key: cord-0900864-s55aimfa
authors: Ritzel, Christian; Ammann, Jeanine; Mack, Gabriele; El Benni, Nadja
title: Determinants of the decision to build up excessive food stocks in the COVID-19 crisis
date: 2022-05-26
journal: Appetite
DOI: 10.1016/j.appet.2022.106089
sha: 83160e258df30d581f5921a8437b363448ff8c2b
doc_id: 900864
cord_uid: s55aimfa

In 2020, the first COVID-19 lockdowns resulted in food panic buying and excessive food stockpiling across many countries around the world. Many governments recommend keeping emergency food stocks for three to ten days for times of potential shortages in food supply. Based on data from an online survey conducted among Swiss inhabitants, we investigated the effect of knowledge level and stockpiling behaviour according to governmental stockpiling recommendations in normal times on the decision to build up more food stocks than usual during the first lockdown in 2020. For this purpose, we applied a combination of latent class analysis and logistic regression. Latent classes were constructed based on knowledge level and stockpiling behaviour according to governmental stockpiling recommendations in normal times. Subsequently, the information on class membership was used as predictor of the decision to excessively stockpile food during the first lockdown. The variable “class membership” revealed that respondents with a low knowledge level and food stocks below governmental recommendations in normal times had a 7.6 percentage points lower probability of excessively stockpiling food during the first lockdown than respondents with a high knowledge level and recommended food stocks in normal times. Excessive stockpiling was additionally driven by the worry that certain food products would disappear from the supermarket shelves entirely or would be in short supply. Moreover, regression results revealed that respondents who reduced their shopping frequency during the first lockdown in 2020 showed a higher probability of building up more food stocks than usual. Our findings are crucial for food suppliers and policymakers to understand the drivers of panic buying and to prevent this phenomenon in future crises.

observed not only for non-perishable food but also, in smaller amounts, for fresh fruits and 54 vegetables. Observed food panic buying and excessive stockpiling during the first lockdown in 55 2020 must be considered irrational behaviour (Schiller et al., 2021) . Interestingly, a variety of 56 studies identified the anticipation of food shortages or fear of potential future food unavailability, 57 respectively as a psychological driver of panic buying and excessive food stockpiling (Ammann 58 et al. (2011) question this assumption. The authors found that environmental knowledge had 129 no effect on energy conservation, and alcohol knowledge was unrelated to drinking behaviour. 130

Only between knowledge and pro-Muslim behaviour was a positive correlation obtained. 131

Empirical evidence implies that people who are aware of the governmental stockpiling 132 recommendations in normal times might not behave in accordance with governmental 133 stockpiling recommendations in a crisis. 134 Benker (2021) described the motivation behind stockpiling behaviour as the minimisation of 135 risk of access loss due to the belief of short supply. Accordingly, stockpiling must be considered 136 fear-based behaviour rather than a rational response to factual existing food shortage (Benker, 137 2021) . A crisis such as the COVID-19 pandemic can lead to excessive stockpiling and food 138 hoarding. Excessive stockpiling behaviour in a crisis is explained by psychological reactions 139 such as panic disorder, anxiety, depression, fear, and uncertainty about the future (Lehberger 140 et al., 2021) . If stockpiling food and beverages according to governmental recommendations 141 is frequently exhibited in normal times due to fear of a sudden crisis, it can also be considered 142 a habitual behaviour, which might be predictive of stockpiling behaviour when a crisis emerges 143 (Francklin, 2013; Ouellette & Wood, 1998) . 144

Against this background, our methodological approach allowed us to test the following 145 hypothesis regarding knowledge level and stockpiling behaviour (stockpiling habits) according 146 to governmental recommendations in normal times: 147 H1: People who were mostly aware of the governmental recommendations, and who frequently 148 (habitually) stockpiled food and beverages according to the recommendations in normal times, 149 would behave in even more fearsome ways and tend to excessively stockpile food during the 150 first COVID-19 lockdown. In contrast, people who mostly did not know about governmental 151 stockpiling recommendations and did not tend to stockpile in normal times would stay calm 152 and thus not tend to excessively stockpile during the first lockdown. 153

By using an LCA, we were able to identify different classes of people with regard to knowledge 154 of and stockpiling behaviour according to governmental stockpiling recommendations in 155 normal times. The variable "class membership" is included in the regression analysis as an 156 independent variable. Regarding the effect of further sociodemographic and non-H2 is considered because even today, women are the primary food purchasers for the 162 household (Crane et al., 2019). It is hardly surprising that women were more likely to build up 163 larger scale food reserves after the outbreak of COVID-19 than men were . 164

