The Impact of COVID-19 Policy Response on Food Prices: A Case Study in China

Abstract

The COVID-19 pandemic has threatened human lives and caused an unprecedented impact on the global economy, notably on the global food system. COVID-19 itself and its policy response have severely affected food prices. This paper analyzes the short-term and long-term impacts of COVID-19 cases and policy responses (government response index (GRI), economic support index (ESI), stringency index (SI)) on food prices (Chinese cabbage price (CP), pork price (PP), flour price (FP), rice price (RP)) in China’s major food producing areas and major food selling areas through the autoregressive distribution lag error correction (ARDL-ECM) method and graphical analysis. as the purpose is to provide evidence for food security policy regulation to stabilize food prices and improve the food system’s ability to withstand similar emergencies in the future. This study finds that the short-term impact of COVID-19 policy responses on food prices is larger, while the long-term impact is smaller. The impacts of COVID-19 itself and COVID-19 policy responses on food prices vary by food type and food production and consumption region. For food type, the Chinese cabbage price was most affected by the COVID-19 policy response, followed by pork, and the staple food prices (flour price and rice price) were least affected. For regions, the Chinese cabbage price in food producing areas was more influenced by COVID-19 and the pork price in food selling areas was more influenced by COVID-19. For the single indexes, GRI and SI had uneven effects on different food prices. However, ESI had a significant positive impact on the Chinese cabbage price (CP) and pork price(PP), except in Guangdong and Hubei. Staple food prices were almost unaffected by COVID-19 confirmed cases and policy responses. Then, taking the Chinese cabbage price in Beijing, Shandong, and Hubei as an example, we find that “dynamic clearance” influenced the Chinese cabbage price in the short term, and Chinese cabbage price increased largely in the first stage. Finally, the reasons behind the research results of this paper are analyzed, and scientific suggestions are put forward for the implementation of food supply policies to ensure food price stability and food security in the face of similar pandemics in the future.

1. Introduction

Since December 2019, the world has been affected by COVID-19 to varying degrees, and due to its strong transmissibility, it has gradually become a global pandemic, with increasing infections and deaths. According to the State of Food Security and Nutrition in the World (SOFI), the number of people affected by hunger globally rose to as many as 828 million in 2021, an increase of about 46 million since 2020 and 150 million since the outbreak of the COVID-19 pandemic. Many scholars have found that outbreaks of serious infectious diseases may lead to food crises [1,2] due to the abandonment of agricultural activities, disruption of food supply chains [3], reduced farmers’ incomes, and labor shortages, posing major challenges to food systems, agricultural systems, and food security [4,5]. COVID-19 has caused a crisis within a crisis, pushing the world further away from its goal of ending hunger, food insecurity, and malnutrition in all its forms by 2030.

Food price stability is an important component of food security [6]. Almost 3.1 billion people could not afford a healthy diet in 2020, up 112 million from 2019 because of the economic impacts of the COVID-19 pandemic (SOFI), which has seriously affected food security. Furthermore, the COVID-19 outbreak has prompted governments to impose restrictions (such as closing public transport, stay-at-home requirements, restrictions on internal movement, etc.) to contain the spread of the virus [7], which also reduce food production and international trade [8], affect food price stability, and induce food crises to some extent [9,10]. For example, as a result of the COVID-19 outbreak and its associated impacts, the staple food prices in Guinea, Libera, and Sierra Leone increased significantly, and the prices of rice and cassava increased by more than 30% and 150%, respectively [11]. In countries with higher COVID-19 case counts, average prices rose significantly, especially for more nutritious food groups [12]. In China, pork prices increased by 9.3% month on month, and the prices of fresh vegetables, fresh fruits, and aquatic products increased by 9.5%, 4.8%, and 3.0%, respectively, during the COVID-19 pandemic. Stable food prices can promote food security [13] and contribute to achieving the zero hunger goal (SDG2) in the 17 Sustainable Development Goals (SDGs) and advancing the 2030 Agenda for Sustainable Development. Therefore, more and more scholars have been focusing on the impact of COVID-19 policy response on food prices.

On the one hand, comparing changes in food prices before and after the implementation of COVID-19 policies is the most intuitive way to analyze the effect of COVID-19 on food prices [14]. Some scholars have compared and analyzed food price changes before and after the implementation of lockdown measures on different scales (including national and local) [15,16,17,18]. For example, COVID-19 has had no significant impact on wholesale fruit and vegetable prices in the United States but has led to a decline of approximately 10% in wholesale prices in China [15]. However, the impact of COVID-19 on food prices is not only related to regional variability but also depends on the type of food [16,17]. In three Indian states (Maharashtra, Jharkhand, and Meghalaya), compared to pre-lockdown prices, rice, onion, potato, and tomato prices rose [16]; in contrast, in Haryana, tomato prices dropped significantly [18]. In China, although many restrictions, such as “road closures and village closures”, adopted during the pandemic have greatly mitigated the spread of COVID-19 [19], they have also made food prices increase to some extent. Unlike in India, Yu et al. found that COVID-19 itself has had a relatively large impact on pork and cabbage prices and no significant impact on rice and wheat flour prices. However, this research only analyzed impact of the number of COVID-19 cases on food prices, not the impact of restrictions [20]. Ruan et al. found that the Chinese cabbage price increased substantially and its price dispersion surged because of the restrictions during the COVID-19 pandemic [21], although they did not consider the regional heterogeneity effects.

On the other hand, some studies have further quantified COVID-19 policy responses to analyze the impact of COVID-19 on food prices. Some scholars use the Oxford COVID-19 Government Response Tracker (OxCGRT) database to quantify epidemic prevention policies and analyze how different COVID-19 policy responses affect food prices. From different perspectives, relevant scholars have analyzed the six most affected countries (United States, Italy, France, United Kingdom, Russia, and Turkey) [22] and BRICS countries (Brazil, Russia, India, China, South Africa, and Turkey) [23] in terms of confirmed COVID-19 cases and the government COVID-19 policy response indices (such as stringency index (SI) and containment health index (CHI)), which have different influences on health care prices and food prices. In low- and middle-income countries, strict policy measures lead to higher food prices, but this impact is mainly achieved through liquidity restrictions [24]. Akter et al. [7] combined the Stay-at-Home Restriction Index (SHRI) in the OxCGRT dataset with the European Union’s Harmonised Index of Consumer Prices (HICP) and found a significant correlation between food prices and stay-at-home restrictions. Although the above studies quantified restrictive policies and further studied the relationship between COVID-19 policy response and food prices, few studies considered the impact of food types and regional heterogeneity, and there are few quantitative studies on the impact of China’s COVID-19 policies on food prices.

