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A reconstruction of the time series of global technology from 5500 BC to the 2000s

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  • Antonio Paradiso

    (Department of Economics, University Of Venice Ca' Foscari)

Abstract

The aim of this study is to reconstruct the millennial historical series of global technology level from 5000 BC to the twenty-first century, using the data collected by Lilley [Men, machines and history, Cobbett Press, London, 1948] and updating them. The resulting series reveals a similar dynamic to the millennial series of global real GDP per capita. This finding is supported by structural changes in the growth dynamics of both series during the period of proto-industrialization and by the results of estimations from an unobserved components model, which highlight the effect of technology on global output. This study contributes to understanding the link between technology and economic development over the course of millennia.

Suggested Citation

  • Antonio Paradiso, 2023. "A reconstruction of the time series of global technology from 5500 BC to the 2000s," Working Papers 2023:12, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2023:12
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Technology; GDP per capita; economic growth; regression with breaks; unobserved components model;
    All these keywords.

    JEL classification:

    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • N70 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services - - - General, International, or Comparative
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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