The ripple effect of animal disease outbreaks on food systems: The case of African Swine Fever on the Chinese pork market

Animal disease outbreaks are a significant and growing threat to global food systems (Rushton et al., 2018). Such shocks to a food system can cause disruption in the stability of food production along the food supply chain as well as impacting food import and export, food access, incomes and diets (Acosta et al., 2021, Savary et al., 2020). The African swine fever (ASF) outbreak in China was one such event. In China, the 2018 ASF outbreak resulted in the death from disease or culling of about 143 million pigs, a reduction of 40.5% and 39.3% in the stock of hogs and sows, respectively (USDA, 2019a, USDA, 2019b). As pork is the most important meat in the Chinese diet, and a main component of China’s consumer price index, the ASF could have led to significant welfare losses (Ma et al., 2021).

Previous research on animal health economics has emphasised the importance of accounting for the indirect economic effects of animal disease outbreaks. Although recent studies have advanced in this direction by assessing potential welfare losses due to incomplete or asymmetric price adjustments (Dai et al., 2015, Seok et al., 2018, Acosta et al., 2020), the importance of demand and supply shifters (i.e. exogenous shocks linked to the outbreak) and spill-over effects across related markets remains to be examined. Price transmission analysis using time series data has been a widely used method for examining the pass-through or pass-back effects of economic shocks in marketing chains (Gardner, 1975, Lloyd, 2017, Serra and Zilberman, 2013, Ubilava, 2018). We will next summarise the key relevant contributions.

Lloyd et al. (2001) is one of the first studies to examine the price dynamics related to an animal disease outbreak of bovine spongiform encephalopathy (BSE) in UK beef production. Specifically, the publicity surrounding the announcement of a link between BSE and Creutzfeldt–Jakob disease (CJD) presented the largest ‘food scare’ in the UK, causing the wholesaler–producer and retailer–wholesaler marketing margins to expand. Sanjuán and Dawson (2003) extended the analysis of the BSE crisis by including structural breaks in the co-integration (i.e. long run) relationship,¹ illustrating that BSE caused a structural break in the beef price relationship between farmers and retailers. Furthermore, Lloyd et al. (2006) revealed the differential impacts of the BSE crisis on producers and retailers due to differences in market power.

Using a vector autoregression (VAR) model, Seok et al. (2018) showed that the effect of the highly pathogenic avian influenza (HPAI) shock on Korea’s egg market prices was distributed unevenly, allowing processors to increase their margins. Employing a regime-dependent vector error-correction model, Acosta et al. (2020) showed that the HPAI outbreak in Mexico caused structural breaks in egg market price dynamics between producers and consumers, which was reflected in an increase in the absolute component of the market’s margin.

Since animal disease outbreaks constitute both a shock to supply (via costs) and demand (via preferences), it is essential to assess the extent to which demand- and/or supply-side shifters affect price adjustment. Research has employed media proxies² to estimate impulse responses that originate from demand-side shock (Livanis and Moss, 2005, Hassouneh et al., 2010, Hassouneh et al., 2012, Saghaian et al., 2008). To investigate price effects related to supply-side shocks, studies have used variables such as the number of animals culled or trade restrictions as proxies (Kim et al., 2019, Park et al., 2008). Using impulse response functions, Dai et al. (2015) estimated the impact of diseases affecting pigs on retail pork prices and price spreads.

As noted, a growing body of literature has used VAR models in stationary or error-correction form to measure the price effects of food shocks via impulse response functions. These studies have primarily focused on sector-specific prices, within or across countries; however, animal disease outbreaks can have broader spill-over impacts on other parts of food systems. Employing impulse response functions using local projections (Jordà, 2005, Jordà, 2009), this study aims to fill this gap by assessing the pass-back and pass-through³ effects of price adjustments for consumers and producers in addition to cross-impacts in associated markets, while accounting for the supply- and demand-side effects of the shock.

This study contributes to the existing literature in several ways. First, we construct a general framework that considers the demand- and supply-side effects of an animal disease outbreak on producers and consumers. Second, we estimate the spill-over effects into related markets to document the broader food system impacts of an animal disease outbreak. Third, we employ a recently developed technique called local projection (Jordà, 2009) to estimate the impulse response functions, which allows us to disentangle the effects of demand- and supply-side factors on price adjustment. Finally, we add to the research by assessing the effects of the ASF outbreak in China, an important and global animal disease shock that has not yet been examined from the perspective of price transmission analysis.