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Algorithm Impact on Fertility and R&D Sector

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  • Miyake, Yusuke

Abstract

This study investigates how Artificial Intelligence (AI) affects fertility decisions, economic growth, and overall social welfare. Despite substantial technological progress and increases in economic output (GDP), advanced economies, notably Japan, face severe demographic challenges due to dramatically declining fertility rates. This phenomenon raises important questions regarding the traditional measures of economic prosperity, prompting a re-evaluation of GDP as a reliable indicator of social welfare. To address these issues, this article develops a dynamic economic growth model incorporating heterogeneous human capital (skilled and unskilled labor) and introduces AI as a new, distinct form of capital investment. Unlike traditional physical capital, AI capital features negligible depreciation rates, significantly altering investment decisions, and long-term growth dynamics. On the demand side, households optimize their utility by allocating their limited time between labor supply, leisure, and child-rearing activities, directly influencing fertility rates and human capital accumulation. This paper argues that AI-driven algorithms fundamentally improve market efficiency by precisely matching heterogeneous consumer preferences and supplier characteristics, leading to optimal resource allocation. Unlike the traditional ”law of one price,” algorithm-driven markets generate multiple equilibrium prices, varying according to individual preferences and attributes, characterized herein as a shift toward a ”law of multiple prices.” The analysis suggests critical policy implications, emphasizing the need for refined economic and educational policies that address the implications of AI-driven market dynamics on fertility choices and income distribution. In particular, policy interventions must strategically promote educational reforms that diversify and enrich human capital, aligning it more closely with the demands of AI-intensive industries. This model provides a theoretical framework for understanding the intricate interplay between AI, demographic shifts, economic inequality, and long-term growth trajectories.

Suggested Citation

  • Miyake, Yusuke, 2025. "Algorithm Impact on Fertility and R&D Sector," MPRA Paper 124245, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:124245
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    References listed on IDEAS

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    1. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    2. Charles I. Jones & Paul M. Romer, 2010. "The New Kaldor Facts: Ideas, Institutions, Population, and Human Capital," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 224-245, January.
    3. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    4. Jones, Charles I, 1995. "R&D-Based Models of Economic Growth," Journal of Political Economy, University of Chicago Press, vol. 103(4), pages 759-784, August.
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    Keywords

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    JEL classification:

    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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