Monte Carlo integration is a commonly used technique to compute intractable integrals and is typically thought to perform poorly for very high-dimensional integrals. To show that this is not always the case, we examine Monte Carlo integration using techniques from the high-dimensional statistics literature by allowing the dimension of the integral to increase. In doing so, we derive nonasymptotic bounds for the relative and absolute error of the approximation for some general classes of functions through concentration inequalities. We provide concrete examples in which the magnitude of the number of points sampled needed to guarantee a consistent estimate varies between polynomial to exponential, and show that in theory arbitrarily fast or slow rates are possible. This demonstrates that the behavior of Monte Carlo integration in high dimensions is not uniform. Through our methods we also obtain nonasymptotic confidence intervals which are valid regardless of the number of points sampled.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

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

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").

The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:

ID = group identifier (1-2000) x = numeric (Level 1) y = numeric (Level 1) w = binary (Level 2)

In all data sets, missing values are coded as "NA".

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This is a sample of data from a MAYA numerical relativity simulation, primarily for use with the tutorials for the mayawaves python package. This simulation is of binary black holes with a mass ratio of 5 and precessing spins.

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1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

Introduction Suicide is still one of the world's most important public health issues, with the World Health Organization (WHO) claiming that over 700,000 people die by suicide annually. Suicide is one of the main causes of death, with far-reaching consequences for people, families, and society. Understanding the global patterns and trends in suicide rates is critical for creating effective prevention methods and providing the required support to at-risk individuals. The purpose of this report is to visualize global data on suicides using the WHO dataset (who_suicide_statistics.csv). This dataset has statistics on the number of suicides in various countries, years, age categories, and sexes. By analyzing this data, it will guide us to learn about demographic and temporal patterns of suicide, show high-risk groups, and highlight regions facing significant challenges. The visualizations will employ various techniques such as graphs, charts, and maps to effectively convey the information and guide the viewer through the findings. Through these visualizations and insights, I suggested key points and recommendations needed to minimize suicide incidents in future. Description of the Dataset The dataset (who_suicide_statistics.csv) has extensive data on global suicide statistics collected by the World Health Organization. This dataset is an invaluable resource for analyzing the patterns and trends in suicide rates across countries, years, age groups, and genders. Below is a detailed description of the columns in the dataset and the kind of information each one provides. Columns in the Dataset • country: Description: The name of the country where the data was collected. Type: Categorical Example Values: 'United States', 'Japan', 'Germany' • year: Description: The year the data was recorded. Type: Numerical Example Values: 2000, 2005, 2010 - age: Description: The age group of the individuals whose suicide data is recorded. Type: Categorical Example Values: '15-24', '25-34', '35-44', '45-54', '55-64', '65-74', '75+' • sex: Description: The sex of the individuals whose suicide data is recorded. Type: Categorical Example Values: 'male', 'female' • suicide_no: Description: The number of suicide cases recorded for the specified country, year, age and sex. Type: Numerical Example Values: 15, 42, 108 • population: Description: The population of the specified age group and sex in the country for that year. Type: Numerical Example Values: 345633, 785042, 3356435 Additional Information • Suicide Rate Calculation: Using the suicide_no and population columns, we can calculate the suicide rate per 100,000 population, which normalizes the data and allows for fair comparisons across different countries and demographic groups. Formula: suicides_rate = (suicide_no / population) * 100000

We present a simple, spreadsheet-based method to determine the statistical significance of the difference between any two arbitrary curves. This modified Chi-squared method addresses two scenarios: A single measurement at each point with known standard deviation, or multiple measurements at each point averaged to produce a mean and standard error. The method includes an essential correction for the deviation from normality in measurements with small sample size, which are typical in biomedical sciences. Statistical significance is determined without regard to the functionality of the curves, or the signs of the differences. Numerical simulations are used to validate the procedure. Example experimental data are used to demonstrate its application. An Excel spreadsheet is provided for performing the calculations for either scenario.

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The perfection ratio of a number is a concept that is related to perfect numbers and how closely a given number approximates the ideal perfection ratio, which is 2.0.

Perfect Numbers:

A perfect number is a positive integer that is equal to the sum of its proper divisors, excluding the number itself. For example: • 6 is a perfect number because its divisors are 1, 2, and 3, and 1 + 2 + 3 = 6 . • 28 is another perfect number because its divisors are 1, 2, 4, 7, and 14, and 1 + 2 + 4 + 7 + 14 = 28 .

Perfection Ratio:

The perfection ratio of a number n is a measure of how close the sum of its divisors (excluding the number itself) is to the number. It is defined as:

\text{Perfection Ratio} = \frac{\text{Sum of Proper Divisors of } n}{n}

•  If the perfection ratio is 2.0, the number is considered perfect.
•  If the perfection ratio is greater than 2.0, the number is abundant (i.e., the sum of its proper divisors exceeds the number itself).
•  If the perfection ratio is less than 2.0, the number is deficient (i.e., the sum of its proper divisors is less than the number itself).

