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Abstract
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 consumer and producer welfare losses due to asymmetric price adjustments, potential over-shifting effects along the supply chain and spill-overs to substitute markets have been under-examined. This study contributes to this field of research by assessing the direct and indirect effects of the African swine fever (ASF) outbreak on the pork market in China. We employ impulse response functions estimated by local projection to calculate the price adjustments for consumers and producers, as well as the cross-effect in other meat markets. The results show that the ASF outbreak led to increases in both farmgate and retail prices but the rise in retail prices exceeded the corresponding change in farmgate prices. Furthermore, beef and chicken prices also rose, demonstrating the spill-over impacts of the outbreak to other markets. Overall, the evidence illustrates that a disruption in one part of a food system can have significant ripple effects across other parts of the system.
Introduction
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.
Section snippets
Methodological framework
To estimate the dynamic effects of disease events on prices at different stages of the value chain, impulse response functions are estimated using the local projection technique pioneered by Jordà (2005). This approach involves sequentially estimating (or projecting) a set of regressions comprising the information set available at time
, periods into the future. Assuming that and the information set contains lags of , yields the following:
Data
We employ weekly prices at retail (pork) and producer (hog) levels as well as the retail prices of substitutes (beef and chicken) from 2017 to 2019. The average prices of pork and hogs in this period were 24.5 and 15 yuan/kg, respectively, while the average prices of beef and chicken were 65.8 and 14.7 yuan/kg, respectively. We also use the price of oil to proxy for other food chain costs, as energy and transportation costs are particularly significant and variable over the period. Other
Results and discussion
Our main results are reported in Figs. 3 and 4. Each figure presents the impulse response functions of shocks to demand (Fig. 3) and supply (Fig. 4) on retail pork prices, upstream hog prices and the prices of beef and chicken (all expressed in yuan/kg). Each impulse response function traces the effect of a one-unit shock for a period of 18 weeks following the shock. For the demand shock, we simulate the effect of a one-unit change in the Baidu search Index, and for supply shock we simulate a
Conclusions
Estimating the burden of disease on the food system can be a complex endeavour due to the market structure of supply chains; thus, it is crucial to assess the differential price impacts of animal disease shocks on producers and consumers to fully capture the broader welfare losses associated with these shocks. This study uses impulse response local projections to estimate the effects of demand- and supply-side shifters associated with ASF outbreak on price adjustments faced by different
Acknowledgements
This research was conducted by the Livestock Policy Lab (LPL). The LPL is a science‐policy platform hosted by the Livestock Information, Sector Analysis, and Policy Branch (NSAL) of the Animal Production and Health Division (NSA) at the FAO.
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