lecture notes #8: empirical studies of convergence
TRANSCRIPT
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Lecture notes #8: empirical studies of convergence
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Facts:
• The gap between rich and poor countries is large
• It is rather persistent
• Standard neo-classical models would predict rather rapid convergence
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Productivity growth seems to accelerate over time
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The gap between rich and poor countries is huge:
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Can we explain it?
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It does not add up
• Common estimates suggest α = 1/3
• Therefore, to have a 10-fold difference in GDP, we need a 20-fold difference in savings rate (2 % vs. 40 %)
• More leeway if technology differed across countries, but unlikely if technology is transferable
• So what do we do?
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If α were greater?
• Growth would take more time to fall to zero
• Convergence would be slower– The speed of convergence is the coefficient of
gdp growth on (local) initial log gdp
• Income differences between countries would be magnified
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Computing the speed of convergence
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An extreme case: α = 1,g=0
• The speed of convergence goes to zero
• The convergence path becomes a balanced growth path at a constant rate
• MRK no longer falling capital accumulation can sustain long-run growth
• The growth rate is now endogenous and depends on preferences
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Convergence in neo-classical models
• Neo-Classical models: each country converges to its own steady state
• All own steady states grow at the same rate
• But the level depend on policies, savings rates, etc
Similar countries converge to same GDP per capita
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Convergence in endogenous growth models
• A laggard never closes the gap
• Therefore, no convergence in income levels
• This because MPK is no higher for the laggard
• Furthermore, differences in policies affect the long-run growth rate
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Looking at convergence allows us to
• Test the relevance of endogenous growth models
• Assess the magnitude of the returns to accumulable factors
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Two approaches
• Barro and Sala-i-Martin: take a data set of similar economic units and look at convergence between them in pc GDP
• Mankiw-Romer-Weil: take a cross-country regression of growth rates on initial income controlling for own long-run steady state
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Barro and Sala-i-Martin
• They use a data-base of U.S. states over a long-run period
• They estimate the equivalent of our local speed of convergence regression:
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The BSM Universal Law of Convergence:
The speed of convergence is 2 % per year
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What do we expect?
• The Solow model predicts (δ+g)(1-α)
• A reasonable calibration is δ=0.06, g=0.02, α=0.3
• This gives v=5.6 % per year
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How universal is the law?
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Findings:
• The more similar the countries, the more it holds unconditionally
• The less similar the countries, the more likely we find divergence
• But the law is restored if controls are added, controlling for own steady state
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How to eradicate poverty?
• 1. Adopt the policies and institutions of advanced countries
• 2. Wait!
• How long? Suppose I am 10 times poorer than the US. How long does it take to be 2 times poorer?
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What do we get?
• With v=0.02, ρ0 = 0.1, ρ1 = 0.5, t = 60 years!
• With v=0.056, we instead get t = 21 years
• We want to understand why the speed of convergence is so low
• Can policy increase the speed of convergence?
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Gloom?
• In principle, the speed of convergence only depends on the deep technological parameters
• That it is low tells us that the technology is not what we thought it was
• But it does not tell us we can increase v
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Mankiw-Romer and Weil
• National accounts suggest that the elasticity of capital is 0.3
• Speed of convergence is more like
1-v/(g+δ) = 1-0.02/0.08 = 0.75
• To reconcile these two facts, they introduce another form of capital: Human capital
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The Augmented Solow model
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The balanced-growth path
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Explaining cross-country differenced in pcGDP:
• The preceding equations define “own” steady state
• They use it to see if it explains cross-country income differences:
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Measuring sH
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What have we learned?
• We have seen that with α = 0.3, it is difficult to explain X-country income differences
• But now what matters is α + β, which acts as α
• So with α + β large enough we can explain cross-country differences.
• A natural question is: can we also expect slow convergence?
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Recomputing the speed of convergence
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Empirical strategy
• Investment rates and schooling are kept to proxy for own steady state
• Initial output is added
• Coefficient in initial output related to SOV as in BSM
• No other control variable is added in strict interpretation of Solow model
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Old Solow does not work…
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…but new does.
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Does it add up?
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Summary
• The Solow model predicts too low income disparities and too quick convergence
• The AK model predicts zero convergence and widening disparities
• The Augmented Solow model does well to predict both the disparities and the speed of convergence