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COVID-19 on The impact of COVID-19 on food food prices in prices in China: evidence of four China major food products from Beijing, Shandong and Hubei Provinces 445
Xiaohua Yu Received 14 April 2020 Revised 9 May 2020 Jilin Agricultural University, Changchun, China and Accepted 3 June 2020 University of Goettingen, Goettingen, Germany Chang Liu Jilin Agricultural University, Changchun, China, and Hanjie Wang and Jan-Henning Feil Department of Agricultural Economics and Rural Development, University of Goettingen, Goettingen, Germany
Abstract Purpose – The purpose of this paper is to empirically study the impact of the coronavirus disease 2019 (COVID-19) on food prices in China and provides policy implications for crisis management for other countries who are still under the crisis of COVID-19 and for the future in China and beyond as well. Design/methodology/approach – This paper first designed a theoretical model of market equilibrium, which shows that the impact of COVID-19 on food prices is linked to the impact difference on demand and supply in response to the COVID-19 crisis. Then we collected the representative prices data for four major food products (rice, wheat flour, pork and Chinese cabbages) from three provinces (Shandong as a producing base, Beijing as a consumption base and Hubei as the epicenter), and set up an iGARCH model. Findings – (1) No significant impact on rice and wheat flour prices, (2) significantly positive impact on cabbages prices and (3) various impact on pork prices. Note that the outbreak and the severity of COVID-19 have different impacts. The outbreak itself may have a relatively large impact on pork and cabbage prices, which may result from social panic, while the magnitude of the impact of severity is relatively small, and some are negative, perhaps due to more reduced demand during the quarantine. Practical implications – China always puts food security in its prior position of policy agenda and has been preparing for the worst scenario of the food security crisis. In the anti-COVID-19 campaign, China’s local governments developed many measures to ensure food provision for each consumer. Hence, the impact of COVID-19 on food prices is minor. However, the outbreak of COVID-19 crisis could cause social panic in some scenarios where consumers may hoard food. Eventually, it may form a vicious cycle to push up food prices. This will be a challenging policy issue in crisis management for almost all governments. Originality/value – This paper provides empirical evidence on the impact of COVID-19 on food prices in China. China has basically contained the COVID-19 in the whole country, and no major food crisis occurred during this process. The results will provide information on crisis management for other countries that are still under the COVID-19 crisis, and for future China and beyond. Keywords COVID-19, Food prices, Wuhan, China, Crisis management, iGARCH Paper type Research paper
1. Introduction The coronavirus disease 2019 (COVID-19), which is a severe respiratory infectious disease caused by the novel coronavirus and firstly identified in Wuhan city of China, has been a global pandemic. Due to its strong infectiousness, it has been transmitted to almost all the China Agricultural Economic countries in the world (Verity et al., 2020). This has been marked as one of the most severe Review Vol. 12 No. 3, 2020 global health catastrophes in human history. Though the origin is unknown, it was identified pp. 445-458 firstly in Wuhan, China. After its outbreak, the governments in China took strong quarantine © Emerald Publishing Limited 1756-137X policies, locked down the whole Hubei Province, and restricted human mobilities in whole DOI 10.1108/CAER-04-2020-0054 CAER China after January 23, 2020. After 76 days, until April 8, 2020, Wuhan City finally removed 12,3 the lockdown measures, and China, for the most part, contained the infectious disease across the whole country. Economic and life loss are huge. The total confirmed infections and deaths are more than 83,600 and more than 3300, respectively, as reported on April 13, 2020. Food is a basic human need, and food security is often challenged during such a humanitarian crisis (Hasiner and Yu, 2020; Kuwonu, 2014). The current literature finds that the outbreak of severe infectious diseases is often accompanied by a food crisis due to 446 abandonment of farming activities, breakdown of food supply chain, and income reduction and unemployment (Kuwonu, 2014; Tse et al., 2006; FAO, 2020). FAO (2020) particularly pointed out that the COVID-19 could threaten food security