Growing condition variations and grain prices in Niger and Nigeria

Abstract

In this paper, we describe how both the existing degree of price correspondence and tradability are important factors explaining why and the extent to which grain price relationships adjust due to growing condition variations. For a set of maize and millet markets in Niger and Nigeria with different agro-ecological characteristics and strengths of price relationships, we use normalised difference vegetation index (NDVI) data to identify years with weather-related production shocks. We then measure the degree to which price transmission between the commercial hub and reference market in each country in the anomalous weather years varies from that in normal years.

Price transmission with sparse market information: The case of United States chickpeas

“…These findings that supply‐related factors in importing regions influence the degree to which prices in trade partner countries move together is consistent with results from studies that included trade‐related variables in price transmission models, such as those by Barrett and Li (2002), Stephens et al (2012), and Arnade and Hoffman (2019). This paper also shows that that NDVI is a useful, timely, and accessible measure of growing condition variations that can be encompassed as a proxy for supply into price formation and transmission models, as was also demonstrated by Brown and Kshirsagar (2015), Baffes et al (2019), and Hatzenbuehler et al (2020).…”

“…The NDVI is a measure of vegetation “greenness” that is global in scope and able to measure light reflectance from nearly every parcel of land on earth. It has been used as a proxy for agricultural production by recent studies such as Brown and Kshirsagar (2015), Baffes et al (2019), and Hatzenbuehler et al (2020). NDVI data can be downloaded at 16‐day or monthly intervals from February 2000 to the current period.…”

“…For example, Brown and Kshirsagar (2015) used satellite data‐based production measures to estimate the degree to which food market price adjustments in developing countries could be ascribed to changes in local growing conditions or global commodity prices. On a more local scale, studies in Tanzania and Kenya (Baffes et al, 2019) and Niger and Nigeria (Hatzenbuehler et al, 2020), both of which used satellite data as a proxy for local production conditions, found that price transmission across regional markets can adjust due to changes in growing conditions, especially among markets with weak price relationships under normal growing conditions. This study represents an application of these emerging methods into a global market setting for chickpeas, a market with large interyear trade volume fluctuations.…”

Weather shocks, traders' expectations, and food prices

“…The extreme sensitivity of agricultural commodity prices to weather anomalies and fluctuations is well known. A substantive strand of empirical research has investigated this linkage and has shown that the causal chain can act either indirectly via the role played by crop yields and production volumes or through consumption choices (D'Agostino & Schlenker, 2016; Dercon, 2004; Hirvonen, 2016; Parker & Meretsky, 2004; Schlenker & Roberts, 2006; Schlenker & Roberts, 2009) or directly through spatial price transmission induced by factors such as market integration, networking, and trade arbitrage (Baffes et al, 2017; Brown & Kshirsagar, 2015; Gilbert et al, 2017; Haile et al, 2015; Hatzenbuehler et al, 2019; Mawejje, 2016; Minot, 2010; Stephens et al, 2012). The disruptive consequences of weather extremes on international prices (Algieri, 2014; Chatzopoulos et al, 2019; Headey & Fan, 2008; Piesse & Thirtle, 2009) and regional conflicts (Klomp & Bulte, 2013; Maystadt & Ecker, 2014) have also been studied extensively.…”

“…Such literature pointed out the presence of different price elasticities as a function of the specific crop-country combination, the sensitivity of the local food system to domestic production, and the degree of openness to international markets. Much less investigated, yet, is the crucial role of information flows in influencing spatial price adjustment across seasons, with the only exceptions of Stephens et al (2012) and Hatzenbuehler et al (2019). Furthermore, this body of empirical research on the weather-price relationship typically explores post-harvest price dynamics rather than pre-harvest ones.…”