farming, detailing the usage of seeds, fertilizers, pesticides, machinery, irrigation methods, and other critical agricultural resources. By collecting this data periodically, the dataset offers insights into the evolving patterns of input utilization, facilitating the identification of trends, gaps, and potential areas of improvement. Policymakers, researchers, and agricultural stakeholders rely on the Input Survey dataset to devise strategies, frame policies, and implement interventions that aim to enhance the efficiency and sustainability of the Indian agricultural sector.

URL: https://geoscience.data.qld.gov.au/dataset/em000207

NO DATA AVAILABLE - This survey has been mentioned or described in reports but no geophysical data has been submitted to the Geological Survey of Queensland

Tickets Input survey results for OC Fair Neighbors, CA conducted by FlashVote

Library Input survey results for North Richland Hills, TX conducted by FlashVote

The Community Input Survey: Renovation of the R.P. Bell Library, Hub for Innovation & Learning survey was conducted to understand the Mount Allison University communities' priorities for a major library renovation project. Students, faculty, staff, and administrators answered questions about proposed functions, services, technologies, resources, and use case vignettes.

Public Input Survey

Bicycle Park Input survey results for Pacifica, CA conducted by FlashVote

General file description This xlsx document contains the literature list that forms the basis of the paper 'A Survey of Methods and Input Data Types for House Price Prediction' by Geerts, M., vanden Broucke, S. and De Weerdt, J. The Excel document contains seven sheets, relating to the phases described in the survey. Phase3 This sheet contains the literature list for the end of Phase 2 and the start of Phase 3. It has 590 rows and 19 columns. Each row contains the citation information of one article. The columns describe the ID, Authors, Title, Year, Source title, Volume, Issue, DOI, ISSN, ISBN, PubMed, Publisher, Document Type, Language, Keywords, Link, Book DOI, Algorithmic (Title) and Algorithmic (Abstract). The latter two columns are used to indicate whether the articles describe an algorithmic approach to predict house prices based on the title and the abstract respectively. These two columns take the values 'Yes', 'No', and 'Maybe', and were completed during Phase 3. Phase4 This sheet contains the literature list for the end of Phase 3 and the start of Phase 4. It has 116 rows and 20 columns. Each row contains the citation information of one article. The columns describe the ID, Authors, Title, Year, Source title, Volume, Issue, DOI, ISSN, ISBN, PubMed, Publisher, Document Type, Language, Keywords, Link, Book DOI, Algorithmic (Title), Algorithmic (Abstract) and Reading. All columns are the same as in the first sheet, except for the three last columns. The columns Algorithmic (Title) and Algorithmic (Abstract) now only contain the value 'Yes' as only the articles that describe an algorithm are retained in Phase 3. The column Reading describes the outcome of Phase 4. This columns is empty if the article is retained in this phase and describes the reason if it is not retained. Phase4(end) This sheet contains the literature list for the end of Phase 4. It has 94 rows and 20 columns. Each row contains the citation information of one article. The columns describe the ID, Authors, Title, Year, Source title, Volume, Issue, DOI, ISSN, ISBN, PubMed, Publisher, Document Type, Language, Keywords, Link, Book DOI, Algorithmic (Title), Algorithmic (Abstract) and Reading. All columns are the same as in the second sheet. The column Reading is now empty because the articles that were not retained in Phase 4 are removed from the list. Data table This sheet contains a table of the literature at the end of Phase 4 with indications of input data types used in the articles, the data novelty score and the cluster that the articles belong to. It has 95 rows, where each row contains the information of one article, except the last 'Total' row. It contains 21 columns : ID: This is the same identifier as in the previous sheets. Column1: This is a new identifier, based on an ordering on year and author. Authors: Same as before. Title: Same as before. Year: Same as before. Structural, Temporal data, Socioeconomic, Environmental, POI, Basic spatial, Location, Eucl Distances, Adv Spatial, Network Distance, Topographical data, Graphs, Images, Text: These are the different input data types. The cell is filled with 'X' if the corresponding article is using the input data type described in the column name. Score: This column indicates the data novelty score, calculated as explained in the paper based on the sheet 'Rules Data novelty score'. Cluster: This column indicates the cluster number as explained in the Discussion section of the paper. Rules Data novelty score This sheet contains 15 rows, of which the first contains the titles, and two columns. The first columns contains the input data types as in the previous sheet and the second column contains the respective novelty scores. Model table This sheet contains a table of the literature at the end of Phase 4 with indications of model types used in the articles, the model novelty score and the cluster that the articles belong to. It has 95 rows, where each row contains the information of one article, except the last 'Total' row. It contains 21 columns : ID: Same as before. Column1: Same as before Authors: Same as before. Title: Same as before. Year: Same as before. MRA, Kriging, SEM, SVC, Time Series, FL, NN, DT, RF, GBT, SVM, ANN, (Other) Ensembles, DL: These are the different model types. The cell is filled with 'X' if the corresponding article is using the model type described in the column name. Score: This column indicates the model novelty score, calculated as explained in the paper based on the sheet 'Rules Model novelty score'. Cluster: This column indicates the cluster number as explained in the Discussion section of the paper. Rules Model novelty score This sheet contains 15 rows, of which the first contains the titles, and two columns. The first columns contains the model types as in the previous sheet and the second column contains the respective novelty scores.

