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Record W3186471172

Essays on Universal Basic Income

2021· article· en· W3186471172 on OpenAlexaboutno aff
Nana Mukbaniani

Bibliographic record

VenueCUNY Academic Works (City University of New York) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsBasic incomeEconomicsPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Universal Basic Income (UBI) is a program in which individuals receive a regular sum of money, usually from the government. The transfer amount is thought to be unconditional of income and enough to cover all subsistence needs. Such a system is easy and cheap to administer because the government does not need to check the eligibility of each applicant. UBI programs are growing as more cities, states and countries (Stockton, California, Newark, New Jersey, Ontario, Canada, Kenya, Finland, Germany, Spain, China, etc) implement experiments of such programs. The idea of a UBI is gaining ground in the U.S.. One of the main responses of the U.S. to high unemployment caused by the COVID-19 pandemic and quarantine was a modified version of a temporary country-wide UBI program in 2020 (CARES Act). 30 mayors across the U.S. created a coalition - Mayors for a Guaranteed Income - to explore cash payment programs and address the racial wealth inequality. UBI is actively discussed to be a potential policy that can mitigate adverse impact of accelerated automation on wages and employment. Thus, it is important to understand what we have learned from UBI experiments, what macroeconomic models predict in the UBI environment, and what is the best approach to implement such programs. This dissertation consists of three chapters. In the first chapter I review the literature on a Universal Basic Income (UBI) policy. I explore the UBI experiments that have been conducted worldwide, their limitations, and lessons that we have learned from them. I also review the macroeconomic models that address the idea of unconditional transfers, their limitations and the required future developments to evaluate how UBI works in a more complex and realistic environment. In the second chapter, I use general equilibrium model of heterogeneous agents to evaluate the impact of the UBI system, on aggregate levels and distributions of wealth, consumption, labor, and welfare. I contrast this with a targeted transfers system where people need to meet certain eligibility criteria (usually, income) to qualify for transfers. I find that in the UBI system with $1,000 monthly payments, the level of aggregate capital falls by 16% and the inequality of wealth increases no matter how the UBI system is financed: through taxes or through foreign aid. Guaranteed payments induce people to save less because of less precautionary needs. As precautionary savings motive is stronger for the asset poor, people in the lowest wealth quintiles reduce their savings more, which increases the inequality of wealth. Even though the welfare of the least skilled and the asset poor increases significantly because of unconditional transfers, the tax-financed UBI system requires a consumption tax rate to be equal to 43% that slightly reduces the welfare of the wealthier. Even though consumption tax rate is unrealistically high, the effective consumption tax rate (consumption tax net of transfers) decreases on average and aggregate welfare increases by 15.7% as measured by consumption equivalent variation. A hybrid model with both targeted transfers and partial UBI (monthly payments of $500) with low, 5% capital income tax rate (to encourage savings) is more efficient as it provides significant, almost 8% gain in welfare with only 22% consumption tax rate and without compromising output or welfare of the asset rich. In the third chapter, I study the impact of a Universal Basic Income (UBI) policy on aggregate output and welfare when there is an automation of production technologies. When the productivity of robots increases, robots substitute for labor and thus, the share of labor in value added decreases. I use general equilibrium models with heterogeneous agents who face idiosyncratic earnings risk and Cobb-Douglas technology with Traditional Capital and Labor Services. Traditional capital does not include robots and can be employed in production only with labor services. Labor services is a CES nest of robot capital and Human capital that can substitute each other. I calibrate the economy to match the evolution of the labor share in the last three decades. If the productivity of robots doubles, I find that output increases in the new equilibrium and the welfare of wealth poor households decreases significantly resulting in more than 6% decrease in aggregate welfare (measured as consumption equivalent variation, CEV). In such a setting, the transition to a UBI system increases welfare significantly, by more than 15%, however, reduces output by 12% because it reduces the precautionary savings motive. The hybrid system in which every household receives 50% of subsistence requirement and the eligibility threshold for targeted transfers equals 50% of subsistence requirement works well as it is less detrimental to output while increasing aggregate welfare by 4% as CEV. Further increase in output in the UBI and Hybrid systems can be achieved by a lower capital income tax rate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.008
Scholarly communication0.0050.008
Open science0.0010.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0200.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.282
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
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