size_categories:n<1K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us Artificial_Intelligence AI Business_Trends
---
tags:
- Artificial_Intelligence
- AI
- Business_Trends
pretty_name: AI_Employment Survey - BTOS
---
# Dataset Card for AI Use in Business from US Census Bureau

This dataset is from the US Census Bureau and contains the segment about AI use in businesses.

## Dataset Details
The BTOS questionnaire defines AI as computer systems and software able to perform tasks normally requiring human 
intelligence, such as decision-making, visual perception, speech recognition and language processing.

Examples of AI technologies and applications include machine learning, natural language processing, virtual agents, 
predictive analytics, machine vision, voice recognition, decision-making systems, data analytics, text analytics and image processing.

While it may seem like AI is everywhere, BTOS shows the opposite. Based on survey responses, only an estimated 3.9% of businesses 
used AI to produce goods or services between Oct. 23 and Nov. 5, 2023. However, this usage varied widely among economic sectors.

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
The BTOS asks a series of qualitative questions about business conditions in the last two weeks and expectations about
future business conditions. These qualitative questions result in response share estimates, the percent of businesses
which have selected each possible answer. For most questions, an index is produced to create one estimate per
question for easier comparison over time.

Source: U.S. Census Bureau, Business Trends and Outlook Survey (BTOS). 
The Census Bureau has reviewed this data product to ensure appropriate access, 
use, and disclosure avoidance protection of the confidential source data.
(Project No. P-7529868, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0162)

- **Curated by:** U.S. Census Bureau
- **Funded by:** U.S. Census Bureau, Business Trends Outlook Survey 
- **Shared by:** U.S. Census Bureau
- **Language(s) (NLP):** English
- **License:** Public Domain (U.S. Government Work)

### Dataset Sources
<!-- Provide the basic links for the dataset. -->
https://www.census.gov/hfp/btos/data_downloads

- **Repository:** U.S. Census Bureau - Business Trends Outlook Survey
Direct link to dataset Survey Supplement Table 
https://www.census.gov/hfp/btos/downloads/Supplement%20Table.xlsx

- **Paper:** Business Trends and Outlook Survey Begins
- https://www.census.gov/newsroom/press-releases/2022/business-trends-and-outlook-survey-begins.html

## Uses

<!-- Address questions around how the dataset is intended to be used. -->
The Business Trends and Outlook Survey (BTOS) provides insight into the state of the economy by providing 
timely data for key economic measures and business expectations about future conditions. By providing 
detailed geographic and subsector data, BTOS provides local, state, and federal officials near real-time 
data for policy and decision-making including after natural disasters or during economic crises. 
The BTOS also provides insight into recovery after these events.

### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
This dataset from the U.S. Census Bureau can be used for various purposes, including:

Demographic Analysis: Understanding population distribution, age, gender, and other demographic characteristics.

Economic Research: Analyzing economic trends, employment statistics, and business dynamics.

Policy Making: Informing government policies and programs based on population and economic data.

Academic Research: Supporting studies in sociology, economics, public health, and other fields.

Market Research: Assisting businesses in market analysis and strategic planning.


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
This dataset is not suitable for:

Personal Identification: The data is anonymized and should not be used to identify individuals.

Real-Time Decision Making: The dataset is not updated in real-time and may not reflect the most current information.

Misuse or Malicious Use: Any use that violates privacy, promotes discrimination, or is intended to harm individuals or groups.


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
An example can provide some insight into the interpretation of the index. Businesses are asked how revenue has
changed in the last two weeks and can answer either increased, decreased, or no change. The following formula is used
throughout the example.

Index = 100*(1*Increased + ½*NoChange + 0*Decreased)
(1) Assume that 10% of businesses reported an increase in revenue, 60% reported no change, and 30% reported a
decrease in revenue. Using the following formula gives an estimate of
Index = 100*(10% + ½*60% + 0*30%) = 40

(2) In the next period 20% of businesses report an increase in revenue, 50% report no change, and 30% report a
decrease in revenue. Using the following formula gives an estimate of
Index = 100*(20% + ½*50% + 0*30%) = 45

(3) In the next period 5% of businesses report an increase in revenue, 70% report no change, and 25% report a
decrease. Using the formula gives an estimate of 100*(5% + ½*70% + 0*25%) = 40.
(4) In the next period 5% of businesses report an increase in revenue, 85% report no change, and 10% report a
decrease. Using the formula gives an estimate of 100*(5% + ½*85% + 0*10%) = 47.