Food distribution and access differ depending on where people live (i.e., urban vs. rural area) 165 (Smith & Morton, 2009 In general, an increase in household size is associated with purchasing larger amounts of food 175 (Ricciuto et al., 2006) . In the context of the COVID-19 pandemic, the findings of Nam et al. 176

(2021) reveal that larger households tended to excessively stockpile necessities compared 177 with smaller households. We hypothesised that the same holds true for food stockpiling 178 behaviour (Hypothesis 4): 179 H4: Larger households built up more food stocks than usual during the first lockdown in 2020 180 than smaller households did. 181

Previous studies on emergency stockpiling in preparation for natural disasters (i.e., earthquake 182 or hurricane) found that high-income households are more likely to stockpile food and drinking 183 people. 196

The Italian-and French-speaking regions of Switzerland were initially more affected by 19 than the German-speaking regions (SWI swissinfo.ch, 2020 Reducing the frequency of shopping trips was considered a strategy to reduce the risk of 233 getting infected with the coronavirus. In this context, Lehberger et al. (2021) found that that 234 reducing shopping frequency was a major reason for stockpiling. Therefore, we tested 235

Hypothesis 11: 236 H11: People who reduced their shopping frequency were more likely to stockpile food 237 excessively during the first Swiss lockdown than people who maintained their shopping 238 frequency. 239 imbalances regarding the frequency distribution of the age groups existed. However, the 257 variables "age" and "age group" were not considered in the empirical analysis. can be found in Table 5 in the appendix. Frequency distributions for variables used in the 268 regression analysis including answer options with values of 98 and 99 can be found in Table  269 6 in the appendix. 270

For the LCA, we used three nominal-scaled items shown in Table 1 

For the logistic regression, we used the variables presented in Table 2 . The (binary) dependent 281 variable took the value of 1 if a respondent built up more food stocks than usual during the first 282 lockdown in 2020 and 0 otherwise. As independent variables, we used sociodemographic 283 characteristics, three variables related to food and beverage purchasing behaviour (utilisation 284 J o u r n a l P r e -p r o o f psychological factor (worried about not finding food). 286 287 

For the empirical analysis, we used a combination of (1) LCA and (2) In the first step, we applied an LCA. A latent variable or a latent phenomenon is a random 296 variable that cannot be directly overserved. Its value can be inferred from observed items by 297 means of a mathematical model. Individual preferences, behaviours, and attitudes are latent 298 variables that can be conceptualized (or measured) as categorical or continuous items (Porcu 299 & Giambona, 2016) . For instance, burnout is a latent phenomenon which can be measured 300 based on the Maslach Burnout Inventory. The Maslach Burnout Inventory is composed of 22 301 items with a scale ranging from 0 = never to 6 = every day. By means of an LCA the following 302 three (latent) burnout groups can be identified: high burnout, moderate burnout and low 303 burnout (Méndez et al., 2020) .Latent or unobserved variables in our analysis were the state of 304 knowledge, the self-reported food and beverage stockpiling behaviour, the number of latent 305 classes, and their share within the total population. The latent classes were constructed based 306 on the (observed) three nominal-scaled items described in Table 1 . Consequently, the main 307 aim of an LCA is to divide the total population into clearly definable and relatively homogeneous 308 classes (Lanza et al., 2007) . 309

Instead of applying an LCA, the three nominal-scaled items could be individually integrated 310 into the logistic regression as independent variables predicting excessive food stockpiling. 311

Even though variable-level methods such as ordinary regression analysis provide valuable 312 explanations for social phenomena, such methods hide differences between subpopulations 313 by providing findings and conclusions representative for the overall sample. Capturing 314 (unobserved) differences between subpopulations is an important aim of social research. In 315 contrast to variable-level methods, LCA is a person-oriented method which allows for 316 modelling distinct variants of (unobserved) heterogeneity within the overall sample. Therefore, 317

we use LCA because it condenses the information of the three overserved items into one single 318 variable called "class membership". From a technical perspective, integrating the three items 319

individually into the logistic regression would reduce the degrees of freedom. Furthermore, and 320 The optimal class solution (optimal number of latent classes) can be obtained by using 332 comparative model fit criteria such as the Bayesian information criterion (BIC), Akaike 333 information criterion (AIC), and log-likelihood (LL) (Goodman, 2002) . Each (latent) class was 334 described with a meaningful label. 335

In the second step, we estimated a logistic regression (Backhaus et al., 2005) , whereby the 336 dependent variable represented a binary variable that took the value of 1 if a respondent built 337 up more food stocks than usual during the first lockdown in 2020 and 0 otherwise (Table 2) . 338