for most vulnerable communities or groups, and thus could cause a crisis within the crisis. The impact of COVID-19 on food security has many channels. Due to the travel restrictions, which have been implemented in many countries throughout this crisis, both consumption and supply of food could be affected. On the one hand, as consumers, particularly in cities, have to stay at home, restaurants consumption is restricted, and shopping frequencies are minimized, in order to avoid infectious contraction. On the other hand, supply could be suppressed due to the breakdown or restrictions of the food supply chain, and even abandonment of production. A phenomenon of food crisis could be mirrored by skyrocketing food prices, which might undermine the welfare of the poor (Yu and Shimokawa, 2016; Yu, 2018). Social panic after the outbreak of infectious diseases could make the food crisis even worse. A typical behavior would be that consumers start to hoard food products, which will limit food availability in the market, particularly in countries without enough domestic food supply. This could further cause more panics, and the food market enters a vicious cycle with again increasing food prices. If speculative capital joins in the game, the market scenario could be further worsened and beyond the control of the governments. Ensuring food security is always put in a prior position in many countries’ policy agenda. China is no exception. Given the largest population in the world, any food crisis could trigger humanitarian crises, and the impact could be beyond China, economically, socially and even psychologically. The governments in China have always been preparing for the worst scenarios of a food crisis, particularly after the Great Famine at the beginning of the 1960s (Yu, 2018). After the outbreak of COVID-19, the government took a strict quarantine policy, but the food supply in all China has been guaranteed by showing green lights to the food supply chain and consumer purchase. Even under the strictest quarantine policy, necessary food was often collectively purchased and delivered to each household. Some regions allow consumers to purchase food in a minimized frequency with permitting cards. After the removal of lockdown for Wuhan City on Apr. 8, it seems that COVID-19 has been basically contained in China, and the Chinese are gradually returning to normal life. During this anti-COVID-19 campaign, food markets seem relatively stable, and no major food crisis occurred, though there are some minor price volatilities. Summarizing the experiences and identifying the market impacts in this period could help provide information for crisis management for other countries who are still under the crisis of COVID-19 and for the future in China and beyond as well. The food prices could mirror the situation of a food crisis. Thus, the existing studies have focused increasing attention on the causes of food price volatility, including crop production (Fafchamps, 1992), social unrest (Bellemare, 2015), energy prices (Gardebroek and Hernandez, 2013; Taghizadeh-Hesary et al., 2019), trade policy coordination (Gouel, 2016), etc. However, to the best of our knowledge, far too little attention has been paid to the impact of epidemics on food price volatility. In light of this, this paper particularly sheds light on the impact of COVID-19 on food prices in China. The travel restriction implemented to fight the infection could reduce both demand and supply (Sun et al., 2017). We first propose a theoretical framework in Section 2 to show that the final aggregate impact is linked to the COVID-19 on difference between supply and demand in response to the COVID-19 shock. Then, Section 3 food prices in selects food prices for four typical food products (rice, wheat flour, pork and Chinese cabbage) from three provinces (Shandong province, Beijing City and Hubei Province). The three China provinces (city) represent a major producing base, a major consumption region and the epicenter for the infectious disease. Section 4 builds up a Fractionally Integrated GARCH model to empirically test the impacts. Section 5 discusses the empirical results. Section 6 concludes and offers policy implications. 447
2. Theoretical model In a well functioning market, an equilibrium holds in which supply St equals demand Dt.We assume both demand and supply of food are determined by market prices Pt and information about COVID-19 Ct. Ct could be a discrete variable that models the outbreak of COVID-19, or the number of infections which measures the severity of diseases. Following the model proposed by Sun et al. (2017), we first have
DtðPt; CtÞ¼StðPt; CtÞ (1)
