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National Competitiveness and Economic Growth

The Changing Determinants of Economic Performance in the World Economy

Timo J. Hämäläinen

The current paradigm shift in the world economy is challenging the traditional competitiveness and growth theories with their few explanatory variables. This book offers a more holistic framework to synthesise the key findings of the various branches of competitiveness and growth research. The author illustrates this framework with a new long wave theory of socio-economic development. This theory emphasises the competitiveness and growth benefits of rapid structural adjustment in the rapidly changing techno-economic environment. Based on thorough analysis the author argues that both markets and governments have become less efficient due to the current transformation of the world economy. His empirical data from 22 OECD countries in the 1980s and 1990s illustrates that efficiency and growth-oriented governments have significantly contributed to their countries’ economic success.
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Chapter 22: Summary of statistical results

Timo J. Hämäläinen


The empirical research on economic growth and competitiveness tends to suffer from a lack of comparable data across many countries and years. One way to overcome this ‘small-n problem’ is to use pooled cross-sectional data which ranges both across space and time (Hicks 1994). Technically the pooled cross-sectional approach combines data on j countries and t years to produce a data set of j * t = n observations. Neither longitudinal single country studies nor cross-sectional one period studies could reach similar degrees of freedom, equally precise statistical estimates or as powerful statistical tests. In principle our benchmarking tables could offer such pooled cross-sectional data for 22 countries and 4 time periods, a total of 88 ‘nation-years’. However, due to missing observations for some indicators, we could not reach the maximum degrees of freedom in any of our statistical regressions. We used our database to study three different time periods: 1980s and 1990s together (max. n = 77), and the 1980s (max. n = 38) and 1990s (max. n = 39) separately. Splitting the database into two periods, the 1980s and 1990s, allowed us to study the changes in the relative importance of different competitiveness factors during the current paradigm shift. Moreover, by comparing the regression fits of the ‘two-decade’ and ‘single-decade’ models, we could test whether the paradigm shift had affected the structural relationships among the dependent and independent variables. Much weaker regression fits with the full data set would indicate that the regression procedure is averaging out two periods with very different underlying...

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