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Convergence Scenarios: an Overview

Convergence Scenarios: an Overview. Jonathan Temple (University of Bristol). “Classical” Convergence. Approach represented by papers such as Barro and Sala-i-Martin (1992) and Mankiw, Romer and Weil (1992) Based on closed economy growth models in Solow/Swan tradition Or Ramsey/Cass/Koopmans

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Convergence Scenarios: an Overview

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  1. Convergence Scenarios:an Overview Jonathan Temple (University of Bristol)

  2. “Classical” Convergence • Approach represented by papers such as Barro and Sala-i-Martin (1992) and Mankiw, Romer and Weil (1992) • Based on closed economy growth models in Solow/Swan tradition • Or Ramsey/Cass/Koopmans • Adds assumptions about world growth

  3. Some Basics… • Predicts countries converge in growth rates • But income levels differ in steady-state • This is because steady-state determinants vary across countries • Most familiar: investment, population growth • These determinants treated as exogenous

  4. A Key Strength… • A key strength of the approach is to recognise that, in principle… • …there is no reason why most countries cannot become rich • So future may not look like the past • Viewpoint differs from non-economists

  5. Empirical Findings • Wide variety of convergence estimates • Recent work suggests rates are slow • So countries spend a lot of time away from steady-state • Some evidence suggests “clubs” • But a lot of uncertainty (small N) • …and asking wrong question?

  6. This is the Wrong Approach…! • Bad approach for emissions scenarios • Estimated convergence rate is (implicitly) an average • Taken across different countries/regions • Regressions weight countries equally • Effectively small countries (sub-Saharan Africa) carry much more weight

  7. The Wrong Approach, Part II • Approach treats countries as closed • These economies evolve independently • For empirical purposes, treats steady-state determinants as exogenous • Rules out feedback from growth • How to relax these assumptions?

  8. Possible Feedback Mechanisms • Endogenous world growth • Geography and market access • Demography • Endogenous institutions • Financial development • Equipment prices • Energy prices • Feedback from climate

  9. Endogenous World Growth • Clear externalities in knowledge • Powerful effects of scale in endogenous growth models • World population of researchers expanding rapidly (India, China…?) • Will only be offset if research process is becoming more difficult

  10. Geography and Market Access • Recent work in trade emphasizes role of spatial location and market access • Suggests multiple equilibria at level of world regions • Fast growth in specific regions will change patterns of market access • Hence feedback from regional growth

  11. Demography • Crucial for emissions scenarios to build in endogenous demography • Offsetting effects: rising longevity versus fertility transition • Quantity/quality trade-off models predict human capital accumulation rise • Importance of policies in China, India

  12. Endogenous Institutions • Work such as AJR (AER 2001) suggests institutions crucial to steady-state GDP • Strong correlations between institution indices and levels of GDP • Endogenous democratization… • …might reduce forecast dispersion? • Less risk of civil war as income rises?

  13. Growth and Finance • Much work links growth to financial development • Development of banks endogenous to income level • Then stock markets… • Financial globalization relevant to convergence (e.g. FDI flows)

  14. Equipment Price Effects • Why does real investment differ? • Work such as Hsieh/Klenow suggests relative price of equipment crucial • Evidence suggests this price much higher in poor countries • Can be expected to fall as part of convergence process?

  15. Energy Price Effects • Price of energy unlikely to be independent of global convergence • May feedback into world growth • This effect will also tend to reduce dispersion of emissions scenarios, relative to dispersion of GDP scenarios

  16. Global Warming Feedbacks • Central point: endogenous climate • So different convergence scenarios may have feedbacks via climate • For example, agriculture, water stress • Changes in infrastructure, global distribution of population, etc.

  17. How to Model All These Effects? • Full structural model very complex… • So best approach may be reduced-form • Aggregate major regions • Consider different convergence behaviour in each… • But build in virtuous circle effects as a reduced-form

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