Taking the total derivative in both sides of Eqn (1), yields
vDt vDt vSt vSt dPt þ dCt ¼ dPt þ dCt: (2) vPt vCt vPt vCt Rearranging Eqn (2), we have v v St Dt dP v v t ¼ Ct Ct vD vS (3) dCt t t vPt vPt
Given the condition of market equilibrium Dt ¼ St, Eqn (3) becomes v v St Ct Dt Ct dP C v v t t ¼ Ct St Ct Dt ; vD P vS P dCt Pt t t t t vPt Dt vPt St which is exactly rewritten as, η ; η ; η ¼ S C D C (4) P; C η η D; P S; P η Where P; C denotes the price elasticity with respect to changes in the COVID-19 η η information Ct, which is the main research purpose of this paper. S; C and D; C are supply and demand elasticities, respectively, in response to COVID-19 information Ct; and η η similarly, S; P and D; P are supply and demand elasticities, respectively, with respect to food price Pt. η < ; η > η Given D; P 0 and S; P 0for normal food products, Eqn (4) shows that the sign of P;C η − η is the same as the sign of D; C S; C. That is, the changes of food prices are determined by the η η shock difference between supply elasticity S; C and demand elasticity D; C. After the outbreak of COVID-19, governments usually take strict measures, up to locking down cities and control human mobilities to contain or slow down the spread of the disease (Kraremer et al., 2020). These measures, of course, could simultaneously reduce demand and supply. If the demand drops more than the supply, the market prices will fall, and vice versa. In other words, the impact of COVID on food prices is uncertain. In the rest section, we are going to study it empirically for China, with the use of the food price data from Beijing, Shandong and Hubei Provinces. CAER 3. Data 12,3 3.1 Food price data In order to empirically study the impact of COVID-19 on food prices in China, we collected daily prices for four main food products (rice, wheat flour, pork and Chinese cabbage) in three representative provinces (Shandong Province, Beijing City and Hubei Province) for the period from January 1, 2019 to April 8, 2020. The latter is the most recent date we have. We include the data for the whole year of 2019 for the purpose of comparison and can remove the possible 448 seasonality. Rice and wheat flour are major staple food; pork is the major meat consumed by Chinese consumers, more than 50% of total consumed meat (Yu and Abler, 2014); and Chinese cabbage is the most popular vegetable in China given the fact that Chinese cabbage is the vegetable largest planted in China. These four food products can largely represent the daily basic food demand for Chinese consumers, particularly in a crisis time. Note that we only have the wholesale market prices, while the retail prices are various and not available so far. However, these prices still could represent the trend of change. The three regions can be seen as approximately representative. Shandong province is the largest vegetable producing base in China; Beijing is the capital city and the second-largest city in China, which is a consumption base for food products, while Hubei is the epicenter of COVID-19. The descriptive statistics and explanations for the variables are reported in Table 1.The trends of different food prices are depicted in Figure 1a–d. According to Table 1 and Figure 1a–d, we have some straightforward conclusions. 3.1.1 Rice and wheat flour prices. Table 1 shows that there are some regional differences in the prices, but the prices do not change much before and after the crisis in all three regions, though the continuity of the prices in Hubei province was disrupted during the crisis. Figures 1a–b are consistent with Table 1, though the price variations for both rice and wheat flour are much smaller in Hubei Province in comparison to Beijing and Shandong. We simply infer that the impact of COVID-19 on rice and wheat flour prices is insignificant. 3.1.2 Pork prices. Though Table 1 shows the pork prices for the period from January 1, 2020 to April 8, 2020 are much higher than those in the same period of 2019, the conclusion for a positive impact of COVID-19 cannot be simply drawn, as Figure 1c shows that the pork prices have been continuously increasing in all three provinces before Oct. 2019. This increasing trend of pork prices was mainly driven by the shock of the African Swine Flu. In order to find a robust conclusion, we have to build an econometric model to estimate it. 3.1.3 Chinese cabbage prices. Table 1 shows the Chinese cabbage prices are also much higher for the period from January 1, 2020 to April 8, 2020 than those in the same period of 2019, though a price spike is observed around the Chinese New Year in both 2018 and 2019. This could be the impact of COVID-19. However, a reliable conclusion should be drawn from the econometric analysis.
