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OpenUBI

Optimizing UBI policy with machine learning. OpenUBI. NABIG 2019 Max Ghenis, Founder, OpenUBI. UBI has come a long way. LIFT+. But important questions remain. How will the deficit change?. How will poverty change?. How many people win or lose?. How will work incentives change?.

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OpenUBI

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  1. Optimizing UBI policy with machine learning OpenUBI NABIG 2019 Max Ghenis, Founder, OpenUBI

  2. UBI has come a long way LIFT+

  3. But important questions remain... How will the deficit change? How will poverty change? How many people win or lose? How will work incentives change? How will inequality change?

  4. ...including the most important question HOW WILL UBI AFFECT ME?

  5. Work's been done on some UBI-like policies Citizens' Climate Lobby (carbon dividend advocacy) https://citizensclimatelobby.org/household-impact-study/

  6. Work's been done on some UBI-like policies Citizens' Climate Lobby (carbon dividend advocacy) https://citizensclimatelobby.org/calculator/

  7. Demo ubiplans.org (Short for www.open-ubi.org/plans) This is what I do for UBI policy overall

  8. Lots of metrics can quantify UBI policies Inequality impact Gini, top 1% income share, etc. Household disruption Share better off, share with incomes decreasing more than 1%, etc. Deficit impact Poverty impact Official, extreme, SPM; child, senior, etc. Work incentives Average marginal tax rate Income distribution Median, bottom 10%, etc.

  9. ...but also to design them? What if we didn't just use metrics to assess plans...

  10. We just saw a simple version of this Inequality impact Gini, top 1% income share, etc. Household disruption Share better off, share with incomes decreasing more than 1%, etc. Deficit impact Poverty impact Official, extreme, SPM; child, senior, etc. Work incentives Average marginal tax rate Income distribution Median, bottom 10%, etc.

  11. Optimal taxation theory also addresses this Inequality impact Gini, top 1% income share, etc. Household disruption Share better off, share with incomes decreasing more than 1%, etc. Deficit impact Poverty impact Official, extreme, SPM; child, senior, etc. Work incentives Average marginal tax rate Income distribution Median, bottom 10%, etc.

  12. For example, Saez and Gruber (2000) "The Elasticity of Taxable Income: Evidence and Implications" Given: • Labor response to taxes • Income distribution • Social weights by income group Calculate: • Optimal tax rates • Optimal UBI

  13. Kasy (2019) is applying optimal taxation to UBI https://maxkasy.github.io/home/files/slides/UBI-experimental-design-Kasy.pdf

  14. Let's apply this to a new type of UBI plan • All tax deductions and credits • Payroll & AMT tax • All benefits except Medicare/aid Includes Social Security and the standard deduction 50% flat taxon all income Extra revenue funds UBI Modeled with Tax-Calculator 2.2.0 REPLACE WITH

  15. $5.2 trillion Raised by this plan

  16. $15,731 UBI per person (children, too) financed by this plan

  17. A flat UBI would have dramatic effects Poverty falls 99.6% Inequality falls 19% Median income rises 31% 34.5%come out behind 29.4%lose 5% or more disposable income

  18. What if we vary the amount by age group? $x per child Under 18 $y per adult 18 to 64 $z per senior 65 or older

  19. What if we vary the amount by age group? Optimizing for: • Share left worse off • Inequality

  20. Suppose we want to minimize the losers 34.5% come out behind today. To minimize would you increase kids, adults, or seniors? How much is too much?

  21. Suppose we want to minimize the losers There are three variables to tweak to optimize: • Child UBI • Adult UBI • Senior UBI But this is actually only two, since the budget forces one of the UBI amounts to go up when others go down, and vice versa Choose child/senior, infer adult

  22. Enter: machine learning Machine learning optimization techniques can explore the multivariate space. Steps: • Try a combination of{child UBI / senior UBI} (infer adult) • Calculate % losers • Try new combination (smartly) • Repeat 100 times

  23. How much better do we do? 34.5% losers to 31.0% With UBIs of: $10,710 per child$21,250 per adult$2,295 per senior (!)

  24. How does this optimal reform compare? Less poverty and inequality reduction, higher median income, fewer losers

  25. What if we optimize for inequality? From 19% inequality cut (Gini index)to 22% cut With UBIs of: $6,141 per child$17,481 per adult$25,310 per senior (!!) Likely due to differing household sizes

  26. Reducing inequality creates more losers

  27. Values sometimes compete

  28. These are political/philosophical questions How much disruption are we willing to give for a more equal, lower-poverty income distribution?

  29. Given those values, modeling can help This was a simple "search space" of only two parameters. What about expanding the problem to: • Changing tax rates? • Choosing programs to replace? • Keeping certain deductions? • Modeling dynamic effects like labor response?

  30. How we can move forward What's non- negotiable? What is negotiable? By what metrics do you choose? Philosophers, activists, and economists together

  31. Thank you! Questions?max@open-ubi.org

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