Date of Award

3-2021

Document Type

Open Access

Degree Name

Bachelor of Arts

Department

Managerial Economics

First Advisor

Stephen Schmidt

Keywords

Economics, Artificial Intelligence, Total Factor Productivity, OLS Regression

Abstract

Investment in and availability of artificial intelligence has become a central concern for most developed economies because of is expected positive impact on an economy. Unlike other forms of capital investment, investment in AI may lead to innovative products and processes that should increase productivity. However, AI’s overall effect on productivity remains largely unknown. Adopting AI replaces labor with capital, which will have a positive effect on labor productivity, but overall productivity may remain the same or even decrease. I look at the impact of AI implementation on Total Factor Productivity (TFP) in order to assess its effect on the economies of the developed world. The data on AI use is from the Stanford Human-Centered Artificial Intelligence database, which provides comprehensive measures of a country’s adoption of AI. Utilizing the methods set forth by Letta and Tol (2018), I perform a cross-country comparison of AI’s effect on TFP. I use OLS to estimate a model of national productivity which controls for country specific factors that would drive TFP and is focused on productivity growth due to the implementation of AI specifically. My findings suggest that more investment in AI implementation does not increase overall productivity. However, I do find that the number of startups focused on AI cause an increase in TFP. These findings contribute to the discussion of the productivity paradox and support the justification that an implementation lag may play a substantial role in the limited short-term productivity seen as a result of AI implementation.

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In Copyright - Educational Use Permitted.