Therefore, based on previous studies, from the perspective of different food types and regional heterogeneity, this paper aims to determine the dynamic relationship between COVID-19 policy response and food prices, and also aims to summarize how the prices of long-shelf-life foods, short-shelf-life foods, and perishable foods were affected by policy responses and how food prices in different food production and consumption regions responded to COVID-19 policy. This study can provide evidence for China and other countries to adopt differentiated epidemic prevention policies to control virus transmission and stabilize food prices, and further provide scientific suggestions for policy regulation to ensure food supply and food security in the face of public health emergencies similar to COVID-19 pandemics in the future.

2. Materials and Methods

2.1. Study Area

Food prices in the food producing and selling regions were affected by COVID-19 differently. We selected Beijing, Shanghai, and Guangdong as food selling areas (Figure 1). According to the China Statistical Yearbook 2021, the per capita food consumption of Beijing, Shanghai, and Guangdong rank six, first, and second, respectively, in China; they are much higher than the country average. Furthermore, the economic development level of Beijing, Shanghai, and Guangdong is relatively high. The per capita disposable income of Beijing, Shanghai, and Guangdong is CNY 69,400, CNY 72,000, and CNY 41,000, ranking second, first, and sixth in China, respectively. Moreover, the grain yields of Beijing, Shanghai, and Guangdong are small, ranking thirty-first, twenty-eighth, and eleventh in China.

With regard to food producing areas, firstly, we selected Shandong, Henan, Hubei, and Hebei as vegetable producing areas (Figure 1). According to data released by the National Bureau of Statistics, the vegetable yield of Shandong, Henan, Hebei, and Hubei in 2020 was 84.347 million t, 76.124 million t, 41.914 million t, and 51.198.2 million t, ranked first, second, fourth, and sixth in China, respectively. Secondly, we selected Shandong, Henan, Hubei, and Hunan as meat producing areas. The meat output of Shandong, Henan, Hunan, and Hubei in 2019 was 3,649,100 t, 4,887,600 t, 3,834,000 t, and 2,687,900 t, ranking fourth, second, third, and eighth in China, respectively. Finally, we selected Henan and Shandong as grain producing areas. The grain outputs of Henan and Shandong are the largest in China, and their agricultural production capacity is higher than other regions. Additionally, Hubei is a special area, because it is not only a food producing area, but also a severely affected area, so Hubei is more representative as a research area. Above all, the food yield (vegetable yield, meat yield, flour yield, and rice yield) in Henan, Shandong, Hebei, Hunan, and Hubei account for a large proportion of the food produced in China, and they rank first, second, third, seventh, and eighth, respectively, in China.

2.2. Food Type

Different food types being affected differently by COVID-19 caused food prices to vary. Thus, foods are divided into long-shelf-life food, short-shelf-life food, and perishable food. Among them, long-shelf-life food mainly includes flour and rice. Flour and rice are the main staple foods consumed in China, with a large consumption scale and certain representability. Short-shelf-life food mainly refers to pork, because pork occupies a dominant position in China’s meat consumption, accounting for about 70% of the total meat consumption in China. Perishable food mainly refers to Chinese cabbage, which became the main vegetable consumed during the COVID-19 outbreak due to its ease of storage. Furthermore, to clarify the impact of COVID-19 on food prices, food prices were divided into Chinese cabbage price (CP), pork price (PP), and staple food prices (flour price (FP) and rice price (RP)).

2.3. Data Descriptions

This paper collects daily price data for four food types from major wholesale markets in Beijing, Shanghai, Hubei, Henan, Hebei, Hunan, and Shandong. The price data come from the Wind database. Some Chinese cabbage data are missing in Henan Province. The food price data of Guangdong Province are derived from the price monitoring data released by the Development and Reform Commission of Guangdong Province.

This article is derived from the Oxford COVID-19 Government Response Tracker (OxCGRT) [25], which spans from 1 January 2020 to 31 December 2020 and is available from the GitHub repository. Data were collected from publicly available sources such as news articles, government press releases, and briefings. The library documents government responses to COVID-19 in 31 provincial jurisdictions [26]. National and local government policies and interventions in some countries are tracked through a set of standardized indicators, and a composite index is created to measure the extent of these responses [25,26]. This paper mainly uses data from three composite indexes to reflect the strength of policy responses: the Government Response Index (GRI), Economic Support Index (ESI), and Stringency Index (SI). Among them, the values of the three composite indices vary between 0 and 100. The size of the value indicates the degree of strictness and a score of 100 indicates the highest degree of strictness, but a higher score does not necessarily mean that the policy is more effective.

2.4. Model Building

2.4.1. Consumer Models

Our model is based on Grossman’s health demand theory model. From the perspective of producers and consumers, combined with the theoretical basis, it is transformed into a food price model, and the health needs theory model is as follows:

$$H_t=I_t\left(M_t,t^i\right)-{\delta }_t{H}_t$$

(1)

where $H_t$ is health capital; $I_t$ is gross investment during interval t; $M_t$ is medical care; $t^i$ is own time spent on sports, for instance; and ${\delta }_t$ is the rate of health capital depreciation determined by exogenous variables. Equation (1) shows that health needs are determined by healthcare and exercise time, and health capital is declining at a constant rate, so this paper assumes that food prices are mainly affected by exogenous factors during the COVID-19 pandemic and substitutes it into Equation (1) to obtain Equation (2), taking the five exogenous variables CC, NCC, GRI, ESI, and SI as the independent variables and FDP as the dependent variable. The dependent variable in this paper is divided into four food prices, which are recorded as CP, PP, FP, and RP, to explore the relationship between the four food prices and five independent variables.

$$F_{FDP}=f\left(CC,NCC,GRI,ESI,SI\right)\times F_{FDP}$$

(2)

2.4.2. ARDL-ECM Model

The autoregressive distribution lag error correction model (referred to as the “ARDL-ECM model”) can directly model multi time series nonstationary sequences without considering that there may be endogeneity problems of explanatory variables, does not require variables to be homogeneous and monolithic, and is more flexible in the selection and setting space of variables [5]. The model can consider both long-term and short-term factors and estimate the residual sequence of model parameters through the least squares method (LS), which can effectively avoid the occurrence of spurious regression [27].

