💡
EA market testing (public)
  • Introduction/overview
    • Introduction & explanation
    • 👋Meet the team
    • 📕Content overview
    • Progress/goals (early 2023)
      • EAMT progress & results
      • Goals, trajectory, FAQs
  • 🤝Partners, contexts, trials
    • Introduction
    • Giving What We Can
      • Pledge page (options trial)
      • Giving guides - Facebook
      • Message Test (Feb 2022)
      • YouTube Remarketing
    • One For the World (OftW)
      • Pre-giving-tues. email A/B
        • Preregistration: OftW pre-GT
    • The Life You Can Save (TLYCS)
      • Advisor signup (Portland)
    • Fundraisers & impact info.
      • ICRC - quick overview
      • CRS/DV: overview
      • 📖Posts and writings
    • University/city groups
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    • Related/relevant projects/orgs
  • 🪧Marketing & testing: opportunities, tools, tips
    • Testing Contexts: Overview
    • Implementing ads, messages, designs
      • Doing and funding ads
      • Video ads/Best-practice guidelines
      • Facebook
      • Targeted ad on FB, with variations: setup
    • Collecting outcome data
      • Facebook ads interface
        • Pivot tables
      • Google analytics interface
      • Google A/B, optimize interface
      • Reconciling FB/GA reports
      • Survey/marketing platforms
    • Trial reporting template
  • 🎨Research Design, methodology
    • Methods: Overview, resources
    • "Qualitative" design issues
    • Real-world assignment & inference
      • Geographic segmentation/blocked randomization
      • Difference in difference/'Time-based methods'
      • Facebook split-testing issues
    • Simple quant design issues
    • Adaptive design/sampling, reinforcement learning
    • 'Observational' studies: issues
    • Analysis: Statistical approaches
  • 🧮Profiling and segmentation project
    • Introduction, scoping work
    • Existing work/data
      • Surveys/Predicting EA interest
      • Awareness: RP, etc.
      • Kagan and Fitz survey
      • Longtermism attitudes/profiling
      • Animal welfare attitudes: profiling/surveying
      • Other data
    • Fehr/SOEP analysis... followup
      • Followup with Thomas Ptashnik
    • Further approaches in progress
      • Profiling 'existing traffic'
  • 📋(In)effective Altruistic choices: Review of theory and evidence
    • Introduction...
    • The challenge: drivers of effective/ineffective giving
      • How little we know...
    • Models, theories, psych. norms
    • Tools and trials: overview
      • Tools/interventions: principles
      • Outcomes: Effective gift/consider impact)
        • (Effectiveness information and its presentation)
        • (Outcome: Pledge, give substantially (& effectively))
          • (Moral duty (of well-off))
        • Give if you win/ conditional pledge
      • Academic Paper Ideas
  • Appendix
    • How this 'gitbook' works
      • Other tech
    • Literature: animal advocacy messaging
    • Charity ratings, rankings, messages
    • "A large-scale online experiment" (participants-aware)
  • Innovationsinfundraising.org
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  • Your Place in the World: Relative Income and Global Inequality
  • Why might this be relevant to our profiling:

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  1. Profiling and segmentation project

Fehr/SOEP analysis... followup

PreviousOther dataNextFollowup with Thomas Ptashnik

Last updated 2 years ago

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Your Place in the World: Relative Income and Global Inequality

See discussion in:

NBER Working Paper (2019/2021), Dietmar Fehr, Johanna Mollerstrom, and Ricardo Perez-Truglia

  • Attitudes towards global redistribution

  • "De-biasing" intervention (how rich participants are relative to Germans, how rich Germany is globally)

Tied to

German Socio-Economic Panel (SOEP), a representative longitudinal study of German households. The SOEP contains an innovation sample (SOEP-IS) allowing researchers to implement tailor-made survey experiments.

a two-year, face-to-face survey experiment on a representative sample of Germans. We measure how individuals form perceptions of their ranks in the national and global income distributions, and how those perceptions relate to their national and global policy preferences. [Their main result]: We find that Germans systematically underestimate their true place in the world’s income distribution, but that correcting those misperceptions does not affect their support for policies related to global inequality.

Why might this be relevant to our profiling:

They ask about support for global redistribution, international aid institutions, globalization, immigration, and more, and have an incentivized giving choice. These are (arguably) measures of support for some EA behaviors/attitudes.

I suspect that this data could be tied to a variety of rich (personality? demographic?) measures in the SOEP. A predictive model for actual EA/Effective giving targeting in other related contexts? If so, let's focus on things we are likely to observe in those other contexts (or at least likely to have proxies for). If there are any 'leaks' (not sure I'm using the term correctly)... missing a single feature could ruin the predictive power of the whole model.

  • Causal interpretations (very challenging)?

    • Here 'nearly immutable characteristics' (like ethnicity, age, parental background, maybe some deep psych traits) might be a bit more convincing

  • *Descriptive* (whatever we mean by that)

    • Some things like "Previous donations" might be sort of colliders or 'confounds' (I'm a bit vague here) in interpreting other associations

See Followup with Thomas Ptashnik in next section

I tried to tackle some of this stuff

🧮
(incompletely) in analyzing the EA survey donations
LogoDoes awareness of global inequality increase personal giving or support for international redistributive policies? - EA Forum