This dataset contains household level transactions over two years from a group of 2,500 households who are frequent shoppers at a retailer. It contains all of each household’s purchases, not just those from a limited number of categories. For certain households, demographic information as well as direct marketing contact history are included.

Due to the number of tables and the overall complexity of The Complete Journey, it is suggested that this database be used in more advanced classroom settings. Further, The Complete Journey would be ideal for academic research as it should enable one to study the effects of direct marketing to customers.

The following are examples of questions that could be submitted to students or considered for academic research:  - How many customers are spending more over time? Less over time? Describe these customers.  - Of those customers who are spending more over time, which categories are growing at a faster rate?  - Of those customers who are spending less over time, with which categories are they becoming less engaged?  - Which demographic factors (e.g. household size, presence of children, income) appear to affect customer spend? -Engagement with certain categories?  - Is there evidence to suggest that direct marketing improves overall engagement?

U.S. Mortality and Health Indicators

Impact of Risk Factors on Population Health Outcomes

By Data Society [source]

About this dataset

This dataset provides county-level mortality and health indicators that are useful for measuring the impact of health policies in the United States. It includes data elements and values from over a dozen categories, including Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death, Relative Health Importance, Vulnerable Populations and Environmental Health, Preventive Services Use, Risk Factors and Access to Care. Additionally, this dataset offers Healthy People 2010 Targets and US Percentages or Rates for easy comparison across states. With comprehensive information for each county in each indicator domain available here at your fingertips could help you get insight into American population health from the local level like never before. Discover trends on disease outbreaks or immunizations that are unprecedentedly localized with insights from this dataset!

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How to use the dataset

This dataset contains various data elements related to the mortality and health of the US population at various levels such as county, state, etc. This dataset is an ideal source of information for researchers and policy makers who are interested in exploring patterns in the mortality and health of US citizens.

In order to use this dataset effectively, it is important to understand the different indicators included as well as how to interpret these indicators. In this guide we will look at each indicator domain separately so that users can easily identify which relevant data elements they need for their analysis.

Demographics: The Demographics indicator domain includes data elements related to demographic characteristics such as age composition, gender composition etc. These indicators can be used to explore trends across different parts of the country or identify disparities among populations.

Leading Causes of Death: The Leading Causes of Death indicator domain contains information on fatalities by cause over a set period of time -- either two years or five years depending on availability -- so that researchers can identify causes that pose major threats to public health overall or in more specific regions such as certain counties. It is important to note that these largely report figures based on death certificates which may not always tell an exact story due to reporting inaccuracies caused by both individual factors and registration biases across counties/states over time.

 **Summary Measures Of Health**: The Summary Measures Of Health Indicator Domain includes measures commonly used for gauging overall population health such as birth rates and death rates but also key quality-of-life considerations like prevalence rate physical activity rate . These can be used together with other data sources (such as income info) when analyzing population health outcomes from a broader perspective than individual diseases or conditions would allow for . 

 **Measures Of Birth And Death**: This category provides further insight into the important summary level figures mentioned earlier by providing observations about frequency , timing , type etc where available . Additionally , it offers valuable insights about trends related specifically (among others ) out - migration /in - migration mortality ratio changes/births outside hospitals marriage age / labor force participation trends etc – all essential ingredients when trying solve complex issues related improving public one's life expectancy positively  

 **Relative Health Importance & Vulnerable Populations And Environment Capacity :** This section covers two closely intertwined fields revealing how they interact – socioeconomic status disparities & environment quality – around boundaries & neighborhoods influencing risks factors (not only related medical matters ) aspects such disabilities insurance coverage alcohol use & smoking habits road fatalities veh

Research Ideas

• Using the Health Status Indicators as input features, machine learning models can be built to predict county-level mortality rate, which can then be used as an important indicator for health and medical resource allocation.
• The data can also be used to analyze the social determinants of health in different counties by combining with socioeconomic indicators such as poverty, population density and educational attainment levels.
• Additionally, the dataset could help assess th...

All the datasets uploaded contain all the variables required for the analysis carried out in the paper titled: “The impact of entrepreneurship training and credit on labour market outcomes of disadvantaged youth” psm_DP_Labd This dataset contains all the variables used to match the propensity scores. 1_Promise_single_974_DP_Labd This dataset has variables regarding ownership of businesses, savings and expenditure. 2_Promise_roster_974_DP_Labd Variables covering all the demographic characteristics are all gathered in this dataset. 3_Promise_q10_occup_974_DP_Labd Variables regarding employment are all in this dataset. 4_Promise_q12_loan_974_DP_Labd All the variables pertaining to loan are filed in this dataset.