In the construction of the model, the one square root test is first performed on the food price data (CP, PP, FP, RP) and COVID-19 policy data (CC, NCC), and the unit root test (ADF) is used in this study. Second, the residual sequence is obtained through a simple linear transformation, and the stationarity of the residual sequence is tested. Finally, the ARDL-ECM model is established with Eviews 10, and the cointegration test is performed. In this study, the ARDL-ECM model is constructed as follows:

$$\begin{array}{l}∆{lnCP}_t={\sigma }_0+{\beta }_0{lnCP}_{t-i}+{\beta }_1{lnGRI}_{t-i}+{\beta }_2{lnESI}_{t-i}+{\beta }_3{lnSI}_{t-i}+{\beta }_4{lnCC}_{t-i}+{\beta }_5{lnNCC}_{t-i}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_0{\sum }_{a_{cp}=0}^{m_{cp}}∆{lnCP}_{t-a_{cp}}+{\theta }_1{\sum }_{b_{cp}=0}^{n_{cp}}∆{lnGRI}_{t-b_{cp}}+{\theta }_2{\sum }_{c_{cp}=0}^{p_{cp}}∆{lnESI}_{t-c_{cp}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_3{\sum }_{d_{cp}=0}^{q_{cp}}∆{lnSI}_{t-d_{cp}}+{\theta }_4{\sum }_{e_{cp}=0}^{r_{cp}}∆{lnCC}_{t-c_{cp}}+{\theta }_5{\sum }_{f_{cp}=0}^{s_{cp}}∆{lnNCC}_{t-f_{cp}}+{\mathsf{\lambda }ECM}_{t-1}\end{array}$$

(3)

$$\begin{array}{l}∆{lnPP}_t={\sigma }_0+{\beta }_0{lnPP}_{t-i}+{\beta }_1{lnGRI}_{t-i}+{\beta }_2{lnESI}_{t-i}+{\beta }_3{lnSI}_{t-i}+{\beta }_4{lnCC}_{t-i}+{\beta }_5{lnNCC}_{t-i}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_0{\sum }_{a_{pp}=0}^{m_{pp}}∆{lnPP}_{t-a_{pp}}+{\theta }_1{\sum }_{b_{pp}=0}^{n_{pp}}∆{lnGRI}_{t-b_{pp}}+{\theta }_2{\sum }_{c_{pp}=0}^{p_{pp}}∆{lnESI}_{t-c_{pp}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_3{\sum }_{d_{pp}=0}^{q_{pp}}∆{lnSI}_{t-d_{pp}}+{\theta }_4{\sum }_{e_{pp}=0}^{r_{pp}}∆{lnCC}_{t-c_{pp}}+{\theta }_5{\sum }_{f_{pp}=0}^{s_{pp}}∆{lnNCC}_{t-f_{pp}}+{\mathsf{\lambda }ECM}_{t-1}\end{array}$$

(4)

$$\begin{array}{l}∆{lnFP}_t={\sigma }_0+{\beta }_0{lnPP}_{t-i}+{\beta }_1{lnGRI}_{t-i}+{\beta }_2{lnESI}_{t-i}+{\beta }_3{lnSI}_{t-i}+{\beta }_4{lnCC}_{t-i}+{\beta }_5{lnNCC}_{t-i}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_0{\sum }_{a_{fp}=0}^{m_{fp}}∆{lnFP}_{t-a_{fp}}+{\theta }_1{\sum }_{b_{fp}=0}^{n_{fp}}∆{lnGRI}_{t-b_{fp}}+{\theta }_2{\sum }_{c_{fp}=0}^{p_{fp}}∆{lnESI}_{t-c_{fp}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_3{\sum }_{d_{fp}=0}^{q_{fp}}∆{lnSI}_{t-d_{fp}}+{\theta }_4{\sum }_{e_{fp}=0}^{r_{fp}}∆{lnCC}_{t-c_{fp}}+{\theta }_5{\sum }_{f_{fp}=0}^{s_{fp}}∆{lnNCC}_{t-f_{fp}}+{\mathsf{\lambda }ECM}_{t-1}\end{array}$$

(5)

$$\begin{array}{l}∆{lnRP}_t∆={\sigma }_0+{\beta }_0{lnRP}_{t-i}+{\beta }_1{lnGRI}_{t-i}+{\beta }_2{lnESI}_{t-i}+{\beta }_3{lnSI}_{t-i}+{\beta }_4{lnCC}_{t-i}+{\beta }_5{lnNCC}_{t-i}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_0{\sum }_{a_{rp}=0}^{m_{rp}}∆{lnRP}_{t-a_{rp}}+{\theta }_1{\sum }_{b_{rp}=0}^{n_{rp}}∆{lnGRI}_{t-b_{rp}}+{\theta }_2{\sum }_{c_{rp}=0}^{p_{rp}}∆{lnESI}_{t-c_{rp}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\theta }_3{\sum }_{d_{rp}=0}^{q_{rp}}∆{lnSI}_{t-d_{rp}}+{\theta }_4{\sum }_{e_{rp}=0}^{r_{rp}}∆{lnCC}_{t-c_{rp}}+{\theta }_5{\sum }_{f_{rp}=0}^{s_{rp}}∆{lnNCC}_{t-f_{rp}}+{\mathsf{\lambda }ECM}_{t-1}\end{array}$$

(6)

In Equations (3)–(6), ∆ is the lag operator showing short-term changes, ln is logarithm, σ₀ is the constant coefficient, ꞵ₀, ꞵ₁, ꞵ₂, ꞵ₃, ꞵ₄, and ꞵ₅ are coefficients of long-term parameters, θ₀, θ₁, θ₂, θ₃, θ₄, and θ₅ are coefficients of short-term parameters, ECM is the error correction term, and λ is the coefficient of the error correction term. When λ < 0, it has practical significance, and it reflects the adjustment speed when the equilibrium relationship between variables deviates from the long-term equilibrium level and adjusts it to the long-term equilibrium state. When λ < 0.5, the adjustment speed is fast; when λ = 0.5, the adjustment speed is moderate; when λ > 0.5, the adjustment speed is slow.