Examples:

1. Perfect Number Example:
•  For n = 6 :
•  Proper divisors: 1, 2, 3 
•  Sum of proper divisors: 1 + 2 + 3 = 6 
•  Perfection ratio: \frac{6}{6} = 1.0 
•  Since the perfection ratio is 2.0 for a perfect number, we see the idea of perfect numbers where the sum of divisors divides evenly.

1. Near-Perfect Numbers

• Definition: A near-perfect number is a number for which the sum of its proper divisors is close to the number itself but not exactly equal.
• Example: Consider the number 24. Its proper divisors are 1, 2, 3, 4, 6, 8, and 12. The sum is 36, which is larger than 24, making it almost perfect in the sense that the sum of its divisors is significant but not equal to the number.

2. Almost-Perfect Numbers

• Definition: An almost-perfect number is a number where the sum of its proper divisors equals the number minus one.
• Example: The number 16 is an almost-perfect number. Its proper divisors are 1, 2, 4, and 8, which sum to 15 (16 - 1).

3. Abundant Numbers

• Definition: A number is abundant if the sum of its proper divisors is greater than the number itself.
• Example: The number 12 is abundant because its proper divisors (1, 2, 3, 4, and 6) sum to 16, which is greater than 12.

4. Deficient Numbers

• Definition: A number is deficient if the sum of its proper divisors is less than the number itself.
• Example: The number 8 is deficient because its proper divisors (1, 2, and 4) sum to 7, which is less than 8.

5. Semiperfect Numbers

• Definition: A semiperfect number is a number that is equal to the sum of some (or all) of its proper divisors.
• Example: The number 12 is semiperfect because 12 = 6 + 4 + 2 (some of its proper divisors).

Relevance to the Heat Map

• Density Analysis: By analyzing the heat map further, we might observe concentrations at other specific perfection ratios besides 2. These could indicate near-perfect, almost-perfect, abundant, deficient, or semiperfect numbers.
• Patterns and Trends: Identifying where these numbers cluster can help us understand the distribution and frequency of numbers with these properties within your dataset.

Iris Petal and Sepal Dataset Description The Iris dataset is one of the most famous datasets in the field of machine learning and statistical classification. It was first introduced by British biologist and statistician Ronald Fisher in 1936 as an example of linear discriminant analysis. The dataset is widely used for educational purposes and model building in machine learning due to its simplicity and versatility.

Dataset Overview The dataset contains 150 observations of Iris flowers from three species:

Iris Setosa Iris Versicolor Iris Virginica Each observation includes four numerical features:

Sepal Length (cm) Sepal Width (cm) Petal Length (cm) Petal Width (cm) Additionally, the dataset provides a class label for the species of the Iris flower.

Feature Descriptions: Sepal Length: The length of the flower’s sepal in centimeters. Sepal Width: The width of the flower’s sepal in centimeters. Petal Length: The length of the flower’s petal in centimeters. Petal Width: The width of the flower’s petal in centimeters. Species: The class label that classifies the flower into one of three species (Setosa, Versicolor, Virginica). Data Summary: 150 instances (50 samples per species) 4 features (numeric data) 1 target variable (categorical – species of the flower) Applications: The dataset is often used for:

Classification tasks: Building models to classify the species of Iris flowers. Exploratory data analysis (EDA): Exploring relationships between features. Data visualization: Plotting petal and sepal dimensions to understand patterns. Predictive modeling: Training and testing machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), and decision trees. Why This Dataset? The Iris dataset is ideal for beginners and experts alike, as it provides an easy introduction to supervised learning. It is perfect for understanding basic classification algorithms and exploring key concepts such as:

Multiclass classification Feature correlation Data visualization techniques This description is tailored for the Kaggle community and provides a clear overview of the dataset’s content and potential use cases. You can customize it further if needed!