This study is a secondary analysis of CRIME, FEAR, AND CONTROL IN NEIGHBORHOOD COMMERCIAL CENTERS: MINNEAPOLIS AND ST. PAUL, 1970-1982 (ICPSR 8167), which was designed to explore the relationship between small commercial centers and their surrounding neighborhoods. Some variables from the original study were recoded and new variables were created in order to examine the impact of community structure, crime, physical deterioration, and other signs of incivility on residents' and merchants' cognitive and emotional responses to disorder. This revised collection sought to measure separately the contextual and individual determinants of commitment to locale, informal social control, responses to crime, and fear of crime. Contextual determinants included housing, business, and neighborhood characteristics, as well as crime data on robbery, burglary, assault, rape, personal theft, and shoplifting and measures of pedestrian activity in the commercial centers. Individual variables were constructed from interviews with business leaders and surveys of residents to measure victimization, fear of crime, and attitudes toward businesses and neighborhoods. Part 1, Area Data, contains housing, neighborhood, and resident characteristics. Variables include the age and value of homes, types of businesses, amount of litter and graffiti, traffic patterns, demographics of residents such as race and marital status from the 1970 and 1980 Censuses, and crime data. Many of the variables are Z-scores. Part 2, Pedestrian Activity Data, describes pedestrians in the small commercial centers and their activities on the day of observation. Variables include primary activity, business establishment visited, and demographics such as age, sex, and race of the pedestrians. Part 3, Business Interview Data, includes employment, business, neighborhood, and attitudinal information. Variables include type of business, length of employment, number of employees, location, hours, operating costs, quality of neighborhood, transportation, crime, labor supply, views about police, experiences with victimization, fear of strangers, and security measures. Part 4, Resident Survey Data, includes measures of commitment to the neighborhood, fear of crime, attitudes toward local businesses, perceived neighborhood incivilities, and police contact. There are also demographic variables, such as sex, ethnicity, age, employment, education, and income.

Female Genital Mutilation is widespread in sub- Saharan Africa despite being illegal in many countries. It has been shown in previous research that female genital mutilation occurs due to long standing tradition which creates difficulty in the elimination of the practice. In any case, the effects of mutilation can have long lasting effects on future demographics through impacts on future fertility and marriage markets. This study looks to identify socio-economic risk factors that can increase the risk of circumcision of respondent first daughters in Senegal suing Demographic Health Survey data. Marriage Markets are affected through a number of channels due to the existence of female genital mutilation. For example, circumcision is associated with preparation for marriage reducing the age of marriages within populations. Furthermore, female circumcision can be used as a mechanism of control for women under the male dominated household, causing circumcision status to effect potential value on the marriage market assuming men supposedly make rational decisions when choosing a wife. Additionally, the practice can have negative impacts on female health, leading to the assumption that fertility will be negatively affected. Literature on the subject of female circumcision is growing, however few address the issue in relation to economic theories of circumcision. Likewise, much of the quantitative literature on female circumcision disregard the severity of circumcision. This paper attempts to link the severity of circumcision to fertility impacts through measurement of birth parity spacing and total births.The Demographic Health Survey provides a wealth of data needed for this type of research data. DHS allows us to use important measures of economic, health and demographics that can be used for government intervention on a range of issues. In regards to FGM, the Senegal: Standard DHS 2010-2011 provides extensive data on socio-demographic, reproduction, marriage, sexual activity and FGM.

Context and Objective:

This dataset is developed as part of a business analysis project aimed at exploring sales performance and customer demographics. It is inspired by real-world scenarios where companies strive to enhance their marketing strategies by understanding consumer behavior. The project focuses on the year 2023 and provides insights into how targeted marketing impacts sales while emphasizing demographic characteristics such as age and gender.

Source:

The dataset is synthetically generated, designed to simulate real-world sales scenarios for 20 products. It includes data points that mirror industry practices, ensuring a realistic and comprehensive foundation for analysis. The structure and data content are informed by common business intelligence practices and hypothetical yet plausible marketing scenarios.

