To assist our country's industrial innovation, the Intellectual Property Office of the Ministry of Economic Affairs, through its patent database, compiled the number of patent applications filed by legal entities and individuals in our country from 2007 to 2009, and conducted an in-depth analysis of patent application trends, including analysis by patent classification and analysis of six emerging industries: "green energy," "biotechnology," "medical care," "refined agriculture," "cultural creativity," and "tourism." Through the patent application status, analytical observations and concise charts, they have identified the products and market trends that the industries plan to launch in the next two to three years.

Abstract

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys were organised to track changes over time.

Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated - through Swahili briefs and posters - to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office.

Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Funding: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office - Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.

Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, 'interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.

Geographic coverage

Subnational

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The CWIQ surveys were sampled to be representative at district level. Data from the 2002 Census was used to put together a list of all villages in each district. In the first stage of the sampling process villages were chosen proportional to their population size. In a second stage the subvillage (kitongoji) was chosen within the village through simple random sampling. In the selected sub-village (also referred to as cluster or enumeration area), all households were listed and 15 households were randomly selected. In total 450 households in 30 clusters were visited. All households were given statistical weights reflecting the number of households that they represent.

Mode of data collection

Face-to-face [f2f]

Research instrument

CWIQ is an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition, as well as utilisation of and satisfaction with social services. An extra section on governance and satisfaction with people in public office was added specifically for this survey.

The standardised nature of the questionnaire allows comparison between districts and regions within and across countries, as well as monitoring change in a district or region over time.

The 2006/7 questionnaire is in Swahili, but it closely follows the 2000 generic CWIQ questionnaire, which is included in external resources, and all variables and values are labeled in English.

Cleaning operations

The data entry was done by scanning the questionnaires, to minimise data entry errors and thus ensure high quality in the final dataset.

Motivation

This dataset was created to pilot techniques for creating synthetic data from datasets containing sensitive and protected information in the local government context. Synthetic data generation replaces actual data with representative data generated from statistical models; this preserves the key data properties that allow insights to be drawn from the data while protecting the privacy of the people included in the data. We invite you to read the Understanding Synthetic Data white paper for a concise introduction to synthetic data.

This effort was a collaboration of the Urban Institute, Allegheny County’s Department of Human Services (DHS) and CountyStat, and the University of Pittsburgh’s Western Pennsylvania Regional Data Center.

Collection

The source data for this project consisted of 1) month-by-month records of services included in Allegheny County's data warehouse and 2) demographic data about the individuals who received the services. As the County’s data warehouse combines this service and client data, this data is referred to as “Integrated Services data”. Read more about the data warehouse and the kinds of services it includes here.

Preprocessing

Synthetic data are typically generated from probability distributions or models identified as being representative of the confidential data. For this dataset, a model of the Integrated Services data was used to generate multiple versions of the synthetic dataset. These different candidate datasets were evaluated to select for publication the dataset version that best balances utility and privacy. For high-level information about this evaluation, see the Synthetic Data User Guide.

For more information about the creation of the synthetic version of this data, see the technical brief for this project, which discusses the technical decision making and modeling process in more detail.

Recommended Uses

This disaggregated synthetic data allows for many analyses that are not possible with aggregate data (summary statistics). Broadly, this synthetic version of this data could be analyzed to better understand the usage of human services by people in Allegheny County, including the interplay in the usage of multiple services and demographic information about clients.

Known Limitations/Biases

Some amount of deviation from the original data is inherent to the synthetic data generation process. Specific examples of limitations (including undercounts and overcounts for the usage of different services) are given in the Synthetic Data User Guide and the technical report describing this dataset's creation.

Feedback

Please reach out to this dataset's data steward (listed below) to let us know how you are using this data and if you found it to be helpful. Please also provide any feedback on how to make this dataset more applicable to your work, any suggestions of future synthetic datasets, or any additional information that would make this more useful. Also, please copy wprdc@pitt.edu on any such feedback (as the WPRDC always loves to hear about how people use the data that they publish and how the data could be improved).