There is no abstract created for this record There is no abstract created for this record

URL: https://geoscience.data.qld.gov.au/dataset/cr009324

EPM 2151, (PART A)- KAMARGA INPUT SURVEY REPORT, (PART B)- COMPUTER INTERPRETATION OF ANOMALIES

Background: “In 2006, the Seattle Police Department began surveying members of the public (customers) who had personal contact with an officer after calling 9-1-1. The surveys have been conducted two to four times a year, and a total of 44 surveys have been conducted to date. These surveys have been designed to assess customers’ experiences and satisfaction with the service provided by the Seattle Police Department, and the results of the surveys have been used to assess service delivery; examine differences between precincts; identify strategies and tactics to achieve specific service objectives; and provide feedback to officers, precinct captains, and watch lieutenants. This report presents the results of the September 2019 customer survey and compares the September 2019 survey results to results from the 13 other surveys conducted since March 2016.” Research Methods. “Similar to the previous surveys, 200 customers who called 9-1-1 and had an officer dispatched to provide assistance were interviewed by telephone for this survey. All of the customers interviewed had called 9-1-1 between August 21 and August 29, 2019, and were randomly selected from lists of 9-1-1 callers who had an officer dispatched to provide assistance, excluding sensitive cases such as domestic violence calls. The interviews were completed between September 3 and September 10, 2019. The interviews were approximately 10 to 12 minutes long. The questionnaire used in the interviews was developed with Department input and approval. During the course of this research, some questions have been added to or deleted from the survey questionnaire to reflect the changing information needs of the Department. However, questions about customers’ overall satisfaction with their experience with the Department after calling 9-1-1, experiences with and opinions of the officer who first visited after the call to 9-1-1, opinions of the Seattle Police Department overall, and satisfaction with the service provided by the 9-1-1 operator have been included in every survey. Since late 2006 and early 2007, the surveys also included questions about customers’ feelings of safety in Seattle.”

Downtown Parking Input survey results for Keene, NH conducted by FlashVote

A baseline survey of ISPs to allow for before-after and with or without comparison of the target outcome of the project interventions.

Data for both academic levels about - the number of applications to the first recruitment session of the Italian National Scientific Qualification (NSQ); - the number of scholars that obtained the qualification after the evaluation process of the NSQ; - total number of publications available for each academic recruitment field; - average number of publications per application

The uploaded data includes the programs used in the research process, input-output tables, and simulation results under four scenarios. The programs used can be viewed by opening the folder, and the functions and contents of each program are labeled with file names. The internal code of the program is also explained and annotated accordingly. The two folders for 2017 and 2020 respectively contain the non competitive input-output tables for distinguishing domestic and foreign investment used in the research, as well as simulation results under four scenarios.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

5,350 named individuals in the United Kingdom were systematically selected from the Electoral Register, which was stratified by local authority, and ordered by postcode.