Given the above, this paper chooses the three COVID-19 policy response indices (GRI, ESI, SI) from the OxCGRT database, and the number of COVID-19 cases (CC, NCC) as independent variables and food prices (CP, PP, FP, and RP) as dependent variables to analyze how COVID-19 policy response affects food price. Finally, combined with the health demand theory model and constructing econometric models, we research the changes in short-term and long-term relationships between the four kinds of foods’ daily prices and COVID-19 policies in different regions.

2.5. Descriptive Statistics

2.5.1. Unit Root Tests and Cointegration Test

Two important conditions must be met before an ARDL model can be implemented. First, all variables must be stable, and the homeliness test can effectively avoid the problem of spurious regression. If the variable is not stationary, differential processing can be performed, but at most, one order differential processing must be stable, that is, the variable can be I(1) stationary or I(2) stable. The stationarity test methods mainly include the augmented Dickey–Fuller (ADF) unit root test [28], Phillips–Perron (P-P) unit root test [29], and Ng–Perron (N-P) unit root test [30]. Variables such as CC are tested for unit roots. Second, a long-term cointegration test must be carried out. The traditional E-G test [31] or Johansen cointegration test [32] has limitations and cannot be used to perform cointegration tests on variables of different orders, so this paper adopts the ARDL boundary cointegration test [33]. According to the F-statistic test null hypothesis, if the F value is greater than the critical value at the significance level, this means the null hypothesis should be rejected, that is, there is a long-term equilibrium relationship between variables. Finally, a simple linear transformation is performed, applying an error correction model derived from the ARDL model (ARDL-ECM model).

Table 1. Unit root test results (ADF).

	lnGRI	lnESI	lnSI	lnCC	lnNCC	lnCP	lnPP	lnFP	lnRP
Beijing
I(0)	−0.04 ***	——	——	——	−0.09 **	——	——	——	——
I(1)	——	−0.20 ***	−0.17 *	−0.64 ***	——	−1.18 ***	−1.48 ***	−1.33 ***	−1.01 ***
Shanghai
I(0)	——	——	——	——	−0.05 *	——	——	——	——
I(1)	−1.01 ***	−0.16 ***	−0.20 ***	−0.48 ***	——	−0.83 ***	−1.47 ***	−1.50 ***	−1.33 ***
Guangdong
I(0)	——	——	——	——	−0.07 **	——	——	——	——
I(1)	−1.00 ***	−0.38 ***	−0.16 ***	−0.70 ***	——	−1.13 ***	−1.00 ***	−1.00 ***	−1.01 ***
Hubei
I(0)	——	——	——	——	−0.04 *	——	——	——	——
I(1)	−1.00 ***	−0.48 ***	−0.61 ***	−0.98 ***	——	−1.20 ***	−0.55 ***	−2.06 ***	−1.13 ***
Henan
I(0)	——	——	——	——	−0.04 **	——	——	——	——
I(1)	−1.00 ***	−0.19 ***	−0.23 ***	−0.30 ***	——	−1.20 ***	−1.01 ***	−1.15 ***	−1.27 ***
Shandong
I(0)	——	——	——	——	−0.09 **	——	——	——	——
I(1)	−1.01 ***	−0.27 ***	−2.30 ***	−0.18 ***	——	−1.08 ***	−0.93 ***	−1.61 ***	−2.15 ***
Hebei
I(0)	——	——	——	——	−0.05 **	——	NA	NA	NA
I(1)	−1.00 ***	−0.20 ***	−0.13 ***	−0.15 ***	——	−1.53 ***	NA	NA	NA
Hunan
I(0)	——	——	——	——	0.28 ***	NA	——	NA	NA
I(1)	1.00 ***	−0.197 ***	−1.00 ***	0.54 ***	——	NA	−0.74 ***	NA	NA

Note: ***, **, and * symbolize that the null hypothesis is rejected at the 1%, 5%, and 10% significance levels, respectively. NA indicates that data are missing. I(0) indicates the original sequence stationary, and I(1) indicates the 1st difference stationary. Ln indicates the logarithm of all data.

and

Table 2. Cointegration test for COVID-19 effects on food prices.

	Beijing	Shanghai	Guangdong	Hubei	Henan	Shandong	Hebei	Hunan
CP F-Statistics	74.292 ***	3.681 ***	14.137 ***	4.625 ***	NA	76.422 ***	55.080 ***	NA
PP F-Statistics	9.824 ***	9.112 ***	11.753 ***	4.598 ***	10.329 ***	8.781 ***	NA	7.211 ***
FP F-Statistics	25.837 ***	3.006 ***	20.481 ***	72.860 ***	28.259 ***	51.236 ***	NA	NA
RP F-Statistics	41.142 ***	7.396 ***	44.587 ***	51.825 ***	20.061 ***	48.378 ***	NA	NA

Note: *** symbolize that the null hypothesis is rejected at the 1% significance levels. NA indicates that data are missing.

show the ADF test and cointegration test results, respectively. From Table 1, lnESI (ESI) in Beijing and lnNCC (NCC) in the remaining provinces are the original sequence stable. The seven provinces lnCHI (CHI), lnGRI (GRI), lnSI (SI), lnCC (CC), lnCP (CP), lnPP (PP), lnFP (FP), and lnRP (RP) are all 1st difference stable. Except for Beijing, the remaining six provinces lnESI (SI) are the 1st difference stable. From the results of Table 2, under the condition that CP, PP, FP, and RP are used as dependent variables, the F-statistical values of all dependent variables pass the significance test at the 1% level, indicating that there is a long-term equilibrium relationship between all variables.

2.5.2. Stability Diagnostics

Stability testing of model parameters is an important part of every empirical study, and if the established error correction model is not stable, the parameter estimation results will be biased. To prove the robustness and reliability of the simulated effects of each empirical study, the Breusch–Godfrey LM test and White’s test stability tests were performed on the model parameters of each province, and

Table 3. Diagnostics test results.