Further Documentation and Resources

1) A high-level overview of synthetic data generation as a method for protecting privacy can be found in the Understanding Synthetic Data white paper.
2) The Synthetic Data User Guide provides high-level information to help users understand the motivation, evaluation process, and limitations of the synthetic version of Allegheny County DHS's Human Services data published here.
3) Generating a Fully Synthetic Human Services Dataset: A Technical Report on Synthesis and Evaluation Methodologies describes the full technical methodology used for generating the synthetic data, evaluating the various options, and selecting the final candidate for publication.
4) The WPRDC also hosts the Allegheny County Human Services Community Profiles dataset, which provides annual updates on human-services usage, aggregated by neighborhood/municipality. That data can be explored using the County's Human Services Community Profile web site.

Abstract

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys have been organised to track changes over time.

Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated - through Swahili briefs and posters - to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office.

Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Funding: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office - Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.

Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, 'interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.

Public Domain: Currently in the public domain are (i) all CWIQ reports - note that Shinyanga 2004 and Kagera 2003 reports are organised into one region-wide report (ii) Swahili and English briefs for 5 pilot dissemination districts funded by the Prime Minister's Office - and (iii) raw data for all CWIQs conducted between 2003 and 2007.

Geographic coverage

Five rural districts of Kagera: Ngara, Biharamulo, Muleba, Bukoba Rural and Karagwe.

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Data from the 2002 Population and Housing Census was used to select 15 households in 30 Enumeration areas in each rural district of the Kagera region. This brings the total number of households to 450 per district or 2,250 at rural regional level. Selection of households did not include refugee camps. Households were further stratified into rural and peri-urban areas and given statistical weights reflecting the number of households they represent.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Due to logistical constraints the completed questionnaires could not be scanned and automatically analysed through CWIQ software. This meant that the lay-out of the questionnaire had to be redesigned slightly to allow easy manual data entry. In order to avoid any problems with coding, missing variables, outliers etc. and to keep continuous thorough checks throughout the data analysis process, all tables and figures were manually produced and assessed for consistency with the data.

Abstract

This surveys aims to develop a mechanism to periodically assess priority indicators of children's well-being as a means to monitor changes over time, by: - developing sample design and listings that can be used repeatedly, ie each time, with a different set of sample households from the same primary sampling units and using the same listings: - employing a well-tested instrument easily implemented in the field that facilitates information-gathering on a concise yet comprehensive set of indicators: - developing and testing the logistics of field operations necessary to ensure timely data collection of high quality.

EMICS sets out to provide estimates of priority indicators at the national level disaggregated by urban and rural residence, and at governorate level - in particular Greater Cairo, Alexandria, Assiut, Sohag, Qena and Aswan.

The survey also aims to provide estimates of these priority indicators at the level of unplanned urban districts (random housing areas) as a separate stratum, in order to study disparities within the urban population and between the growing unplanned communities.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used for EMICS was a modified version of the standard questionnaire developed by UNICEF for multiple indicator cluster surveys. Except for two modules on salt iodixation and vitamin A deficiency, all sections of the standard questionnaire were incorporated into the survey. Adaptation of the original instrument took two forms: changing the layout and format of some sections and introducing new sections or aidditional questions to existing sections. Basically, EMICS features three modules.

Household module

This consists of three sections. - A roster of eligible members of the household: mothers or care-takers of children under 15 years of age and married women of reproductive age together with all infants and children under 15. Only usual residcnts were listed. visitors were not included. Information on age, sex and education attainment was obtained for every listed individual. School repeat and drop-out status plus data on child labour were collected for children aged 5- I4 years. - Contraceptive use among married women of reproductive age. - Source of drinking water and sanitary means of exereta disposal.