Addresses were checked against Laing & Bussion.s Care Home and Hospital Information database and 14 addresses were removed. A further 336 named individuals were systematically selected and removed from the remaining sample, using a random start and fixed interval method on the sample sorted by local authority and postcode, leaving 5,000 addresses for the usable sample.

The 5,000 sampled individuals were sorted by local authority and postcode.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

The 2015-16 Armenia Demographic and Health Survey (2015-16 ADHS) is the fourth in a series of nationally representative sample surveys designed to provide information on population and health issues. It is conducted in Armenia under the worldwide Demographic and Health Surveys program. Specifically, the objective of the 2015-16 ADHS is to provide current and reliable information on fertility and abortion levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of young children, childhood mortality, maternal and child health, domestic violence against women, child discipline, awareness and behavior regarding AIDS and other sexually transmitted infections (STIs), and other health-related issues such as smoking, tuberculosis, and anemia. The survey obtained detailed information on these issues from women of reproductive age and, for certain topics, from men as well.

The 2015-16 ADHS results are intended to provide information needed to evaluate existing social programs and to design new strategies to improve the health of and health services for the people of Armenia. Data are presented by region (marz) wherever sample size permits. The information collected in the 2015-16 ADHS will provide updated estimates of basic demographic and health indicators covered in the 2000, 2005, and 2010 surveys.

The long-term objective of the survey includes strengthening the technical capacity of major government institutions, including the NSS. The 2015-16 ADHS also provides comparable data for longterm trend analysis because the 2000, 2005, 2010, and 2015-16 surveys were implemented by the same organization and used similar data collection procedures. It also adds to the international database of demographic and health–related information for research purposes.

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 0-4 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 sample was designed to produce representative estimates of key indicators at the national level, for Yerevan, and for total urban and total rural areas separately. Many indicators can also be estimated at the regional (marz) level.

The sampling frame used for the 2015-16 ADHS is the Armenia Population and Housing Census, which was conducted in Armenia in 2011 (APHC 2011). The sampling frame is a complete list of enumeration areas (EAs) covering the whole country, a total number of 11,571 EAs, provided by the National Statistical Service (NSS) of Armenia, the implementing agency for the 2015-16 ADHS. This EA frame was created from the census data base by summarizing the households down to EA level. A representative probability sample of 8,749 households was selected for the 2015-16 ADHS sample. The sample was selected in two stages. In the first stage, 313 clusters (192 in urban areas and 121 in rural areas) were selected from a list of EAs in the sampling frame. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey. Appendix A provides additional information on the sample design of the 2015-16 Armenia DHS. Because of the approximately equal sample size in each marz, the sample is not self-weighting at the national level, and weighting factors have been calculated, added to the data file, and applied so that results are representative at the national level.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used for the 2015-16 ADHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Armenia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Armenian. They were pretested in September-October 2015.

Cleaning operations

The processing of the 2015-16 ADHS data began shortly after fieldwork commenced. All completed questionnaires were edited immediately by field editors while still in the field and checked by the supervisors before being dispatched to the data processing center at the NSS central office in Yerevan. These completed questionnaires were edited and entered by 15 data processing personnel specially trained for this task. All data were entered twice for 100 percent verification. Data were entered using the CSPro computer package. The concurrent processing of the data was an advantage because the senior ADHS technical staff were able to advise field teams of problems detected during the data entry. In particular, tables were generated to check various data quality parameters. Moreover, the double entry of data enabled easy comparison and identification of errors and inconsistencies. As a result, specific feedback was given to the teams to improve performance. The data entry and editing phase of the survey was completed in June 2016.