3.2 COVID-19 infections Since the outbreak of COVID-19 first in Wuhan, this disease has been spread to the whole nation and not in the whole world. Though the exact outbreak time is unknown, it is believed in Dec. 2019 (Verity et al., 2020). The severity of COVID-19 is widely known after China took strong quarantine measures, for instance, the lockdown of the whole Wuhan City from Jan. 23 until Apr. 8. The National Health Commission officially reported the confirmed infections on Jan. 17, which will be regarded as the outbreak time for our research. After paying high economic and living costs, China basically contained the disease. The trends of new confirmed infections and deaths are presented in Figures 2a and b, respectively. The shock of COVID-19 could have two dimensions: one is the outbreak itself, and the other is the severity. These two dimensions will be quantitatively analyzed in the econometric Variable Obs. Mean SD Max Min Mean1 Mean2 Data resources COVID-19 on food prices in Rice China Shandong 465 5.96 0.46 7.10 4.90 5.87 5.67 Shandong Gaishi Agricultural Trade Company Beijing 465 5.53 0.08 5.80 5.40 5.61 5.46 Beijing Xinfadi Agricultural Products Wholesale Market Hubei 428 4.08 0.04 4.10 4.00 4.10 4.00 Wuhan Baishazhou Agricultural 449 and Sideline Products Market Wheat flour Shandong 465 3.63 0.10 4.00 3.40 3.62 3.72 Qingdao Huazhong Vegetable Wholesale Market Beijing 465 3.16 0.09 3.30 3.04 3.28 3.15 Beijing Xinfadi Agricultural Products Wholesale Market Hubei 428 3.09 0.09 3.10 3.00 3.10 3.18 Wuhan Baishazhou Agricultural and Sideline Products Market Pork Shandong 463 36.59 13.92 59.80 19.50 21.24 53.96 Jinan Weikang Meat and Aquatic Products Wholesale Market Beijing 465 29.46 11.53 50.50 11.50 15.98 43.11 Beijing Xinfadi Agricultural Products Wholesale Market Hubei 448 25.16 10.30 41.38 10.00 12.92 36.52 Carcass Pork Price in Huanggang City and Hubei Province Chinese cabbage Shandong 465 1.32 0.56 3.00 0.50 1.18 2.10 Shouguang Agricultural Products Logistics Park Beijing 465 1.14 0.68 5.20 0.40 0.80 2.16 Beijing Xinfadi Agricultural Products Wholesale Market Hubei 465 0.93 0.56 5.00 0.40 0.81 1.44 Wuhan Baishazhou Agricultural Table 1. and Sideline Products Market Descriptive statistics of Note(s): 1. Data source: Wind database the samples (units: 2. The prices are wholesale market prices Yuan/Kg; time: 3. Mean denotes the mean value of whole sample; Mean1 denotes the mean value from January 1, 2019 to April January 1, 2019–April 8, 2019; Mean2 denotes the mean value from January 1, 2020 to April 8, 2020 8, 2020) models. In the econometric model, we will use the daily new confirmed infections to measure the severity of COVID-19.
3.3 Tests of autocorrelations The GARCH model has been widely applied to analyzing price volatility. However, the validity of GARCH depends on the autocorrelation of the time series. We used a Box-Pierce test to test autocorrelations for all price series in this study (Box and Pierce, 1970), and the test results are reported in Table 2. We find that the null hypothesis of no autocorrelations has been rejected for all price series. Hence, the GARCH model is a valid method.
3.4 Unit roots test Stationarity is often required for a normal GARCH model. That is, the time series does not have long-term memories. We often use the augmented Dicky–Fuller (ADF) test to test whether the timer series persists unit roots. In Table 3, the ADF test cannot reject the null hypothesis of unit roots basically for all series at the significance of 5%. In this case, following Baillie Richard et al. (1996), we have to use the Fractionally Integrated GARCH model (iGARCH), which allows long-term memory. CAER price 7.20 12,3 6.90 6.60 6.30 6.00 5.70 5.40 5.10 450 4.80 4.50
date shandong_rice beijing_rice (a) price 4.20 4.00 3.80 3.60 3.40 3.20 3.00
date shandong_flour beijing_flour wuhan_flour (b)
price 60.00 50.00 40.00 30.00 20.00 10.00
date shandong_pork beijing_pork wuhan_pork (c) price 6.00 5.00 Figure 1. 4.00 (a) Rice price in Shandong and Beijing; 3.00 (b) Wheat flour prices 2.00 in Shandong, Beijing 1.00 and Hubei; (c) Pork 0.00 prices in Shandong, Beijing and Hubei; (d) Chinese cabbages’ date prices in Shandong, Beijing and Hubei shandong_cabbage beijing_cabbage wuhan_cabbage (d) person COVID-19 on 16,000 food prices in 14,000 China 12,000 10,000 8,000 451 6,000 4,000 2,000 0
date
all_case all_case_exclude_Hubei Hubei_case wuhan_case (a) person 4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0 Figure 2. 1 4 7 10131619222528313437404346495255586164677073767982 (a) Daily change in day newly confirmed cases of COVID-19; (b) all_death all_deaths_exclude_Hubei Hubei_death wuhan_death Changes of deaths from COVID-19 (b)