Moving from period (1) to (2), the revenue index improves, capturing the improvement of businesses from the “no
change” to “increased” positions. Note that the index improves even though there is no improvement for the 30% of
businesses that report decreases in revenue.

Moving from period (2) to (3), the revenue index declines. This reflects the larger weight given to increases (versus no
change and decreases) as well as the magnitude of the change.

Moving from period (3) to (4), the revenue index again improves this time reflecting a move away from “decreased” to
“no change” while there is no change in the share reporting increases.

These indexes measure the breadth of change for the variable of interest. They capture the extensive margin of change,
in that an index moving toward 100 reflects the extent to which the situation for that variable is increasing and
movement towards 0 reflects the extent to which the situation for that variable is decreasing while a value near 50
suggests a preponderance of businesses reporting no change such as in period 4. Depending on the question, increasing
could have a negative connotation, such as the question on prices.
The examples above are meant to illustrate that the index accounts for and simplifies the underlying dynamics of the
variable of interest when the dynamics may be complex and difficult to summarize otherwise.


## Dataset Creation
The dataset from the U.S. Census Bureau's Business Trends and Outlook Survey (BTOS) is created through a detailed and systematic 
process to ensure it provides accurate and timely data on business conditions. Here are the key steps involved:

Survey Design: The BTOS is designed to collect data on key economic measures and business expectations. The survey includes core questions and supplemental content as needed.
Sampling: The sample consists of approximately 1.2 million businesses, divided into six panels of about 200,000 businesses each. These businesses are asked to report once every 12 weeks for a year.
Data Collection: Data is collected every two weeks through online surveys. Businesses provide information on various aspects of their operations, including economic conditions and expectations for the future.
Data Cleaning: The collected data undergoes a cleaning process to remove any inconsistencies, errors, or incomplete responses, ensuring the dataset's accuracy and reliability.
Anonymization: To protect the privacy of respondents, all personally identifiable information is removed from the dataset.
Data Aggregation: The cleaned and anonymized data is aggregated to provide insights at various geographic levels, including national, state, and metropolitan areas.
Documentation: Detailed documentation is provided, including survey instruments, data dictionaries, and methodology reports, to help users understand the dataset's structure and variables.
Quality Assurance: The dataset undergoes rigorous quality assurance checks to ensure its accuracy and reliability. This includes validation against known benchmarks and peer reviews.
Publication: The final dataset is published on the U.S. Census Bureau's website, making it accessible to researchers, policymakers, and the public. Regular updates are provided to reflect the most current information.
This comprehensive creation process ensures that the BTOS dataset is a valuable resource for understanding business trends and supporting informed decision-making.

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->
The Business Trends and Outlook Survey (BTOS) provides insight into the state of the economy by providing timely data for key 
economic measures and business expectations about future conditions. This specific segment of the dataset is curated to discover how
AI is being used in business or how it is affecting business operations.

### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The BTOS sample consists of approximately 1.2 million businesses split into 6 panels (approximately 200,000 cases per panel). 
Businesses in each panel will be asked to report once every 12 weeks for a year. Data collection will occur every two weeks. 
The BTOS is comprised of a set of core questions and supplemental content, which is included as needed.
The initial target population for BTOS is all nonfarm, employer businesses with receipts of $1,000 or more that are 
in the United States, District of Columbia, and Puerto Rico. 
The following industries were designated as out of scope for the BTOS:

Agriculture production (NAICS in [“110000,” “111,” “112”])
Railroads (NAICS = "482")
U.S. Postal Service (NAICS = "491")
Monetary Authorities – Central Bank (NAICS = "521")
Funds, Trusts, and other financial vehicles (NAICS = "525")
Religious grant operations and religious organizations (NAICS = "813")
Private households (NAICS = "814")
Public administration (NAICS = "92")
Unclassified with legal form of organization as tax-exempt or unknown
Businesses classified without a 2-digit NAICS (NAICS = "00") were not included.




#### Data Collection and Processing
The BTOS has a biweekly data collection schedule.
The questionnaire for the BTOS contains approximately 25 core questions. The respondents are asked to report for the previous 
two weeks and looking forward six-months on numerous concepts.

