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VOLATILITY AND DEVELOPMENT

VOLATILITY AND DEVELOPMENT. Mikl´os Koren and Silvana Tenreyro The Quarterly Journal of Economics February 2007. Miklós Koren. Assistant Professor

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VOLATILITY AND DEVELOPMENT

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  1. VOLATILITY AND DEVELOPMENT Mikl´os Koren and Silvana Tenreyro The Quarterly Journal of Economics February 2007

  2. Miklós Koren • Assistant Professor • Department of EconomicsCentral European University1051 Budapest, Nádor utca 9., HungaryPhone +36 1 327 3000, +1 609 488 4644Fax +36 1 327 3232korenm at ceu dot hu • Personal Website (URL): http://miklos.koren.hu/research

  3. Silvana Tenreyro • Contact Details: Email:s.tenreyro@lse.ac.uk Tel: +44(0)20-7955-6018 Room number: S.579 On Leave: Michaelmas'08 Personal Website (URL):http://personal.lse.ac.uk/tenreyro/ Research Interests: Macroeconomics, Monetary Economics, International Economics, Growth

  4. The Main Contents of This Paper • Why is GDP growth so much more volatile in poor countries than in rich ones? (i) poor countries specialize in fewer and more volatile sectors, (ii) poor countries experience more frequent and more severe aggregate domestic shocks, (iii) poor countries’ macroeconomic fluctuations are more highly correlated with the shocks affecting the sectors they specialize in. • Decompose aggregate volatility into these three sources, and link volatility and development.

  5. A new approach to identifying and quantifying the sources of volatility Breakdown the aggregate volatility into: • the volatility of sectoral specific shocks • aggregate country specific shocks • the covariance between country-specific and sector-specific shocks So we can • point out the potential areas to which risk management efforts should be directed. • quantify and then understand more about the underlying mechanisms generating volatility.

  6. Methodology Innovations in the growth rate of GDP per worker in country j is expressed as the weighted sum of the innovations in the growth rates of value-added per worker in every sector. Is the employment share a good weight efficiency? Object: Var(qj) and its components

  7. The first disturbance is specific to a sector, but common to all countries. The second disturbance is specific to a country, but common to all sectors within a country. The third disturbance captures the residual unexplained by the other two. NOTE: This is not a regression equation, it is simply an accounting identity.

  8. Estimating the Model • Regression equation • ds, s = 1, ...S, are dummy variables that take the value 1 for sector s, and 0 otherwise • hj , j = 1, ...J, are dummy variables taking the value 1 for country j, and 0 otherwise.

  9. Then, we can compute thevarious measures of risk exposure： • and further:

  10. Findings • First, global and idiosyncratic sectoral risk decrease with the level of development.Countries’ productive structure moves from more volatile sectors to less ones. • Then, the volatility of country-specific declines as developed. • The level of specialization (concentration) follows a U-shaped curve with respect to development. • Fourth, the covariance between sectoral risk and country risk does not vary systematically with the level of development.

  11. LOWESS (locally weighted scatter smooth) a non-parametric methodology In this paper to characterize the relationship between each dimension of risk and the level of development. • Measurement Error

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