4. Model 4.1 GARCH and Fractionally Integrated GARCH GARCH is a very flexible time series model and has been widely used in studying price volatility in the literature, as it can capture heterogeneities in both first- and second-order moments. We assume food price in China follows GARCH(1,1), which is widely known as a powerful tool in the literature (Yu, 2014; Wang et al., 2020). ¼ þ þ ε þ ρ ε yt u0 u1yt−1 t 1 t−1 (5) CAER Box-Pierce test ADF test 12,3 Values p-value Values p-value
Beijing Rice 429.15 0.000 3.3405 0.063 Flour 459.91 0.000 0.0920 0.990 Pork 459.45 0.000 2.3618 0.424 452 Cabbage 406.65 0.000 2.2047 0.491 Shandong Rice 443.57 0.000 1.3972 0.8329 Flour 394.34 0.000 1.8779 0.6296 Pork 460.48 0.000 2.0592 0.5529 Cabbage 435.85 0.000 2.6588 0.2993 Hubei Rice 414.98 0.000 Table 2. Flour 407.84 0.000 Box-Pierce test and Pork 444.37 0.000 1.6378 0.7305 ADF test Cabbage 324.74 0.000 3.3889 0.05557
Beijing Shandong Hubei Variables Coefficient t-value Coefficient t-value Coefficient t-value
Mean function AR(1) 0.9951*** 10.2465 0.9754*** 70.1790 0.9808*** 98.6197 MA(1) 0.13 0.3269 0.0628 1.2610 0.0347 0.7854 Outbreak 0.0476 0.7907 0.2446*** 5.8857 0.0178* 1.7384 No. of cases 0.0000 0.0267 0.0000 0.6832 0.0000*** 3.8582 Constant 0.7409* 1.8023 0.9569*** 12.8098 0.9878*** 31.7230 Variance function ARCH 0.926 0.7326 0.1900*** 4.7476 0.9089*** 16.0951 GARCH 0.0739 0.7326 0.8099*** 4.7476 0.091*** 16.0951 Outbreak 0.0000 0.0000 0.0007 0.7401 0.1017* 1.8046 No. of cases 0.0001 0.0287 0.0000*** 3.8643 0.0002 1.5856 Constant 0.0056 0.2182 0.0003** 2.0333 0.0393*** 4.7608 Distribution Student’s t-distribution Student’s t-distribution Student’s t-distribution Day dummy Yes Yes Yes Table 3. Month dummy Yes Yes Yes Impact of COVID-19 on Observations 466 466 466 the Chinese Likelihood 468.5234 338.2773 423.0422 cabbage price Note(s): *, ** and *** denote 1%, 5% and 10% statistical significance, respectively
ε jψ ∼ ð ; σ2Þ Where, yt is the food price at t, and t t N 0 t . Eqn (5) is the ARMA (1,1) model, which can capture most of the short-term shocks. In order to capture the generalize autoregressive conditional heteroskedasticity (GARCH) in the time series, we assume σ2 ¼ ω þ ασ2 þ βε2 t t−1 t−1 (6) We often assume α þ β < 1, which implies that the GARCH(1,1) process is weakly stationary as the mean, variance and autocovariance are finite and constant over time. In other words, the time series yt has no long-term memory. Unfortunately, this condition is not sufficient for weak stationarity in the presence of autocorrelation (which is indicated in Table 2). Following COVID-19 on the ideas of Integrated GARCH proposed by Engle and Bollerslev (1986), Baillie Richard et al. food prices in (1996) introduced a Fractionally Integrated GARCH (iGARCH). The iGARCH allows China α þ β ¼ 1: (7)
Such a specification allows yt pertains to long-term memory and allows unit roots in the GARCH process. As we cannot reject the hypothesis of unit roots for the prices in the above 453 Data section (Table 2), food prices in China show long-term memories. Therefore the iGARCH can be assumed to be an appropriate model to realize our research purposes in this paper.
4.2 Modelling shocks of COVID-19 The outbreak of COVID-19 was purely an exogenous event. This could shock both the mean and variance of prices. In addition, after the outbreak of COVID-19 in Wuhan in January, there are two effects. First, the outbreak itself could cause significant social, economic and psychological shocks; and the price could be different before and after the outbreak. We use a dummy variable to capture this effect. Second, the severity of COVID-19 may affect food prices in a different dimension. To consider this dynamic effect again, we simply include daily new infections of COVID-19 in the model. Hence, the model is specified as ¼ þ þ þ þ ε þ ρ ε yt u0 u1yt−1 u2Zt u3Ct t 1 t−1 (8) ε jψ ∼ ð ; σ2Þ 5 Where t t N 0 t ; Zt is a dummy variable ( 1 if after January 17, 2020; 0 if before January 17, 2020); and Ct is the number of daily new confirmed infections officially reported by the National Health Commission of China. σ2 ¼ ω þ ω þ ω þ ασ2 þ βε2 t 0 1Zt 2Ct t−1 t−1 (9) 5 ω 5 ρ α β α and ui(i 0,1,2), j(j 0,1,2), 1, and are parameters to be estimated. Particularly, is the coefficient for GARCH, while β for ARCH. Finally, the fat-tail problem could bias the estimation results in GARCH models (Verhoeven and McAleer, 2004), so that we assume εt follows a student’s t-distribution, which performs better than a normal distribution assumption.