Inspiration:

This dataset is inspired by the challenges businesses face in balancing targeted and broad marketing strategies. Companies frequently debate whether niche marketing for specific demographics or campaigns targeting a wider audience yields better outcomes. The dataset serves as a sandbox for exploring these questions, combining data analytics, visualization, and storytelling to drive actionable business insights.

Key Features:

Sales Data: Includes monthly sales records for 20 products, categorized by revenue, units sold, and discounts applied.

Demographic Information: Covers customer age, gender, and location to enable segmentation and trend analysis.

Applications:

Business Insights: Explore product popularity trends across different demographic groups. Revenue Analysis: Understand revenue patterns throughout 2023 and their correlation with customer age and gender.

Marketing Strategy Optimization: Evaluate the effectiveness of targeted vs. broad campaigns, particularly those targeting specific gender or age groups.

Visualization and Storytelling: Build dashboards and presentations to communicate insights effectively. This dataset is ideal for analysts and students seeking hands-on experience in SQL, exploratory data analysis, and visualization tools like Power BI. It bridges the gap between data science and practical business decision-making.

Abstract

As part of the efforts of the World Bank Group to understand the impact of COVID-19 on the private sector, the Enterprise Analysis unit is conducting follow-up surveys on recently completed Enterprise Surveys (ES) in several countries. These short surveys follow the baseline ES and are designed to provide quick information on the impact and adjustments that COVID-19 has brought about in the private sector.

The Zimbabwe Informal Businesses COVID-19 Impact Survey is different from the standard follow-up survey conducted by the unit in other countries, the major difference veing that this is not a follow-up survey.

Geographic coverage

National

Analysis unit

Enterprise

Universe

The universe of inference is all registered establishments with five or more employees that are engaged in one of the following activities defined using ISIC Rev. 3.1: manufacturing (groupd D), construction (group F), services sector (groups G and H), transport, storage, and communcations sector (group I) and information technology (division 72 of group K)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the survey was selected using stratified random sampling, broadly following similar methodology explained in the ES Sampling Note. However, unlike ES that uses three levels of stratification (size, location, and sector), this survey uses two levels of stratification, namely location/region of the informal busines and the gender of the main business owner.

Stratifies random sampling was preferred over simple random sampling for several reasons: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision b. To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is informal sector businesses operating in Zimbabwe. Informality is defined as any business that doesn't have registration from Zimbabwe Registrar of Companies. c. To make sure that the final total sample includes establishments from different regions and from businesses owned by male and femal. d. To exploit the benefits of stratifies sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e. lower standard errors, other things being equal.) e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Total sample target: 1020

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire contains the following modules: - Control information and introduction - General information - Sales and operations - Production - Labor force - Finance - Policies and prospects - Registration - Information on permanently closed establishments - Interview protocol

Response rate

98.4%

Global businesses expect several macrotrends to have significant impact in their operations in the next five years. The leading trends considered by the companies as most likely to influence their industry are the increased adoption of new and frontier technologies, the broadening of digital access and the broader application of Environmental, Social and Governance (ESG) standards, all expected to drive trends by approximately **% of the surveyed companies. Demographic and geopolitical factors, such as ageing populations, dividends, increased geopolitical divisions and COVID pandemic ongoing impacts are also considered by the businesses, being reported as expected trend drivers by over ** percent of the surveyed companies.

This dataset captures consumer perceptions and business outcomes related to the adoption of AI-powered chatbots in retail. It includes demographic variables (age, gender, country) alongside detailed measures of chatbot performance, such as response accuracy, personalization, speed, usability, and ability to handle complex queries.

It also tracks business-oriented outcomes such as cost reduction, efficiency gains, and anticipated business impact, along with customer-centered perceptions like satisfaction, brand loyalty, privacy concerns, skepticism, and likelihood of future adoption.

The dataset was designed to support research in areas such as:

Customer trust and AI adoption in retail

Trade-offs between personalization, efficiency, and privacy

Business impact of conversational AI systems

Cross-country and demographic differences in AI acceptance

Inspired by ongoing industry studies from Gartner, McKinsey, BCG, and consumer reports, this dataset provides a synthetic yet realistic foundation for academic research, data science projects, and experimentation in customer experience analytics.

