Endogenous growth theory II. The empirics of GDP growth

Questions What are the variables (institutional, cultural, demographic…) which determine GDP per capita and/or LT growth Do we expect poor countries close the gap with rich countries? What are the policies/institutions which allow such convergence to take place

An industry developed in the 1990s Take a cross-section of countries Regress their growth performance over a given period on a set of explanatory variables: Investment Education Financial development Corruption Age Political variables: coups, etc… Then write a World Bank report saying that variable X is “good for growth”

The findings: A recent paper by Sala-i-Martin et al. runs a horse-race between a large number of specifications involving more than 67 variables They rank variables by robustness using Bayesian techniques

A distribution of estimators across models:

The most robust variables:

The shortcomings Whether we are really talking about growth depends on the specification The economic interpretation of these regressions is not clear Many variables are not robust

The initial income problem: If initial income is not included in the regression, we estimate a permanent sustainable growth rate If it is and has a negative coefficient, we estimate the long-run output level It can only grow if One of the explanatory variables grow (but most can’t) A growth trend affects all countries

The interpretation problem Some variables affect growth because they proxy for the growth in the inputs of the production function: education, investment, etc… Others matter because they affect human behaviour and therefore how the economy accumulates these inputs Finally, whether initial income should enter depends on how the input contributions are specified

Example 1

Example 2

Example 3

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

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

Looking at convergence allows us to Test the relevance of endogenous growth models Assess the magnitude of the returns to accumulable factors

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

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:

The BSM Universal Law of Convergence: The speed of convergence is 2 % per year

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

How universal is the law?

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

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?

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?

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

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

The Augmented Solow model

The balanced-growth path

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:

Measuring sH

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?

Recomputing the speed of convergence

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

Old Solow does not work…

…but new does.

Does it add up?

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