4.3 Seasonality Furthermore, food prices often exhibit seasonality (Yu, 2014; Sun et al., 2017). For instance, the Chinese New Year holidays often push up food prices. As the data are a daily time series, we cannot use the moving average method or Holt–Winter Seasonal Method to remove the seasonality. We hence include dummy variables (St) for month and weekdays to control for the seasonality in the mean function. Thus, we can extend Eqn (9) as follow: σ2 ¼ ω þ ω þ ω þ ασ2 þ βε2 þ θ t 0 1Zt 2Ct t−1 t−1 St (10)
5. Results and discussion Tables 3–6 present the estimated results of the iGARCH(1, 1) with the exogenous shocks of COVID-19. In addition to the essential parameters in Eqs (5) and (6), we also include the time dummy to further control the seasonality effect on food prices. Specifically, we use dummies for each month and dummies for each weekday in a week. Additionally, we analyze the food price changes in Beijing, Shandong, and Hubei due to the reason that the three provinces are representative of agricultural food price markets in China during the spread of COVID-19, as is mentioned in Section 2. CAER Beijing Shandong Hubei 12,3 Variables Coefficient t-value Coefficient t-value Coefficient t-value
Mean function AR(1) 0.9990*** 292.2851 0.9985*** 2243.9588 0.9972*** 1225.5407 MA(1) 0.0473 0.7968 0.2688*** 6.5718 0.1220*** 3.5280 Outbreak 0.7789*** 5.5071 0.0944 1.8492 0.1138*** 3.3035 454 No. of cases 0.0000 1.0857 0.0000 0.3135 0.0000** 2.5315 Constant 15.3072*** 23.5474 84.5326*** 8.7949 45.2303*** 6.5373 Variance function ARCH 0.2947*** 3.0728 0.0500*** 26.5224 0.0500*** 24.2195 GARCH 0.7052*** 3.0728 0.9500*** 26.5224 0.9500*** 24.2195 Outbreak 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 No. of cases 0.0001 1.0011 0.0000 0.0003 0.0000 0.0000 Constant 0.0172 1.1336 0.1937*** 13.0619 0.0985*** 13.0124 Distribution Student’s t-distribution Student’s t-distribution Student’s t-distribution Day dummy Yes Yes Yes Month dummy Yes Yes Yes Table 4. Observations 466 466 466 Impact of COVID-19 on Likelihood 523.2179 770.4353 536.4091 the pork price Note(s): *, ** and *** denote 1%, 5% and 10% statistical significance, respectively
5.1 Product price difference 5.1.1 Chinese cabbage. Table 3 reports the impact of COVID-19 on the Chinese cabbage price. The results of the mean function show that the coefficients of AR(1) are statistically significant, indicating that the historical Chinese cabbage price has a significant positive effect on the present price. Notably, the outbreak of COVID-19 significantly increases the Chinese cabbage price in Shandong and Hubei. A possible explanation for this phenomenon is that the outbreak of COVID-19 has a more substantial effect on the Chinese cabbage demand than supply, or a simple social panic after the COVID-19 crisis. Specifically, people tend to hoard Chinese cabbage in the COVID-19 crisis due to its long shelf-life and low price, which would promote the Chinese cabbage demand, leading to the increases in the price in the short run. In contrast, the coefficient of the number of confirmed COVID-19 cases in Hubei is negative and statistically significant, which suggests that as the number of confirmed COVID-19 cases increases in Hubei, the Chinese cabbage price experiences a downturn trend. This is understandable because the Chinese government took strict regulations to lockdown the cities to contain the spread of COVID-19, which would reduce more demand for the Chinese cabbage than the negative effect of supply. Furthermore, the results of the variance function present that some of the coefficients of the COVID-19 outbreak and the confirmed cases are significantly positive, suggesting that the information of COVID-19 enhances the Chinese cabbage price variation. On the one hand, the spread of COVID-19 would undoubtedly affect both the consumption and production processes due to government regulations. On the other hand, the information of COVID-19 might have a psychological impact on social individuals, which could cause abnormal purchasing and producing behaviors. The disagreement on the impact is much higher among market players (Wang et al., 2020). Specifically, social individuals might have a different interpretation for the information of COVID-19, which could lead to different purchasing and producing behaviors. As a result, the variation of Chinese cabbage price increases. 