2011年-世界发展银行全球_The_Relative_Volatility_of_Commodity_Prices___A_Reappraisal_31页_1mb

Summary of "The Relative Volatility of Commodity Prices: A Reappraisal"

Core Content

This paper challenges the conventional wisdom that international prices of primary commodities are more volatile than those of manufactured goods. Using a detailed dataset of U.S. import prices classified at the 10-digit level of the Harmonized System (HS) from 2002 to 2011, the authors analyze the volatility of individual goods rather than aggregated price indices, which is a novel approach in the literature.

Main Viewpoints

• Conventional Wisdom: It is commonly believed that primary commodity prices are more volatile than manufactured goods, which has influenced policy discussions and financial strategies aimed at mitigating this volatility.
• New Evidence: The paper presents new findings suggesting that, on average, the prices of individual primary commodities are less volatile than those of individual manufactured goods.
• Policy Implications: The results have important implications for economic policy, particularly for developing countries with concentrated export baskets. These countries may need to focus on diversification and managing terms of trade volatility, not necessarily because commodities are inherently more volatile, but due to their export structure.

Key Information

• Data Source: The analysis is based on U.S. import data from the Foreign Trade Division of the U.S. Census Bureau, covering over 18,000 goods.
• Data Processing: Prices were calculated as the ratio of import values to quantities. The dataset was filtered to include only goods with price data for at least 36 consecutive months, resulting in 12,955 products.
• Volatility Measure: The authors use the standard deviation of de-trended price series, as well as alternative measures like interdeciles and interquartile ranges, to assess volatility.
• Product Classification: Commodity and manufactured goods are classified based on the IMF and UNCTAD definitions, as well as the NAICS system. The paper emphasizes that the classification of goods significantly affects the results.

Main Results

• Volatility Distribution: The analysis shows that the cumulative distribution functions (CDFs) of price volatility indicate that manufactured goods have higher volatility than commodities.
• Stochastic Dominance: Formal tests using the Kolmogorov-Smirnov test statistic confirm that the volatility of manufactured goods strictly stochastically dominates that of commodities.
• Robustness of Findings: The results hold even when considering a constant sample of goods, volatility of quantities, and product differentiation. The findings are also robust to measurement errors in unit values.

Robustness Tests

• Product Destruction: Even when excluding goods that exited the sample, the main result remains valid, indicating that the observed volatility is not driven by product destruction.
• Volatility of Quantities: The volatility of import quantities also shows that commodities are less volatile than manufactured goods.
• Product Differentiation: The paper classifies goods into homogeneous and differentiated categories based on Rauch (1999), finding that differentiated manufactured goods are the most volatile.
• Measurement Errors: The authors test the impact of measurement errors by excluding goods with low import values and find that the results remain consistent, suggesting that measurement errors are not the main driver of the findings.

Conclusion

The paper concludes that the conventional belief about commodity price volatility is not supported by the detailed analysis of individual goods. The higher volatility of manufactured goods is attributed to their differentiated nature and the frequent shifts in residual demand due to product innovation. This finding has important implications for development policy, suggesting that economic diversification and innovation capacity are critical for managing macroeconomic volatility, rather than focusing solely on commodity price fluctuations. The authors emphasize the need for further theoretical research to explain these findings and highlight the importance of accurate data classification in future studies.

References

• The paper is part of the World Bank's Policy Research Working Paper Series.
• It draws on previous literature such as Deaton and Laroque (1992), Caballero et al. (2008), and Rauch (1999).
• The authors thank several experts for their contributions and acknowledge that all remaining errors are theirs.

Figures and Tables

• Figure 1: Shows the volatility of aggregate price indices using IMF commodity indices.
• Figure 2: Displays the volatility of aggregate price indices using UNCTAD commodity indices.
• Figure 3: Presents the cumulative distribution functions of price volatility for goods with uninterrupted price series.
• Figure 4: Shows the CDFs of price volatility for selected manufactured products.
• Figure 5: Displays the CDFs of price volatility for goods available for the whole period.
• Figure 6: Illustrates the cumulative distribution function of the volatility of import quantities.

This paper provides a critical reappraisal of the volatility of commodity prices and highlights the importance of using disaggregated data for accurate policy analysis.