Response rate

A total of 8,749 households were selected in the sample, of which 8,205 were occupied at the time of the fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. The number of occupied households successfully interviewed was 7,893, yielding a household response rate of 96 percent. The household response rate in urban areas (96 percent) was nearly the same as in rural areas (97 percent).

In these households, a total of 6,251 eligible women were identified; interviews were completed with 6,116 of these women, yielding a response rate of 98 percent. In one-half of the households, a total of 2,856 eligible men were identified, and interviews were completed with 2,755 of these men, yielding a response rate of 97 percent. Among men, response rates are slightly lower in urban areas (96 percent) than in rural areas (97 percent), whereas rates for women are the same in urban and in rural areas (98 percent).

The 2015-16 ADHS achieved a slightly higher response rate for households than the 2010 ADHS (NSS 2012). The increase is only notable for urban households (96 percent in 2015-16 compared with 94 percent in 2010). Response rates in all other categories are very close to what they were in 2010.

Sampling error estimates

SAS computer software were used to calculate sampling errors for the 2015-16 ADHS. The programs used the Taylor linearization method of variance estimation for means or proportions and the Jackknife repeated replication method for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Nutritional status of children based on the NCHS/CDC/WHO International Reference Population - Vaccinations by background characteristics for children age 18-29 months

See details of the data quality tables in Appendix C of the survey final report.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Abstract

The 2016 Integrated Household Panel Survey (IHPS) was launched in April 2016 as part of the Malawi Fourth Integrated Household Survey fieldwork operation. The IHPS 2016 targeted 1,989 households that were interviewed in the IHPS 2013 and that could be traced back to half of the 204 enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11. The 2019 IHPS was launched in April 2019 as part of the Malawi Fifth Integrated Household Survey fieldwork operations targeting the 2,508 households that were interviewed in 2016. The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. Available as part of this project is the IHPS 2019 data, the IHPS 2016 data as well as the rereleased IHPS 2010 & 2013 data including only the subsample of 102 EAs with updated panel weights. Additionally, the IHPS 2016 was the first survey that received complementary financial and technical support from the Living Standards Measurement Study - Plus (LSMS+) initiative, which has been established with grants from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Living Standards Measurement Study (LSMS) team, in collaboration with the World Bank Gender Group and partner national statistical offices. The LSMS+ aims to improve the availability and quality of individual-disaggregated household survey data, and is, at start, a direct response to the World Bank IDA18 commitment to support 6 IDA countries in collecting intra-household, sex-disaggregated household survey data on 1) ownership of and rights to selected physical and financial assets, 2) work and employment, and 3) entrepreneurship - following international best practices in questionnaire design and minimizing the use of proxy respondents while collecting personal information. This dataset is included here.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The IHPS 2016 and 2019 attempted to track all IHPS 2013 households stemming from 102 of the original 204 baseline panel enumeration areas as well as individuals that moved away from the 2013 dwellings between 2013 and 2016 as long as they were neither servants nor guests at the time of the IHPS 2013; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE:

A sub-sample of IHS3 2010 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS 2013) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban. The IHPS 2013 main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Given budget and resource constraints, for the IHPS 2016 the number of sample EAs in the panel was reduced to 102 out of the 204 EAs. As a result, the domains of analysis are limited to the national, urban and rural areas. Although the results of the IHPS 2016 cannot be tabulated by region, the stratification of the IHPS by region, urban and rural strata was maintained. The IHPS 2019 tracked all individuals 12 years or older from the 2016 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD - Household and Geographic Area Identification and Survey Information (data of interview, enumerator's and supervisors codes, etc.) - Household Roster - Education - Health - Time Use and Labor - Housing - Food Consumption (over past one week) - Food Security - Non-food Expenditures - over past one week and one month - Non-food Expenditures - over past three months - Non-food Expenditures - over past 12 months - Durable Goods - Farm Implements, Machinery, and Structures - Household Enterprises - Children Living Elsewhere - Other Income - Gifts Given Out - Social Safety Nets - Credit - Subjective Assessment of Well-being - Shocks and Coping Strategies - Child Anthropometry - Deaths in Household