Children underfive module

This was administered to mothers and care-takers of children under five in the household. It has seven sections. - Source of ORS packets and awareness of mothers and care-takers of serious signs of diarrhoea and pneumonia. - Prevalence of diarrhoea in the preceding two weeks. feeding practices during the diarrhea episode, awareness of mother or care-taker of the importance of increasing fluids and continuing feeding during the diarrhoea episode. and source of medical consultation. - Prevalence of pneumonia. treatment provided and source of consultation. - Breast-feeding status and feeding practices. - Information on all vaccinations the child received. Data obtained either from an official certificate or directly from the mother or care-taker if no certificate was available. - Height and weight of each child under five. - Tetanus toxoid vaccination among mothers of children under five.

Disability module

This instrument collected information on different disability conditions among all children under 15 years of age in each sample household. Disabilities of interest were: - hearing problems: - loss of sight in one or both eyes: - speech impediment: - malfunctioning of upper or lower limbs: - mental retardation; - chronic health conditions such as diabetes. epilepsy. renal failure, cancer or heart conditions.

All survey instruments were pre-tested in three different locations: a middle-income urban neighbourhood. one village and one random housing area. The final instruments used are presented in Appendix D of the report.

Cleaning operations

Preparation of data entry programmes. data structure files and data management programmes started well before the field operations. These programmes and files were tested and corrected using completed questionnaires during the pre-test phase. Data entry and validation paralleled data collection. A clean data file was available for analysis three weeks after the conclusion of field operations. Computer software utilized was Foxpro for data entry and data management. Stata and Epi-info for cross-tabulations and analysis. Sampling variances within a selected set of indicators were computed using Clusters.

This is a dataset on 460,452 individuals employed by the Dutch East India Company (Verenigde Oost-Indische Compagnie, VOC) in the seventeenth and especially the eighteenth centuries, developed from 774,200 muster records in the ‘VOC-opvarenden’ collection. The original data has been enhanced through the disambiguation of individual records, the standardization of 44,152 unique place names, and the addition of wage details and rank structure.

This collection includes the original ‘VOC-opvarenden’ dataset (comprising three files), enriched files (totaling nine), integrated external data, and Jupyter notebooks documenting the transformation from original to enriched datasets. The accompanying data paper provides an in-depth overview of the original dataset, the enhancement process, and potential applications. Additionally, it features appendices serving as codebooks, offering concise descriptions of each variable present in the enriched data files.

Enabling research into career patterns, network structures, and migration trends, this resource is of significant value to the study of early modern history, social and economic history, and sociology.

The Kurdish News Summarization Dataset (KNSD) is a newly constructed and comprehensive dataset specifically curated for the task of news summarization in the Kurdish language. The dataset includes a collection of 130,000 news articles and their corresponding headlines sourced from popular Kurdish news websites such as Ktv, NRT, RojNews, K24, KNN, Kurdsat, and more. The KNSD has been meticulously compiled to encompass a diverse range of topics, covering various domains such as politics, economics, culture, sports, and regional affairs. This ensures that the dataset provides a comprehensive representation of the news landscape in the Kurdish language. Key Features Size and Variety: The dataset comprises a substantial collection of 130,000 news articles, offering a wide range of textual content for training and evaluating news summarization models in the Kurdish language. The articles are sourced from reputable and popular Kurdish news websites, ensuring credibility and authenticity. Article-Headline Pairs: Each news article in the KNSD is associated with its corresponding headline, allowing researchers and developers to explore the task of generating concise and informative summaries for news content specifically in Kurdish. Data Quality: Great attention has been given to ensuring the quality and reliability of the dataset. The articles and headlines have undergone careful curation and preprocessing to remove duplicates, ensure linguistic consistency, and filter out irrelevant or spam-like content. This guarantees that the dataset is of high quality and suitable for training robust and accurate news summarization models. Language and Cultural Context: The KNSD is specifically tailored for the Kurdish language, taking into account the unique linguistic characteristics and cultural context of the Kurdish-speaking population. This allows researchers to develop models that are attuned to the nuances and specificities of Kurdish news content. Applications: The KNSD can be utilized in various applications and research areas, including but not limited to: News Summarization: The dataset provides a valuable resource for developing and evaluating news summarization models specifically for the Kurdish language. Researchers can explore different techniques, such as extractive or abstractive summarization, to generate concise and coherent summaries of Kurdish news articles. Machine Learning and Natural Language Processing (NLP): The KNSD can be used to train and evaluate machine learning models, deep learning architectures, and NLP algorithms for tasks related to news summarization, text generation, and semantic understanding in the Kurdish language. The Kurdish News Summarization Dataset (KNSD) offers an extensive and diverse collection of news articles and headlines in the Kurdish language, providing researchers with a valuable resource for advancing the field of news summarization specifically for Kurdish-speaking audiences.