	Breusch–Godfrey LM Test				White’s Test
	F-Statistic				F-Statistic
	(lnCP)	(lnPP)	(lnFP)	(lnRP)	(lnCP)	(lnPP)	(lnFP)	(lnRP)
Beijing	2.597 *	2.421 **	4.167 **	2.462 ***	7.069 ***	2.838 ***	2.197 ***	10.144 ***
Shanghai	3.963 **	3.065 **	1.694 *	3.125 **	17.093 ***	3.964 ***	2.305 **	21.450 ***
Guangdong	1.827 **	3.402 **	2.746 ***	2.409 **	8.390 ***	14.948 ***	7.433 ***	7.744 ***
Hubei	1.388 *	3.658 *	3.863 *	4.661 ***	29.920 ***	18.552 ***	4.245 ***	4.281 ***
Henan	2.155 **	1.673 *	3.171 **	2.453 **	5.487 ***	7.630 ***	31.822 ***	6.645 ***
Shandong	2.853 **	5.215 **	3.164 **	2.554 **	52.669 ***	48.484 ***	17.704 ***	18.006 ***
Hebei	2.329 *	NA	NA	NA	2.211 ***	NA	NA	NA
Hunan	NA	3.974 **	NA	NA	NA	2.353 **	NA	NA

Note: ***, **, and * symbolize that the null hypothesis is rejected at the 1%, 5%, and 10% significance levels, respectively. NA indicates that data are missing.

shows that the overall stability of all models in each province passed the significance test, and the regression parameters were stable and reliable.

Taking Shanghai as an example, this paper plots the CUSUM (recursive residual accumulation) estimation test plots of the ARDL-ECM model of cabbage, pork, flour, and rice (See Figure 2), and the results show that in all samples, the fluctuation range of the ARDL-ECM model CUSUM value of lnCP, lnPP, lnFP, and lnRP in Shanghai is within the confidence interval of the 5% significance level. Although some parts exceed the bounds of the confidence interval, this does not affect the overall stability of the model itself.

3. Results

3.1. Chinese Cabbage Price Results

Table 4. Results from the ARDL estimation (dependent variable: CP).

	Beijing	Shanghai	Guangdong	Hubei	Hebei	Henan	Shandong
C	−0.005	−0.002	−0.0002	−0.373	−0.003	NA	−0.003
∆lnGRI	1.030	1.126	−0.789	−6.398 **	5.837 ***	NA	1.768 **
lnGRI	0.845	1.541	−0.643 ***	−5.012 ***	5.232 **	NA	1.624 ***
∆lnESI	0.143 ***	0.082 *	−0.097	0.310 ***	−0.029	NA	0.187 ***
lnESI	0.134 ***	0.116 ***	−0.026 ***	0.243 ***	−0.026	NA	0.172 ***
∆lnSI	−1.048	−0.056	−0.059	0.491	−0.448 **	NA	0.130 *
lnSI	−0.121	−0.077	−0.049 *	0.385	−0.401 **	NA	0.120 ***
∆lnCC	0.097 ***	0.016 **	0.013	−0.044	−0.007	NA	−0.062
lnCC	0.795 ***	0.022 **	0.010 *	−0.035	−0.006	NA	−0.057
∆lnNCC	0.001	0.003	0.006	0.008 *	−0.002	NA	0.005
lnNCC	0.001	0.004	0.004 **	0.006 *	−0.002	NA	0.004
ECM (−1)	−0.035 **	−0.371 ***	−0.104 ***	−0.063 **	−0.044 **	NA	−0.024 *
F-Statistics	8.280 ***	19.174 ***	4.786 ***	8.362 ***	7.794 ***	NA	16.266 ***
Sample	329	326	300	328	327	NA	328

Note: ***, **, and * symbolize that the null hypothesis is rejected at the 1%, 5%, and 10% significance levels, respectively. ECM (−1) implies an error correction term that shows the speed of adjustment to long-run equilibrium.

shows that with the continuation of the pandemic and the implementation time of epidemic prevention policies, Chinese cabbage prices in all provinces will be affected in the long term (ECM (−1) < 0). Additionally, ECM (−1) passed the test at the significance level of 10%, and the coefficient was −0.024, indicating that after the Chinese cabbage price deviated from the long-term equilibrium state in the short term, it adjusted to the long-term equilibrium direction at a speed of 0.24% in Shandong. ECM (−1) passed the test at the significance level of 5%, and the coefficients were −0.035, −0.063, and −0.044, indicating that after the Chinese cabbage price deviated from the long-term equilibrium state in the short term, it adjusted to the long-term equilibrium direction at a speed of 0.7%, 1.26%, and 0.88% in Beijing, Hubei, and Hebei, respectively. Similarly, ECM (−1) passed the test at the significance level of 1%, and the coefficients were −0.371 and −0.104, indicating that after the Chinese cabbage price deviated from the long-term equilibrium state in the short term, it adjusted to the long-term equilibrium direction at a speed of 37.1% and 10.4% in Shanghai and Hubei, respectively.

In the major vegetable selling areas (Table 4) Beijing and Shanghai, as China’s first-tier cities, CC has a positive and significant impact on the CP, which is consistent with the impact of ESI. However, GRI and SI have no significant impact on the CP in Beijing and Shanghai. The difference is that GRI, SI, CC, and NCC have no short-term impact on the CP in Guangdong Province, but in the long term, GRI and SI will have a negative impact on the CP in Guangdong Province, and CC and NCC have a positive impact on the CP. In the major vegetable producing areas (Table 4), CP in Hebei and Shandong is mainly affected by GRI and SI. The difference is that GRI has a significant positive impact on the CP in Hebei and Shandong. In principle, the government’s strict lockdown measures will cause the CP to rise, but the results show that SI has a significant negative/positive impact on the CP in Hebei and Shandong. That is, the strictness of the lockdown measures will decrease the CP in Hebei Province and increase the CP in Shandong Province. Hubei Province, as a major producing area, is also the most serious epidemic area, and GRI has a significant negative impact on the CP while ESI has a significant positive impact on the CP.

In general, CP coefficients in food producing areas are smaller than in food selling areas, which indicates that the CPs in food producing areas are more affected by COVID-19 policy response than those in food selling areas.

3.2. Pork Price Results

In 2020, PPs were mainly affected by African swine fever and COVID-19, but it has been proven that African swine fever had a small impact on pork prices [34], and African swine fever was not the main factor in this study. From the overall results in

Table 5. Results from the ARDL estimation (dependent variable: PP).

