Denver Basic Income Project Releases Year One Research Report

Denver Basic Income Project Releases Year One Research Report

Photo: Armando Geneyro
Note: The use of the term Basic Income in this article does not conform to BIEN’s definition.

Denver Basic Income Project (DBIP) has released the results its Year One quantitative and quantitative findings. What the research has discovered supports what DBIP always believed – that guaranteed income gives families and individuals financial tools, and a cushion to cover their most basic needs per their circumstances.
DBIP’s research shows:
You can review the Year One Research Report Executive Summary for an in-depth look at the research design, cost analysis due to reductions in public service utilization, and notable findings from both the quantitative and qualitative reports.
You can read the full reports on the research page of DBIP’s website.

Given that Denver annually spends over $40,000 on shelter and medical costs per person experiencing homelessness and is also dealing with the humanitarian and fiscal crisis of people arriving from the borders, cost-effective programs like this are extremely valuable. As the first and largest project of its kind studying the impact of guaranteed income on homelessness, the research and results of the Denver Basic Income Project have the potential to be replicated and scaled across the U.S.
The Year One report is a monumental milestone for the Denver Basic Income Project, and we would not be here without the support of the community and our generous funders, including the City and County of Denver, The Colorado Trust, the Denver Foundation, and the Wend Collective.
New Study on a European UBI

New Study on a European UBI

Financed by the European Parliament and written by three Catalonian economists, “The fundamental part of this research means to answer the following question: can UBI be financed by the EU? And more specifically, to answer the key question of how it can be financed, through three taxes: income tax, wealth tax and carbon tax.”

To download the free report in pdf format, click here.

UBI: Short-Term Results from a Long-Term Experiment in Kenya

UBI: Short-Term Results from a Long-Term Experiment in Kenya

Abstract: “What would be the consequences of a long-term commitment to provide everyone enough money to meet their basic needs? We examine this hotly debated issue in the context of a unique eld experiment in rural Kenya. Communities receiving UBI experienced substantial economic expansion|more enterprises, higher revenues, costs, and net revenues|and structural shifts, with the expansion concentrated in the non-agricultural sector. Labor supply did not change overall, but shifted out of wage employment and towards self-employment. We also compare the effects to those of shorter-term transfers delivered either as a stream of small payments or a large lump sum. The lump sums had similar, if not larger, economic impacts, while the short-term transfers had noticeably smaller effects, despite having delivered the same amount of capital to date. These results are consistent with a simple model of forward-looking lumpy investment, and more generally with a role for savings constraints, credit constraints, and some degree of (locally) increasing returns, among other factors.”

Read a summary of the report.

Read the full report.

An Early Look at the Labor Market ImpactPotential of Large Language Models

An Early Look at the Labor Market Impact
Potential of Large Language Models

Abstract of a recent paper:

“We investigate the potential implications of large language models (LLMs), such as Generative Pretrained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. … Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. … The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. … We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.”

The full paper can be found here.