5.1.2 Pork. Table 4 reports the impact of COVID-19 on the pork price. Likewise, the coefficients of AR(1) are positive and statistically significant. Importantly, the outbreak of COVID-19 has a significant impact on the pork price in Beijing and Hubei. As discussed in the Beijing Shandong COVID-19 on Variables Coefficient t-value Coefficient t-value food prices in China Mean function AR(1) 0.9531*** 29.2041 0.9998*** 2346.1282 MA(1) 0.0321 0.3135 0.0008 0.0775 Outbreak 0.0125 0.1246 0.0002 0.0427 No. of cases 0.0000 0.0016 0.0000 0.3069 455 Constant 5.5191*** 64.4741 5.0999*** 215.6270 Variance function ARCH 0.0500** 2.3961 0.0957 0.8405 GARCH 0.9500** 2.3961 0.9042 0.8405 Outbreak 0.0000 0.0052 0.0000 0.0000 No. of cases 0.0000 0.0001 0.0000 0.0000 Constant 0.0000 0.3835 0.0000 0.5592 Distribution Student’s t-distribution Student’s t-distribution Day dummy Yes Yes Month dummy Yes Yes Observations 466 466 Table 5. Likelihood 1437.664 1134.323 Impact of COVID-19 on Note(s): *, ** and *** denote 1%, 5% and 10% statistical significance, respectively the rice price
Beijing Shandong Variables Coefficient t-value Coefficient t-value
Mean function AR(1) 0.9936*** 112.3371 0.9991*** 4.8185 MA(1) 0.0083 0.4831 0.0042 0.1104 Outbreak 0.0012 0.1992 0.0999 1.0879 No. of cases 0.0000 0.0069 0.0000 0.0000 Constant 3.1186*** 27.7342 3.5434 0.3456 Variance function ARCH 0.0500*** 3.8959 0.9915** 2.1453 GARCH 0.9500*** 3.8959 0.0084** 2.1453 Outbreak 0.0000 0.0006 0.0000 0.0000 No. of cases 0.0000 0.0001 0.0000 0.0000 Constant 0.0000*** 6.5339 0.0000 0.0016 Distribution Student’s t-distribution Student’s t-distribution Day dummy Yes Yes Month dummy Yes Yes Observations 466 466 Table 6. Likelihood 1576.967 2088.738 Impact of COVID-19 on Note(s): *, ** and *** denote 1%, 5% and 10% statistical significance, respectively the flour price theoretical framework, such impact might be due to the changes in pork demand and supply. Regarding the opposite impact of the COVID-19 outbreak on the pork price in Beijing and Hubei, we will analyze the regional differences in the next section. Besides, the coefficient of the confirmed COVID-19 cases is significantly negative in Hubei. We interpret this as the lockdown effect on the pork price, which is consistent with the impact on the Chinese cabbage price. The severity of the diseases would cause stricter quarantine policies, which leads to even less demand. CAER However, in terms of the variance function, the coefficients of the variables related to the 12,3 COVID-19 are not significant. It evidences that the spread of COVID-19 might not affect the pork price variation. 5.1.3 Rice and flour. Tables 5 and 6 report the impact of COVID-19 on the rice and flour prices for Shandong and Beijing. Due to a long-time disruption of the price report for Wuhan during the lockdown, we cannot run the econometric model for Hubei province. Similarly, the historical prices of rice and flour have a significant positive effect on present prices. Yet, the 456 impacts of COVID-19 are not significant in the mean function. It might be due to the fact that rice and wheat flour are the main staple food in China. On the demand side, household staple food consumption is relatively stable. On the supply side, the Chinese government places great importance on staple food security, which could ensure the supplies of rice and flour even in the crisis. As such, the impact of COVID-19 is somewhat limited. Regarding the variance function, the impact of COVID-19 is not significant in terms of the rice and flour price variations. The possible interpretation might be similar to the results of the mean function.