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys have been organised to track changes over time. Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated through Swahili briefs and posters to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office. Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork. Funding: Projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period. Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, 'interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens. Public Domain: Currently in the public domain are (i) all CWIQ reports note that Shinyanga 2004 and Kagera 2003 reports are organised into one region-wide report (ii) Swahili and English briefs for 5 pilot dissemination districts funded by the Prime Minister's Office and (iii) raw data for all CWIQs conducted between 2003 and 2007.

Table. Raw Consumer Data: This dataset comprises a sample of 100 out of 10,000 consumer records for Q4 2023. The data was collected through a combination of public API access to e-commerce platforms a pasted DT

весь этот текст умести в 3000 символов или меньше, на англ, не разбивай по таблицам, опиши в целом что где и для чего (это для репозитория описание)

Here's a concise 3000-character summary of the data description for the repository:

This study on multi-homing in Chinese e-commerce uses diverse datasets to analyze market dynamics, consumer behavior, and policy impacts. The data, collected from Q1 2019 to Q4 2023, includes:

Consumer records: A sample of 50 out of 10,000 anonymized consumer profiles for Q4 2023, gathered via public APIs and surveys. It covers platform usage, purchases, and satisfaction ratings, crucial for understanding multi-homing patterns and market effects.

Seller data: 50 out of 5,000 anonymized seller records for Q4 2023, obtained through surveys and marketplace analytics. It includes platform activity, sales, revenue, and listings, vital for analyzing seller strategies and market competition.

Price data: 50 out of 100,000 quarterly product records, collected via web scraping and APIs. It covers prices, categories, and sales volumes across platforms, essential for examining pricing strategies and product diversity.

Platform revenue: Quarterly data on GMV, commission, and advertising revenue for major platforms, compiled from public reports. This non-confidential information is key to assessing market size, growth trends, and platform performance.

Consumer surveys: 50 out of 10,000 quarterly responses on platform preferences and demographics. This anonymous data helps correlate factors influencing multi-homing behavior across consumer segments.

Policy simulations: 50 out of 1000 scenarios based on economic models and empirical data, crucial for predicting policy impacts on multi-homing and market outcomes.

Moderation analysis: Quarterly data on multi-homing rates, network effects, market concentration, prices, and product variety. Derived from platform statistics and research reports, it's vital for examining market factor interactions.

Platform-level PVAR data: Quarterly information on market share, prices, product variety, user satisfaction, and activity. Compiled from public reports and aggregated user data, it's essential for analyzing dynamic relationships between multi-homing, market structure, and consumer welfare.

All datasets are non-confidential and anonymized where necessary. They combine to provide a view of the Chinese e-commerce ecosystem, enabling in-depth analysis of multi-homing effects on market concentration and consumer welfare.

The data was collected through various methods including public APIs, web scraping, surveys, and analysis of public financial reports. It represents a mix of primary research conducted by the authors and aggregation of publicly available information.