The environmental impact assessment-related API provided by the National Institute of Environmental Research of the Ministry of Environment allows you to understand the population and housing characteristics of a specific business district through population and housing attribute information predicted and surveyed, and allows you to look up the development status (number of cases) of nearby areas, business code, full-time population after the business, user population after the business, planned housing population after the business, etc. It is a service that provides detailed information such as the housing supply rate (%) surveyed based on the business district subject to the environmental impact assessment, environmental impact assessment business code, year, population by gender and total population, total number of households (households), total number of houses (households), arrangement of data base year, and name of city, county, district, town, township, and dong.

The Los Angeles Metropolitan Area Surveys [LAMAS] 8, 1974 collection reflects data gathered in 1974 as part of the Los Angeles Metropolitan Area Surveys (LAMAS). The LAMAS, beginning in the spring of 1970, are a shared-time omnibus survey of Los Angeles County community members, usually repeated twice annually. The LAMAS were conducted ten times between 1970 and 1976 in an effort to develop a set of standard community profile measures appropriate for use in the planning and evaluation of public policy. The LAMAS instruments, indexes, and scales were used to track the development and course of social indicators (including social, psychological, health, and economic variables) and the impact of public policy on the community. Questions in this survey cover respondents' attitudes toward the following topics: commute times, means of transportation, and trust in government. In addition, participating researchers were given the option of submitting questions to be asked in addition to the core items. These additional topics include: mental health and psychological factors, access to medical care, alcoholism, the energy crisis, and attitudes towards black-owned businesses. Demographic variables in this dataset include age, sex, marital status, ethnicity, education, income, occupation, political party affiliation, and language.

This survey of consumer attitudes and expectations includes detailed information on old age insurance and private pension plans and their effect on the functioning of the economy, as well as information on the respondents' actual and expected family size. Questions were asked to help determine how pension plans influenced the attitudes and behavior of people long before their retirement. Open-ended questions were asked concerning evaluations and expectations about price changes, employment, recession, and the national business situation. Additional variables probe respondents' buying intentions for a house, automobiles, appliances, and other consumer durables, as well as respondents' appraisals of present market conditions for purchasing these items. Other variables probe respondents' opinions on a proposed government tax reduction and its effect on business conditions or employment, their own financial status relative to the previous year, and the Cold War between the former Soviet Union and the West. Data are also provided on respondents' house maintenance, total liquid assets, and savings and investments. Demographic variables provide information on age, sex, race, marital status, religion, education, occupation, and family income.

Demographic characteristics of sample youth group members by district.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The uploaded Excel file contains the raw data collected through a structured questionnaire aimed at investigating the factors driving Portuguese consumers in the e-grocery sector. The dataset includes detailed responses on consumer preferences, perceived barriers, and other relevant demographic and behavioral variables.In addition, the accompanying SPSS files provide the processed data along with the statistical analyses performed for the study titled “What Drives Portuguese Consumers in e-Grocery? Preferences, Barriers, and Business Implications.”

Descriptive characteristics of respondents comparing those who did and did not experience CMD.

The major objective of this study was to examine how physical characteristics of commercial centers and demographic characteristics of residential areas contribute to crime and how these characteristics affect reactions to crime in mixed commercial-residential settings. Information on physical characteristics includes type of business, store hours, arrangement of buildings, and defensive modifications in the area. Demographic variables cover racial composition, average household size and income, and percent change of occupancy. The crime data describe six types of crime: robbery, burglary, assault, rape, personal theft, and shoplifting.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

Characteristics of respondents by type of climate hazard reported.

Abstract

The survey was conducted in Sierra Leone between July and September 2017 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms.

Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey. Data from 150 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2017 Sierra Leone ES was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random sampling was preferred over simple random sampling for several reasons:

• To obtain unbiased estimates for different subdivisions of the population with some known level of precision.
• To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except subsector 72, IT, which was added to the population under study), and all public or utilities sectors.
• To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions.
• To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

• Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is - The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

  Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in Appendix C of the Survey Report.

  Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries- Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).

  For the Sierra Leone ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

  Regional stratification for the Sierra Leone ES was done across four regions: Bo, Western Urban, Kenema, and Bombali.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used during the survey namely; 1. Manufacturing Module Questionnaire 2. Services Module Questionnaire

The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing Questionnaire (includes the core module, plus manufacturing specific questions). Retail firms were interviewed using the Services Questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services Questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: - For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). - Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.75.9 This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 0.06.