	Beijing	Shanghai	Guangdong	Hubei	Henan	Shandong	Hunan
C	−0.001	−0.0014	−0.004	0.00027	−0.00029	−0.002	−0.001
∆lnGRI	−1.517 ***	−1.691 ***	0.029	0.159 **	−0.230 **	−0.364 **	−1.497 *
lnGRI	−0.965 **	−1.144 ***	0.021	0.589 *	−0.422 **	−0.383 **	−0.758 **
∆lnESI	0.057 ***	0.063 **	−0.0005	−0.031 ***	0.014 **	0.017 **	1.644 *
lnESI	0.036 **	0.042 ***	0.0004	−0.115 **	0.020 **	0.018 **	2.355 **
∆lnSI	−0.060	0.148 **	−0.108 *	−0.019	−0.052 **	0.020 **	0.00035
lnSI	−0.038	0.100 *	−0.078 **	−0.070	−0.073 **	0.021 **	0.00050
∆lnCC	0.019	0.004	−0.017 **	0.015 ***	0.009 **	0.010	0.022 *
lnCC	0.012	0.003	−0.012 **	0.054 **	0.012 **	0.011	0.032 *
∆lnNCC	0.006 **	−0.007 **	0.002	0.0001	−0.005	0.001	−0.005
lnNCC	0.004 **	−0.005 **	0.002	0.0004	−0.006	0.001	−0.007
ECM (−1)	−0.048 **	−0.032 *	−0.034 *	−0.012 *	−0.015 **	−0.010 *	−0.083 *
F-Statistics	9.580 ***	7.937 ***	7.710 ***	35.020 ***	3.232 ***	4.705 ***	2.162 ***
Sample	326	327	149	361	327	149	95

Note: ***, **, and * symbolize that the null hypothesis is rejected at the 1%, 5%, and 10% significance levels, respectively. ECM (−1) implies an error correction term that shows the speed of adjustment to long-run equilibrium.

, the long-term equilibrium coefficient of each province is small; ECM (−1) < 0 indicates that PP deviations from the long-run equilibrium will gradually be corrected. Additionally, ECM (−1) passed the test at the significance level of 10%, and the coefficient was −0.032, −0.034, 0.012, 0.010, and 0.083, indicating that after the pork price deviated from the long-term equilibrium state in the short term, it adjusted to the long-term equilibrium direction at a speed of 0.32%, 0.34%, 0.12%, 0.01%, and 0.83% in Shanghai, Guangdong, Hubei, Shandong, and Hunan, respectively. In addition, except for Hubei and Guangdong, GRI had a negative significant effect on the PP in various provinces, and ESI had a positive and significant effect on the PP in various provinces.

In the major pork selling areas, the first-tier cities of Beijing and Shanghai, the PP is slightly affected by the impact of COVID-19, and NCC has a positive/negative impact on the PP in Beijing and Shanghai, but this impact is weak and with the increase in NCC, PP increases by about 0.12%. In Beijing and Shanghai, GRI has a negative and significant effect on the PP and ESI has a positive and significant effect on the PP. In contrast, SI has a positive and significant effect on the PP in Shanghai, but it has no significant effect on the PP in Beijing. Nevertheless, the impact of COVID-19 policy response on the PP in Guangdong is different from that on the PP in Beijing and Shanghai. GRI, ESI, and NCC have no significant effect on the PP, but SI and CC have a negative and significant effect on the PP.

As the main pork producing areas, CC is significantly positively correlated with the PP in Hubei, Henan, and Hunan Provinces, indicating that the increase in confirmed cases increases the possibility of rising pork prices. In Henan, Shandong, and Hunan Provinces, GRI has had a negative impact on the PP and ESI has a positive impact on the PP. SI has a negative and positive impact on the PP in Henan and Shandong, respectively. However, as a pork producing area and severely affected area, the PP in Hubei is different from that in Henan, Shandong, and Hunan. In contrast, GRI has a positive effect on the PP and ESI has a negative effect on the PP in Hubei.

3.3. Staple Food Price Results

From the results in

Table 6. Results from the ARDL estimation (dependent variable: FP).

	Beijing	Shanghai	Guangdong	Hubei	Henan	Shandong
C	−0.001	−0.00007	0.0003	0.000088	0.00011	0.00023
∆lnGRI	0.519	−0.022	−0.032	0.346	−0.320	−0.340
lnGRI	0.237	−0.015	−0.033	0.279	−0.290	−0.276
∆lnESI	−0.289	0.0005	0.0007	−0.012	0.010	0.024
lnESI	−0.132	0.0004	0.0007	−0.010	0.009	0.020
∆lnSI	−0.010	−0.014	0.003	0.029	0.155	0.029
lnSI	−0.004	−0.010	0.003	0.024	0.141	−0.026
∆lnCC	0.188	−0.001	0.0006	0.005	0.002	−0.009
lnCC	0.085	−0.001	0.0006	0.004	0.001	−0.007
∆lnNCC	−0.001	−0.0003	−0.0004	0.002	0.005	−0.001
lnNCC	−0.0005	−0.0003	−0.0004	0.002	0.004	−0.0006
ECM (−1)	−0.088 **	−0.041 **	−0.157 ***	−0.410 ***	−0.148 ***	−0.157 ***
F-Statistics	2.957 **	4.464 ***	2.551 ***	17.820 **	6.626 ***	17.828 ***
Sample	351	353	150	363	354	328

Note: *** and ** symbolize that the null hypothesis is rejected at the 1% and 5% significance levels, respectively. ECM (−1) implies an error correction term that shows the speed of adjustment to long-run equilibrium.

and

Table 7. Results from the ARDL estimation (dependent variable: RP).

	Beijing	Shanghai	Guangdong	Hubei	Henan	Shandong
C	0.000037	0.00016	−0.00013	0.00004	0.00037	NA
∆lnGRI	−0.005	−0.010	0.102	0.0005	−0.462 ***	NA
lnGRI	−0.0007	−0.006	0.085	0.0005	−0.450	NA
∆lnESI	−0.0004	−0.00008	−0.003	0.0001	0.013	NA
lnESI	−0.0004	−0.00005	−0.003	0.0001	0.013	NA
∆lnSI	−0.0003	0.001	−0.030	−0.028	0.297 ***	NA
lnSI	−0.0003	0.001	−0.025	−0.026	0.290	NA
∆lnCC	0.0005	−0.00003	−0.0004	−0.002	0.071 ***	NA
lnCC	0.0002	−0.00002	−0.0003	−0.002	0.069	NA
∆lnNCC	−0.0002	0.000014	0.001	0.00021	−0.0002	NA
lnNCC	−0.0002	0.000059	0.001	0.00017	−0.0002	NA
ECM (−1)	−0.070 ***	−0.012 **	−0.412 ***	−0.100 ***	−0.170 ***	NA
F-Statistics	1.742 *	6.460 ***	12.768 ***	2.872 **	7.524 ***	NA
Sample	354	353	351	363	350	NA

Note: ***, **, and * symbolize that the null hypothesis is rejected at the 1%, 5%, and 10% significance levels, respectively. ECM (−1) implies an error correction term that shows the speed of adjustment to long-run equilibrium.