IntroductionTerritory view based on families’ vulnerability strata allows identifying different health needs that can guide healthcare at primary care scope. Despite the availability of tools designed to measure family vulnerability, there is still a need for substantial validity evidence, which limits the use of these tools in a country showing multiple socioeconomic and cultural realities, such as Brazil. The primary objective of this study is to develop and gather evidence on the validity of the Family Vulnerability Scale for Brazil, commonly referred to as EVFAM-BR (in Portuguese).MethodsItems were generated through exploratory qualitative study carried out by 123 health care professionals. The data collected supported the creation of 92 initial items, which were then evaluated by a panel of multi-regional and multi-disciplinary experts (n = 73) to calculate the Content Validity Ratio (CVR). This evaluation process resulted in a refined version of the scale, consisting of 38 items. Next, the scale was applied to 1,255 individuals to test the internal-structure validity by using the Exploratory Factor Analysis (EFA). Dimensionality was evaluated using Robust Parallel Analysis, and the model underwent cross-validation to determine the final version of EVFAM-BR.ResultsThis final version consists of 14 items that are categorized into four dimensions, accounting for an explained variance of 79.02%. All indicators were within adequate and satisfactory limits, without any cross-loading or Heywood Case issues. Reliability indices also reached adequate levels (α = 0.71; ω = 0.70; glb = 0.83 and ORION ranging from 0.80 to 0.93, between domains). The instrument scores underwent a normalization process, revealing three distinct vulnerability strata: low (0 to 4), moderate (5 to 6), and high (7 to 14).ConclusionThe scale exhibited satisfactory validity evidence, demonstrating consistency, reliability, and robustness. It resulted in a concise instrument that effectively measures and distinguishes levels of family vulnerability within the primary care setting in Brazil.

Full dataset: Factor loading, communality, and Eta.

Training dataset: Factorial loads, communality, and Eta.

Abstract

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys have been organised to track changes over time.

Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated - through Swahili briefs and posters - to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office.

Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Funding: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office - Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.

Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, ‘interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.

Public Domain: Currently in the public domain are (i) all CWIQ reports - note that Shinyanga 2004 and Kagera 2003 reports are organised into one region-wide report (ii) Swahili and English briefs for 5 pilot dissemination districts funded by the Prime Minister's Office - and (iii) raw data for all CWIQs conducted between 2003 and 2007.

Geographic coverage

Rural districts in the Shinyanga region: Kishapu, Shinyanga Rural, Maswa, Meatu, Bukombe, Bariadi and Kahama

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Rural Shinyanga CWIQ was sampled to be representative at district level in all seven rural districts of Shinyanga region: Kahama, Bukombe, Bariadi, Meatu, Maswa, Shinyanga Rural, and Kishapu. Data from the 2002 Census was used to select 15 households in 30 Enumeration Areas in each rural district of the Shinyanga region. This brings the total number of households to 450 per district or 3,150 at rural regional level. Households were stratified into rural and peri-urban areas and given statistical weights reflecting the number of households they represent.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data are collected through a small 10-page questionnaire (downloadable below), called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare.Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Cleaning operations

Due to logistical constraints the completed questionnaires could not be scanned and automatically analysed through CWIQ software. This meant that the lay-out of the questionnaire had to be slightly redesigned to allow easy manual data entry. In order to avoid any problems with coding, missing variables, outliers etc. and to keep continuous thorough checks throughout the data analysis process, all tables and figures were manually produced and their consistency with the data assessed.CWIQ does not collect information on consumption and thus cannot directly calculate poverty rates. Therefore the 2000/01 Household Budget Survey (HBS) was used to determine predictors of poverty that are included in CWIQ, or could be easily added without delaying the field work. Through regression analysis weights for each poverty predictor were determined. By way of this weighted sum of poverty predictors each household can be predicted to either lie above or below the poverty line. This allows Rural Shinyanga CWIQ to analyse all data by (predicted) poverty status.