5.2 Regional price differences As discussed above, the impact of COVID-19 is heterogeneous between different provinces. To better understand the impact of COVID-19 on food prices in China, we further provide some explanations for regional differences. 5.2.1 Heterogeneous impact on the Chinese cabbage price. As shown in Table 3, although the coefficients of the COVID-19 outbreak are significant in Shandong and Hubei provinces, the impact on the Chinese cabbage price in Shandong is much larger than Hubei. Moreover, the outbreak of COVID-19 significantly increases the Chinese cabbage price variation in Hubei, while the number of confirmed cases enhances the price variation in Shandong. The main reason for this is that Hubei province is the epicenter of COVID-19. The Chinese government implemented a series of regulations and also mobilized abundant social resources to ensure social stability in Hubei. As a result, the outbreak of COVID-19 in Hubei has less impact on the Chinese cabbage price than Shandong. Notably, the increase in the number of confirmed cases in Hubei would enhance the regulations and promote the supply of social resources, which could cause a decrease in the Chinese cabbage price. For instance, during the outbreak of COVID-19, many neighboring provinces deliver a lot of agricultural products to Hubei to help ensure food supply. 5.2.2 Heterogeneous impact on the pork price. Interestingly, Table 4 shows that the impact of COVID-19 outbreak on the pork price in Beijing is the opposite of Hubei. One main reason why the impact in Beijing is negative is due to the demand decrease. After the outbreak of COVID-19, people tend to stay at home to avoid the virus transmission, which might reduce the frequency of pork purchasing. However, the situation is quite different in Hubei, the epicenter of COVID-19. As we have known, the spread of COVID-19 is the severest in China. Under such circumstances, the public was in a state of panic, motivating people to hoard more pork due to the long shelf life in the fridge. The sudden increase in pork demand would promote the pork price. Similarly, the rise in the number of confirmed cases in Hubei also decreases the pork price, which is consistent with the impact on the Chinese cabbage price.
6. Conclusions and policy implication In general, this paper finds that the impacts of COVID-19 on food prices are heterogeneous in different regions for different products, though the general impact is rather minor so far. The main reason could be due to well-functioning but strict policies, both food policies and anti- COVID-19 policies. Different products have different impacts. Generally, we find that no significant changes in prices of staple foods, such as rice and wheat flour, a slight increase in prices of vegetables proxied by the Chinese cabbage, and various changes in prices of pork, which is the main COVID-19 on protein source for Chinese consumers. food prices in In Hubei Province, the epicenter of COVID-19, the outbreak of the disease increased both pork and cabbage prices, which may result from both panic emotion and the lockdown policy. China However, the number of confirmed infections has a significantly negative impact on food prices, though the magnitude is small. This can be explained by more reduced demand due to less out-door activities, including shopping frequencies, either required by the government or self-discipline. In Beijing, which is the second-largest city in China, we even find that pork 457 prices dropped slightly after the outbreak, which might be due to the similar reason. The Chinese governments have taken strict anti-COVID-19 policies that obviously have successfully contained the disease so far. During the process of the anti-COVID-19 campaign, sufficient food supply to every consumer to avoid the humanitarian crisis is put in a prior position. This could largely help to stabilize the market and reduce panic. These could be seen as good positive experiences for future China and other countries to ensure the food security of the population. Though the prices of rice and wheat flour have been stable, we should be cautious about it as the diseases have been spread globally. Many countries started to take travel ban policies, both domestically and internationally, and some took food exporting bans as well. This may destruct the global food supply chain, which may harm food security for many developing countries (FAO, 2020). It may take a few years to recover to a normal state. The food supply in China has been in a tight balance for decades. The food production is about 650 million tons in recent years, and import is more than 100 million tons of which soybeans are the major imported products. China always puts food security in a prior policy agenda and secure cereal supply domestically (Yu, 2018). The imports of rice, wheat and corn have been controlled in a low share of domestic supply with the use of the method of a tariff- rate quota. The total import of rice, wheat and corn is only about 10 million tons annually. Social panic from such infectious diseases could be the largest threat to food security, particularly in China. If social panic accompanied by the diseases occurs, people often start to hoard foods, which further worsen the market situation, and push up food prices. This will form a vicious cycle. For instance, given that the total rice supply in China is about 150 million tons if each consumer additionally hoards 10 kg, the total hoard will be about 10% of yearly supply, which will significantly shift the market balance and push up market prices substantially. This will cause further panic and food security issues. In this scenario, governments should pay more attention to stable social panic. Particularly, it is essential to timely release the information related to the situation of COVID-19 and food supply so as to stabilize the expectations of social individuals. As such, how to stable the possible market panic will be challenging policy issues for our future research.
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Corresponding author Hanjie Wang can be contacted at: [email protected]
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