Editing:
Due to the nature of the survey questions and rapid cycle of data collection and release, the BTOS response data are not subjected to editing.
Nonresponse:
Nonresponse is defined as the inability to obtain requested data from an eligible survey unit. Two types of nonresponse are often distinguished. 
Unit nonresponse is the inability to obtain any of the substantive measurements about a unit. In most cases of unit nonresponse, the U.S. Census Bureau 
was unable to obtain any information from the survey unit after several attempts to elicit a response. Item nonresponse occurs when a particular question is unanswered.
Nonresponse Adjustment:
The BTOS nonresponse adjustment consists of two weighting class adjustment factors: NAF1 and NAF2. NAF1 uses adjustment cells 
defined by state and employment size class (EMPSIZE). EMPSIZE is a categorical version of the number of employees in the establishment 
and has three categories: four or fewer employees ("A"), between five and 19 employees ("B"), and 20 or more employees ("C"). 
NAF2 uses adjustment cells defined by 2-digit NAICS (sector) and EMPSIZE.


#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

The source data producers for the Business Trends and Outlook Survey (BTOS) are the U.S. Census Bureau. The Census Bureau is responsible for designing, collecting, cleaning, and disseminating the data. They ensure the data is representative of U.S. businesses and provide detailed geographic and subsector information.
The BTOS sample consists of approximately 1.2 million businesses and is a voluntary survey.

### Annotations [optional]
There are no specific annotations of note.
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
The annotation process for this dataset involves several key steps to ensure the data is accurately labeled and useful for analysis:

Data Collection: The initial data is collected through surveys conducted by the U.S. Census Bureau.
Data Cleaning: The collected data is cleaned to remove any inconsistencies, errors, or incomplete responses.
Annotation Guidelines: Clear guidelines are established to ensure consistency in the annotation process. These guidelines include definitions, 
examples, and instructions for annotators.
Annotation: Trained annotators label the data according to the established guidelines. This may involve categorizing responses, 
tagging specific data points, or adding metadata.
Quality Control: The annotated data undergoes a quality control process to ensure accuracy and consistency. This may involve spot-checking annotations, 
double-checking by a second annotator, or automated validation checks.
Final Review: The final annotated dataset is reviewed and approved before being made available for use.


#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
U.S. Census Bureau. Individual names are not listed.

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
Disclosure is the release of data that reveal information or permit deduction of information about a particular 
survey unit through the release of either tables or microdata. Disclosure avoidance is the process used to protect 
each survey unit’s identity and data from disclosure. Using disclosure avoidance procedures, the Census Bureau modifies 
or removes the characteristics that put information at risk of disclosure. Although it may appear that a table shows 
information about a specific survey unit,the Census Bureau has taken steps to disguise or suppress a unit’s data that 
may be “at risk” of disclosure while making sure the results are still useful.


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
BTOS estimates may be subject to nonresponse bias if businesses that respond to the survey are systematically different from businesses that do not. 
BTOS estimates may also be subject to other types of nonsampling error, such as coverage error, measurement error, and processing error.

Sampling Error:
The sampling error of an estimate based on a sample survey is the difference between the estimate and the result that would be 
obtained from a complete census conducted under the same survey conditions. This error occurs because characteristics differ 
among sampling units in the population and only a subset of the population is measured in a sample survey. Sampling weights are 
used to ensure that the sample represents the entire in-scope population. The use of sampling weights also allows for the 
estimation of sampling variability of the survey estimates.

A common measure of sampling variability for percentage estimates is the standard error of the estimate. The standard error 
is the square root of the sampling variance, which is the squared difference, averaged over all possible samples of the same size and design, 
between the estimator and its average value. The standard errors for the BTOS estimates are calculated using a delete-a-group jackknife 
procedure, using 10 groups.

It is important to note that the sampling variance and standard error only measure sampling variability. They do not measure any systematic biases in the estimates.

The Census Bureau recommends that individuals using these estimates incorporate sampling error information into their analyses, 
as this could affect the conclusions drawn from the estimates.


### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
The Census Bureau recommends that individuals using these estimates incorporate sampling error information into their analyses, 
as this could affect the conclusions drawn from the estimates.



## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
US Census Bureau. (2022, July 15). Data collection begins for new business trends and outlook survey. Census.gov. 
https://www.census.gov/newsroom/press-releases/2022/business-trends-and-outlook-survey-begins.html


**APA:**
US Census Bureau. (2022, July 15). Data collection begins for new business trends and outlook survey. Census.gov. 
https://www.census.gov/newsroom/press-releases/2022/business-trends-and-outlook-survey-begins.html


## More Information
A copy of the questionnaire can be found at: 
https://www.census.gov/hfp/btos/downloads/BTOS%20Content%20V2%20Supplement%2011.29.2023_Watermarked.pdf

For the AI supplement used in this dataset, only questions #23-#37 are pertinent.


## Dataset Card Contact
Contact us at econ.btos@census.gov