, the long-term equilibrium relationship of the staple food (flour and rice) price in each province passed the significance test, and ECM (−1) < 0, indicating that the staple food price in each province will deviate from the long-term equilibrium in the short term due to COVID-19, but it can return to the long-term equilibrium relationship as soon as possible after experiencing short-term disturbances from COVID-19.There are regional differences in the impact of government policy responses on staple food prices during the COVID-19 pandemic, and the impact of GRI, ESI, SI, CC, and NCC in Beijing, Shanghai, Guangdong, Hubei, and Henan on the FP and RP and the impact of GRI, ESI, SI, CC, and NCC on the FP in Shandong do not pass the significant test, which indicates that the effect of the COVID-19 policy response on staple food prices is not statistically significant.

3.4. Impact of “Dynamic Clearance” on Chinese Cabbage Price Graphic Analysis

In combatting the Delta variant, China has maintained the policy target of “dynamic clearance”, aiming to halt all community transmission of COVID-19 [25]. To further visualize the impact of China’s “dynamic clearance” epidemic policy response on cabbage prices, we selected Shandong, Beijing, and Hubei Provinces for phased data visual analysis and chose GRI, ESI, and SI to represent the COVID-19 policy response and the CP to perform phased data analysis. For graphic analysis, the Chinese cabbage price was calculated and analyzed.

According to the situation of the first wave of the outbreak in Beijing (Beijing CP (a) in Figure 3), after the national lockdown policy was implemented on 23 January, the Chinese cabbage price in Beijing during the first stage (23 January to 18 February) significantly increased by about 112.57 percent compared to the period before the lockdown. In the second stage (from 18 February to 30 April), the Chinese cabbage price in Beijing dropped 21.87 percent compared to the previous stage, although the policy (GRI, ESI, and SI) was stricter than the previous stage. In terms of the second wave of the outbreak in Beijing, in the third stage (7 June–21 June), the policy implementation intensity was close to that of the second stage, and the Chinese cabbage price increased slightly, by about 39.46 percent, much less than in the first stage (112.57 percent). In the fourth stage (21 June to 12 July) and fifth stage (12 July to 31 August), the Chinese cabbage price still maintained the trend of gradual decline. Therefore, as can be seen from the graphic results, in the early stage of the outbreak, the impact on the Chinese cabbage price was significant, but with the implementation of the COVID-19 policy response, the impact of the policy on the Chinese cabbage price gradually decreased.

Moreover, taking the second COVID-19 pandemic in Beijing as an example (Beijing CP (b) in Figure 3), from the perspective of the COVID-19 policy implementation at this specific time, affected by the second epidemic, on 13 June, the Beijing agricultural products wholesale market closed, and from 14 June to 16 June, Beijing experienced shortages of most supermarket vegetables, and vegetable prices increased significantly. However, the local government set up a temporary trading venue on 16 June, and the circulation efficiency of agricultural products rose rapidly, filling Beijing’s vegetable supply gap. Beijing’s multilevel market supervision department plays an active role in maintaining the stability of the food market. Even when the second epidemic broke out, the vegetable supply was guaranteed, and the Chinese cabbage price fluctuation was relatively stable.

With the increase in the number of infected people in the major food producing area of Shandong Province, COVID-19 policy implementation became stricter, and Chinese cabbage prices increased by about 47.15% from 24 January 2020 to 4 February 2020. With the decline in the strictness of the policy after February 10, the Chinese cabbage price also decreased by about 17.85 percent. In the later stage (31 March to 30 April), the implementation of the COVID-19 policy response did not cause a large change in the Chinese cabbage price and it even gradually fell back to the same level as in 2019. After the third stage, “dynamic clearance” was achieved, and there were no unusual fluctuations in Chinese cabbage price.

As Shandong is a major vegetable producing area, its vegetable supply is sufficient (Shandong CP in Figure 4). Although the Chinese cabbage price in Shandong increased in the first stage (24 January to 4 February), an increase of about 47.15% compared to before the lockdown, the increase degree was much smaller than that in Beijing, the major selling area. Then, after 4 February, although the epidemic continued, the Chinese cabbage price in Shandong gradually decreased.

Hubei Province, a center of the affected area, essentially achieved “dynamic clearance” on March 21 (Hubei CP in Figure 5). From 23 January to 4 February, the outbreak of COVID-19 for two weeks, due to the impact of entry and exit restrictions and people’s expected psychological hoarding, led to an increase in daily demand for necessities and shortages in supermarket stocks, and the Chinese cabbage price rose significantly. In stage 1 (23 January to 4 February), the Chinese cabbage price surged in Hubei, increasing by about 91.92 percent compared to the day before lockdown. On 31 January, food transportation channels and food supply channels were opened as part of the tight prevention and control situation, and on 3 February, the state established a rapid linkage working mechanism, with active assistance from Hubei’s neighboring provinces to coordinate the transportation of vegetable resources and establish inter-provincial transit stations to ensure food supply and demand balance in Hubei and further stabilize food prices. Therefore, in stage 2 (4 February to 17 March), the Chinese cabbage price in Hubei Province decreased by about 28.19 percent compared to stage 1. Even in the later period (after stage 3 (17 March to 30 April), “dynamic clearance”), the implementation of the COVID-19 prevention policy tightened, and there was no sharp rise in vegetable prices.

To summarize, Chinese cabbage prices in Beijing, Shandong, and Hubei rose significantly in the first stage, but they decreased gradually after the first stage with no change in policies (“dynamic clearance”). Therefore, we can see more clearly from the graphic analysis results that the influence of the policy on the cabbage price is mainly in the form of short-term fluctuations, and then it will gradually decline.