Dataset Description: emodata

The emodata dataset is designed to analyze and predict emotions based on numerical labels and pixel data. It is structured to include information about emotion labels, pixel values, and their usage in training and testing. Below is a detailed description of the dataset:

1. General Information - Purpose: Emotion analysis and prediction based on numerical scales and pixel data. - Total Samples: 49,400 - Emotion Labels: Represented as numerical intervals, each corresponding to a specific emotional intensity or category. - Pixel Data: Images are represented as pixel intensity values. - Data Split: - Training set: 82% of the data - Testing set: 18% of the data

2. Emotion Labels

• The labels are grouped into numerical intervals to categorize emotional intensity or types. Each interval corresponds to the count of samples:
  • 0.00 - 0.30: 6,221 samples
  • 0.90 - 1.20: 6,319 samples
  • 1.80 - 2.10: 6,420 samples
  • 3.00 - 3.30: 8,789 samples
  • 3.90 - 4.20: 7,498 samples
  • 4.80 - 5.10: 7,377 samples
  • 5.70 - 6.00: 6,763 samples
• Statistical Summary:
  • Mean: 3.1
  • Standard Deviation: 1.94
  • Quantiles:
  • Minimum: 0
  • 25%: 1
  • Median: 3
  • 75%: 5
  • Maximum: 6

3. Pixel Data

• Unique Values:
  • Total Unique Values: 34,000
• Most Common Pixel Intensities: Common pixel intensity values for various samples are listed, indicating grayscale or color representation.
• Pixel Usage:
  • Training: 82%
  • Testing: 18%

4. Data Quality

• Valid Samples: 100% (49.4k samples)
• Mismatched Samples: 0%
• Missing Samples: 0%

5. Usage

This dataset is particularly suited for: - Emotion Classification Tasks: Training machine learning models to classify emotions based on numerical and image data. - Deep Learning Tasks: Utilizing pixel intensity data for convolutional neural networks (CNNs) to predict emotional states. - Statistical Analysis: Exploring the distribution of emotional intensities and their relationship with image features.

Potential Applications

• Sentiment Analysis
• Emotion Detection in Images
• Human-Computer Interaction Systems
• AI-based Feedback Systems

This dataset provides a comprehensive structure for emotion analysis through a combination of numerical and image data, making it versatile for both machine learning and deep learning applications.

The US Consumer Phone file contains phone numbers, mobile and landline, tied to an individual in the Consumer Database. The fields available include the phone number, phone type, mobile carrier, and Do Not Call registry status.

All phone numbers can be processed and cleansed using telecom carrier data. The telecom data, including phone and texting activity, porting instances, carrier scoring, spam, and known fraud activity, comprise a proprietary Phone Quality Level (PQL), which is a data-science derived score to ensure the highest levels of deliverability at a fraction of the cost compared to competitors.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

The considerations in this report A Skateboard are part of the example collection which can be found in http://www3.math.tu-berlin.de/multiphysics/Examples/. The aim is to investigate different formulations, i.e., regularized formulations or also index reduced formulations, of the model equations in combination with different numerical solvers with respect to its applicability, efficiency, accuracy, and robustness.

The sheer volume of financial statements makes it difficult for humans to access and analyze a business's financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions about financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks in the general domain, the finance domain includes complex numerical reasoning and an understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. More details are provided here: Paper, Preview

The dataset is stored as JSON files, each entry has the following format: ``` "pre_text": the texts before the table; "post_text": the text after the table; "table": the table; "id": unique example id. composed by the original report name plus example index for this report.

"qa": { "question": the question; "program": the reasoning program; "gold_inds": the gold supporting facts; "exe_ans": the gold execution result; "program_re": the reasoning program in nested format; } ```

This dataset is the first of its kind intending to enable significant, new community research into complex application domains. It was hosted for a competition at CodaLabs on FinQA where if given a financial report containing both text and table, the goal is to answer a question requiring numerical reasoning. The code is publicly available @GitHub/FinQA

Between December 2020 and April 2022, samples were collected at six commercial buildings in Fairbanks, Alaska and between May 2019, to June 2021 at a large, compartmentalized warehouse at a coastal site in Virginia. Types of samples collected included: indoor air; outdoor air; SSSG; soil gas; radon; differential pressure; indoor and outdoor temperature; heating, ventilation, and air conditioning (HVAC) parameters; and other environmental factors.

VAPOR is the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers. VAPOR provides an interactive 3D visualization environment that can also produce animations and still frame images. VAPOR runs on most UNIX and Windows systems equipped with modern 3D graphics cards. VAPOR is a product of the National Center for Atmospheric Research's Computational and Information Systems Lab. Support for VAPOR is provided by the U.S. National Science Foundation and by the Korea Institute of Science and Technology Information This dataset contains sample files of model outputs from numerical simulations that VAPOR is capable of directly reading. They are not related to each other aside from being sample data for VAPOR.
To unpack the tar.gz files on Linux/OSX, issue the command tar -xzvf [myFile].tar.gz on the file you've downloaded. On Windows, a program like 7-zip can perform that operation. Once unpacked, the files can be directly imported into VAPOR, or converted to VDC. For more information see the "Getting Data Into VAPOR" Related Link below.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the 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 take 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

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, 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 "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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 have been 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.

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: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 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. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. 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 relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. 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 number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. 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 75.2%.

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