A Spatiotemporal Analysis of Agricultural Prices: An Application to Colombian Data

Abstract

This study focusses on whether the geographical separation of markets constitutes a factor that helps explain the dynamics of agricultural prices. To do this, the authors employ a highly disaggregated dataset for Colombia that consists of weekly observations on wholesale prices for 18 agricultural products traded in markets scattered around the country. The sample period spans almost a decade. According to their results, which are based on generalized impulse response functions, distance (and thus transportation costs) is a factor that helps explain the speed at which prices adjust to shocks in other locations, thus confirming that price adjustments take longer for markets farther apart. [JEL Classifications: O18, Q13, R12]. C 2012 Wiley Periodicals, Inc.1 Indeed, Colombia is often characterized by a "center-periphery" dichotomy, where the central region (which includes the three main cities of the country, namely Bogotá, Medellín, and Cali) comprises the largest concentration of population, economic activity, and infrastructure (see Galvis, 2007).

Disease Outbreak, Health Scare, and Distance Decay: Evidence from HPAI Shocks in Chinese Meat Sector

“…There is also a connection to the literature on distance-decaying spillovers of public health scares to meat price risks. Our hypothesis that meat price risks are distance-decaying, is consistent with Cudjoe, Breisinger, and Diao [26] maintaining that distance between producer and consumer markets determines food price transmission, employing a threshold cointegration model in Ghana; consistent with Iregui and Otero [27] arguing that distance, namely transportation costs, negatively affects the speed of food price adjustment to shocks in other areas, based on generalized impulse response analysis with a highly disaggregated dataset for Colombia; consistent with Singh-Peterson et al [28] concluding that distance from food distribution center positively affects food price, using a spatial analysis of a healthy food basket survey undertaken across Queensland, Australia; consistent with Palermo et al [29] pointing out that distance from state capital city center is positively related to food price, adopting standard multiple regressions and multi-collinearity tests in Victoria; consistent with Yan, Terheggen, and Mithofer [30] asserting that distance between farmers' locations and nearest village market negatively affect village-level food price, utilizing a multiple regression analysis for small-scale farmers in Southwest China; consistent with Le Cotty, d'Hotel, and Ndiaye [31] claiming that market remoteness measured by distance, i.e., transport costs, has a positive effect on food price volatility, relying upon an autoregressive conditional heteroskedasticity model in Africa; and consistent with Iregui and Otero [32] suggesting that for traded food products, distance is negatively associated with the speed of food price adjustment to the long-run equilibrium, utilizing a pairwise approach to testing for spatial market integration in Colombia.…”

“…Second, relation with previous literature on distance-decaying spillovers of public health scares to meat price risks. Although prior literature explores the distance-decaying features of food prices, previous literature typically analyzes the effects of geographical distance (i.e., transport costs) on food price levels, food price volatility, and food price transmission, so as to test if food spatial market integration as well as the law of one price (LOP) holds [26][27][28][29][30][31][32]. However, we seek to characterize the effects of distance on spatial spillovers of public health scares to meat price risks, by constructing spillover measures weighted by distance following Halpern and Murakozy [38], and by performing our distance-decaying dynamic SAR regressions 147 times, and suggest that distance effects on spatial spillovers of public health scares to meat price risks are U-shaped.…”