Transmission of Global Commodity Prices to Domestic Producer Prices: A Comprehensive Analysis

Abstract

Utilizing a comprehensive dataset that includes a sample of 104 countries for corn, 54 countries for soybeans, 82 countries for wheat, and 77 countries for rice and covers the period from 1991 to 2013, we estimate a globally comprehensive but heterogeneous (country-specific) transmission elasticities between international prices and domestic producer prices. We mainly utilize the traditional two-step Engel-Grange cointegration model and the recently developed nonlinear autoregressive distributed lags (NARD) model to estimate the transmission elasticities. We find mixed evidence on the existence of long-run relationship between international and domestic price. For corn 66 out of 104, for soybeans 27 out of 54, for wheat 47 out of 82, and for rice 49 out of 77 countries, we fail to have a long-run relationship. For corn and soybeans, the long-run relationship is evident in top producing countries whereas the converse is evident for wheat and rice, particularly for rice. We also find that the pass-through of international to domestic prices is asymmetric in the majority cases-these asymmetries are negative, i.e., the domestic producer prices react less fully to an increase in international prices than to a decrease and are acute in the short-run than the long-run. We also estimate the cropspecific short-run global mean transmission elasticities, which vary from 0.358 (corn) to 0.524 (soybeans).

Welfare impact of asymmetric price transmission on rice consumers in Bangladesh

“…In the international context, the NARDL model has been applied for examining APT in several subjects. Delatte and Lopez-Villavicencio (2012) used the NARDL approach to examine the pass-through exchange rate in Japan, Germany, the UK, and the U.S.; Katrakilidis and Trachanas (2012) used it to explore aspects of housing price in Greece; Atil et al (2014) and Greenwood-Nimmo and Shin (2013) used it to detect APT in energy sector at the world perspective and the UK, respectively; Verheyen (2013) used it to explore the factors of mutual trade between EMU countries and the U.S.; Karantininis et al (2011) used it to examine APT in the Swedish food sector; Ibrahim (2015) used it to analyze the co-movement of Malaysia's oil and food prices; Vouzavalis (2017) used it to investigate the asymmetric association among natural gas, oil, and gasoline prices; Lacheheb and Sirag (2019) used it to examine the association between inflation rate and oil price in Algeria; Iqbal and Babcock (2016) used it to calculate the elasticity of price transfer between domestic producer level and international market for different countries; Nsabimana and Habimana (2017) used it to examine the impact of a variation in rainfall patterns on Rwanda's food prices; Ekananda and Suryanto (2018) used it to analyze the response of domestic soybean price with the variation of exchange rate; Pal and Mitra (2016) used it to examine asymmetric association between India's oil product and crude oil prices; and Davids et al (2017) used it to evaluate the degree of price transmission among three African countries.…”

The Impact of World Food Price on Domestic Inflation: Evidence from Sri Lanka

“…Using a Generalized least squared method, OLS regression model, Error correction specification model, the study concluded that global food prices are affecting domestic prices in the short run and medium term. Iqbal et al (2016) examined how global diversified international prices have an impact on domestic producer prices of 104 countries. Time series data for the period 1991-2013 were employed in this study.…”