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Data Ethics for Economics Researchers: Home

Data Ethics and Literacy

Ethics should be considered at the start of every research project. Librarian's (or library staff) can help!

As a researcher, ask yourself:

  • What do I want to know?
  • How will I collect that information?
  • How will I store it?
  • Do I need consent?
  • What are the long term implications of my data use?

*Anytime you are about to start collecting or giving your data, consider the ethical practices behind it. As a researcher, consider addressing ethical concerns in the data collection that you may have for your own personal data use. 

Anytime we think about data collection, ask:

  • How is this data being used?
  • How is this data being shared?
  • What are the short term and long term repercussions of this data collection?

Integrity in Economic Research

The AEA's founding purpose of  "the encouragement of economic research" requires intellectual and professional integrity. Integrity demands honesty, care, and transparency in conducting and presenting research; disinterested assessment of ideas; acknowledgement of limits of expertise; and disclosure of real and perceived conflicts of interest. source: AEA Code of Conduct

Data Misconduct

  • Plagiarism
    • Appropriation of the ideas, processes, results, or words of another without giving proper credit
  • Falsification
    • Manipulation of research materials, equipment, of processes
    • Changing or omitting results so research is not accurately represented
  • Fabrication
    • Making up results and recording or reporting them

See Princeton University's Policies on Research Misconduct and Research Ethics

Proprietary Data

You may have access to proprietary data through the Library (or your department). When you share your research, remember that the proprietary data itself can never be shared, published, or deposited in a data repository. The Library negotiates license terms and the researcher has the responsibility to comply with the legally binding institutional agreement. If you are ever unsure, ask your Librarian or the administrator of the license contract! 

Citing Data from Others

Properly citing data assists in the research process by giving data creators proper credit for their work, aids replication, provides permanent and reliable information about the data source, helps track the impact of the data, and facilitates resource discovery and access.

Provide citations for data sets when you have either conducted secondary analyses of publicly archived data or archived your own data being presented for the first time in the current work.

If you are citing existing analysis or statistics, cite the publication in which the data were published (e.g., a journal article, report, or webpage) rather than the data set itself.

Data can include

  • Algorithms
  • Scripts

Parts of the Research Process:

  • Raw Data
  • References
  • Results
  • Samples

How we work with data

  • Generating
  • Recording
  • Curating
  • Processing
  • Disseminating
  • Sharing
  • Using

Ethics of Data Gathering

It is never OK to do any of the following extraction methods on any U.S. Government database or Library subscription database:

  • Data Mining
  • Data Mirroring
  • Scraping
  • Data Robots
  • Other similar methods

*Alternatively, use an application programming interface (API)

Restricted Data

Restricted data, is data that cannot be released directly to the public research community because of the possible risks to study participants as well as the confidentiality promised to them. Access may be gained to these data requiring guarantee of strict legal and ethical use including a sound data security plan and other supporting documentation ensuring trustworthiness. 

Repositories such as ICPSR remove potentially identifying information for their public-use data and grant access to the data which contain highly sensitive personal information following an application process.

Personal Data vs. Sensitive Data

Much of the personal data that we each generate is beyond our immediate control as companies get a hold of them to use for market intelligence.

Examples of personal data include:

  • Name
  • Address
  • Email address
  • Information in public directories
  • Location data
  • IP address
  • Identifier of your phone

However, sensitive personal data is a special category that must be treated with extra security.

Examples of sensitive data include:

  • Racial or ethnic origin
  • Political opinion
  • Religious or philosophical beliefs
  • Trade union membership
  • Genetic data
  • Data related to a person's sex life or sexual orientation
  • Biometric data which uniquely identifies an individual

More comprehensive overview on levels of sensitive data, visit Princeton Research Data Service - Data Security 

Labor Economics Librarian

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Charissa Jefferson