4. Discussion

In terms of the CP results, ESI increased cabbage prices in various provinces to some extent, mainly due to the social panic caused by the epidemic and government economic support [35], which had a greater impact on cabbage consumption and increased its price. With regard to selling areas, GRI and SI had no significant impact on the CP in Beijing and Shanghai, mainly because during the epidemic period, the governments of Beijing and Shanghai strengthened market price monitoring, opened green channels for vegetables to “enter Beijing” and “enter Shanghai”, and the supply of Chinese cabbage was sufficient, which played a key role in stabilizing vegetable prices in Beijing and Shanghai. This ensured the smooth transportation channels of vegetables and improved the price subsidy measures of the supply and marketing chain, which are conducive to stabilizing vegetable prices and improving the resilience of the vegetable supply and marketing system. In producing areas, COVID-19 had less of an impact on the producers themselves, but the COVID-19 outbreak timings of Hebei, Shandong, Beijing, and Shanghai are extremely close, and the implementation of COVID-19 response policies had a certain timeliness, so at the time of the outbreak of the epidemic, there was insufficient short-term supply, increased consumer demand, and increased cabbage prices. Although the NCC caused CP to rise to a certain extent, the government strictly implemented the lockdown policy but also strengthened the province’s social resources supply to the surrounding provinces and cities, especially in Hubei [20], and the implementation of economic subsidies and other policies, which may have led to CP decrease.

With regard to the PP results, in selling areas, the impact of NCC on the PP was different in Beijing and Shanghai, indicating that the epidemic itself had a “rise and fall boost” effect on pork prices in Beijing and Shanghai, and there was uncertainty [36]. In Guangdong, as a coastal province and a large pork transferring province, during the COVID-19 pandemic, the implementation of lockdown measures increased the PP in principle, but Guangdong Province’s full implementation of “point-to-point transportation” and other response policies provided a guarantee for pork supply from other provinces, which could stabilize Guangdong PP to a certain extent. In producing areas, since the outbreak of the epidemic, the provinces have taken timely emergency responses, played the role of the government, mitigated the impact of PP fluctuations, resumed work and production in an orderly manner, and increased input into the government’s meat reserves, which alleviated the price increase pressure of Henan, Shandong, and Hunan. However, because Hubei Province was the first area to break out of the epidemic, the government fully implemented the lockdown policy, and the early epidemic impact led to public panic and purchasing power increases, resulting in a short-term increase in pork prices, but with the implementation of economic support policies such as pork price subsidy policies and government economic subsidy policies, the PP gradually fell.

In terms of staple food prices, the government response policies during the COVID-19 pandemic had a weak impact on daily staple food prices in major food producing areas and main food consuming areas. Since flour and rice are major foods in China and subject to national macro-control, the reserves are large, and even if they are temporarily affected by confirmed COVID-19 cases and prevention policies, they can quickly return to a stable state in the long term. This shows that in the face of external shocks such as the pandemic, the state’s implementation of grain reserve policies and food subsidy policies has played a significant role in stabilizing food prices and protecting food security.

In general, the impact of COVID-19 policy response on perishable food prices and short-shelf-life food prices is more than that on long-shelf-life food prices; this has been confirmed in this paper and previous studies [37]. Because China has long implemented the policy of grain reserves and meat reserves, in the face of the outbreak of the pandemic, grain reserves and meat reserves were put into the market according to the market supply and demand. However, perishable food such as vegetables is not easy to store and transportation was severely hindered during the lockdown period of the pandemic, so there were shortages in the supply of vegetables and the price changes are obvious. It can be seen that government policy regulation plays an important role in stabilizing food prices in the case of public health emergencies. Meanwhile, it is very important to improve the policy regulation of short-term food reserves and increase the policy regulation of perishable food in order to improve the food system emergency capacity in the future. Based on the above research, the following suggestions are made to improve the emergency response capacity of China’s food system and to offer Chinese experience to other countries.

First, when implementing a government economic subsidy policy (ESI), we should pay attention to regional differences, and subsidies can be appropriately increased in serious epidemic areas. At the same time, in the major producing areas and selling areas, we should pay attention to the stimulus consumption brought by government subsidies. This stimulus consumption will lead to an increase in food demand, causing an imbalance between food supply and demand. While implementing the economic subsidy policy, the implementation of a “limited purchase” policy is necessary to eliminate citizens’ purchasing panic and hoarding psychology and to stabilize food prices.

Second, “multi-point” material transport transfer stations should be established. Similarly, point-to-point cross-regional transport stations between major food selling areas and producing areas should be established, such as vegetable supply bases and pork production bases in several major food producing areas. Implementing origin reserves in the urban and suburban areas in the major food producing areas and the “green channel” policy could realize precise connection and transportation in the food producing and selling areas. These measures can not only affect the transportation of production means in the major food producing areas, but can also quickly realize food reserves in the major food producing areas and ensure adequate supplies in the major food selling areas.

Third, form of e-commerce sales should be expanded. There should be active coordination of the connection between relevant e-commerce enterprises and community vegetable shops and retail stores, wechat public accounts or mini programs of regional daily necessity e-commerce services should be established, and e-commerce enterprises should regularly publish the price and production location of daily necessities such as vegetables and meat. In response to emergencies, these vegetable shops or retail stores can launch specials such as “special price vegetable buns” and “special price meat buns” and use online group buying sales and community-oriented distribution services to achieve point-to-point connection to alleviate problems such as labor shortage and meet the needs of daily necessities through multiple channels.

Finally, Beijing, Shanghai, and Guangdong and Hubei, Henan, Shandong, Hebei, and Hunan are food selling areas and food producing areas according to their agricultural development degree and economic development degree. However, this does not constitute a comprehensive analysis and selection of the research area from the perspectives of producers, processing enterprises, and wholesale trade, which is also a limitation of this paper. Further research can comprehensively analyze the impact of COVID-19 policy on food prices from the perspectives of producers, processing companies, wholesale traders, retailers, and consumers.

5. Conclusions

By studying the relationship between daily food prices and policy responses in China’s major food producing areas and major food selling areas with the ARDL-ECM method and graphic analysis, this paper finds that the impact of policy responses on the prices of four foods during the COVID-19 pandemic was mainly in the form of the short-term impact, and the long-term impact results are significant but weak. In the long term, short-term food price changes will soon adjust to equilibrium. However, short-term effects vary by food type and by region. Additionally, in the future, implementing different epidemic prevention policies and ensuring smooth transportation and joint prevention and control between major producing and selling areas are important means to strengthen food security and avoid food crisis during an epidemic. Finally, according to the results of this study, some suggestions are put forward to improve the emergency response capacity of China’s food system and that of other countries to ensure global food security.