AGRICULTURE - Garden Roster (both for rainy season and dry (dimba) season) - Plot Roster (both for rainy season and dry (dimba) season) - Garden Details (both for rainy season and dry (dimba) season) - Plot Details (both for rainy season and dry (dimba) season) - Coupon Use (rainy season) - Other Inputs (both for rainy season and dry (dimba) season) - Crops (both for rainy season and dry (dimba) season) - Seeds (both for rainy season and dry (dimba) season) - Sales/ Storage (both for rainy season and dry (dimba) season) - Tree/ Permanent Crop Production (last 12 months) - Tree/ Permanent Crop Sales/ Storage (last 12 months) - Livestock - Livestock Products - Access to Extension Services - Network Roster

FISHERY - Fisheries Calendar - Fisheries Labor (last high season and last low season) - Fisheries Inputs (last high season and last low season) - Fisheries Output (last high season and last low season) - Fish Trading (last high season and last low season)

COMMUNITY - Roster of Informants - Basic Information - Economic Activities - Agriculture - Changes - Community Needs, Actions and Achievements - Communal Resource Management - Communal Organization

Cleaning operations

a. Data Entry Platform To ensure data quality and timely availability of data, the IHPS 2019 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHPS 2019, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer that the NSO provided. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

b. Data Management The IHPS 2019 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHPS 2019 Interviews were mainly collected in “sample” mode (assignments generated from headquarters) and a few in “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample. This hybrid approach was necessary to aid the tracking operations whereby an enumerator could quickly create a tracking assignment considering that they were mostly working in areas with poor network connection and hence could not quickly receive tracking cases from Headquarters. The range and consistency checks built into the application was informed by the LSMS-ISA experience with the IHS3 2010/11, IHPS 2013 and IHPS 2016. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (the NSO management) assigned work to the supervisors based on their regions of coverage. The supervisors then made assignments to the enumerators linked to their supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHPS 2019 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to Stata for other consistency checks, data cleaning, and analysis.

c. Data Cleaning The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field-based field teams utilizing error messages generated by the Survey Solutions application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent. The supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some supervisors preferred to

The IZA Evaluation Dataset Survey (IZA ED) was developed in order to obtain reliable longitudinal estimates for the impact of Active Labor Market Policies (ALMP). Moreover, it is suitable for studying the processes of job search and labor market reintegration. The data allow analyzing dynamics with respect to a rich set of individual and labor market characteristics. It covers the initial period of unemployment as well as long-term outcomes, for a total period of up to 3 years after unemployment entry. A longitudinal questionnaire records monthly labor market activities and their duration in detail for the mentioned period. These activities are, for example, employment, unemployment, ALMP, other training etc. Available information covers employment status, occupation, sector, and related earnings, hours, unemployment benefits or other transfer payments. A cross-sectional questionnaire contains all basic information including the process of entering into unemployment, and demographics. The entry into unemployment describes detailed job search behavior such as search intensity, search channels and the role of the Employment Agency. Moreover, reservation wages and individual expectations about leaving unemployment or participating in ALMP programs are recorded. The available demographic information covers employment status, occupation and sector, as well as specifics about citizenship and ethnic background, educational levels, number and age of children, household structure and income, family background, health status, and workplace as well as place of residence regions. The survey provides as well detailed information about the treatment by the unemployment insurance authorities, imposed labor market policies, benefit receipt and sanctions. The survey focuses additionally on individual characteristics and behavior. Such co-variates of individuals comprise social networks, ethnic and migration background, relations and identity, personality traits, cognitive and non-cognitive skills, life and job satisfaction, risky behavior, attitudes and preferences. The main advantages of the IZA ED are the large sample size of unemployed individuals, the accuracy of employment histories, the innovative and rich set of individual co-variates and the fact that the survey measures important characteristics shortly after entry into unemployment.