Our independent variable of interest, the observed "class membership" (i.e., Class 1, Class 2, 339 and Class 3) of a respondent, was used as a predictor of the binary decision. Additionally, we factors of the respondents (Table 2 ) as further independent variables in our regression model. 342

Initially, an average variance inflation factor (VIF) of 4.4 for all independent variables indicated 343 that multicollinearity was not an issue. However, the VIF for the independent variable "age" 344 was 15.6, which is above the tolerated value of 10. Likewise, the alternative (ordinal-scaled) 345 variable "age group" had a high VIF of 9.9. To avoid inconsistent estimates, neither variable 346 was considered in the regression model (note that both variables were non-significant when 347 included in the regression model). We explored the following classes regarding knowledge level and stockpiling behaviour 362 according to the governmental recommendations in normal times. 363

The first was a class consisting of people who were mostly aware of the governmental 364 recommendations and who frequently (habitually) stockpiled food and beverages according to 365 the recommendations in normal times. 366

The second was a class consisting of people who were mostly not aware of the governmental 367 recommendations and who mostly did not stockpile food and beverages according to the 368 recommendations in normal times. 369 J o u r n a l P r e -p r o o f normal times between these two contrasting classes. 371

In the following, we describe in detail the three latent classes regarding knowledge level and 372 stockpiling behaviour in normal times, as well as their frequency within the total population. 373

Class 1: "Informed and always following stockpiling recommendations in normal times" 375 Regarding our variable of interest, "class membership," the regression results reveal that the 446 fear-based stockpiling behaviour in normal times was reinforced during the COVID-19 447 lockdown when supermarket shelves for certain products were temporarily empty. In this 448 context, findings indicate that respondents belonging to Class 3, "Uninformed and rarely 449 following stockpiling recommendations in normal times," showed a 7.6 percentage points lower 450 probability of building up excessive food stocks during the first lockdown in 2020 than 451 respondents belonging to Class 1, "Informed and always following stockpiling 452 recommendations in normal times." This result suggests that even though Class 1 exhibited 453 the highest knowledge level, and stocks were always in line with the governmental 454 recommendations in normal times, respondents from this class were likely responsible for the 455 who found a positive relationship between education and excessive food stockpiling in a crisis. 469 Therefore, we must reject H6. 470

People living in the Italian-and French-speaking regions of Switzerland showed a higher 471 likelihood of excessively stockpiling food compared with people form the German-speaking 472 region. However, the positive effect was not statistically significantly different from zero. Thus, 473

we must reject H7. 474 Surprisingly, the positive effect of the variable "risk group" was statistically non-significant. 475 Schmidt et al.'s (2021) findings likewise indicate that belonging to a risk group was not a 476 significant predictor of change in purchasing quantity. Consequently, we must reject H8. 477

Stockpiling above the usual level seems to be motivated by concerns that certain foods were 478 temporarily not available during the first lockdown in 2020. For instance, people who were not 479 worried that they would no longer be able to buy certain foods showed a 22.6 percentage 480 points lower probability of building up unusually high stocks than those who were very worried. Even though home delivery and take-away sales have increased since the beginning of the 486 pandemic, neither variable had a significant influence on the decision to build up more food 487 stocks than usual during the lockdown. Consequently, H10 must be rejected. 488

The regression results furthermore indicate that respondents with a lower shopping frequency 489

Panic buying and excessive stockpiling behaviour due to the first lockdown of economies 503 caused by the COVID-19 pandemic was a globally observed phenomenon. Understanding the 504 underlying drivers is crucial for policymakers in affected countries to ensure food security and 505 thereby prevent panic among the population, especially in the beginning of a future crisis. A 506 large body of literature has already investigated the sociodemographic and psychological 507 determinants of food stockpiling behaviour during the COVID-19 lockdown. Our study provides 508 an additional contribution by exclusively addressing the relationship between governmental 509 food and beverage stockpiling recommendations and the decision to build up more food stocks 510 than usual during the first Swiss lockdown in 2020. Although the French-and Italian-speaking 511 regions are overrepresented in our sample, we believe that our findings reflect the general 512 tendency regarding excessive food stockpiling of Swiss inhabitants during the first lockdown. 513

By applying an LCA, we were able to depict the heterogeneity within the Swiss population 514 regarding food and beverage stockpiling knowledge level and behaviour according to the 515 governmental recommendations in normal times. Compared with Class 2, "Uninformed and 516 mostly following stockpiling recommendations in normal times," and Class 3, "Uninformed and 517 rarely following stockpiling recommendations in normal times," Class 1, "Informed and always 518 following stockpiling recommendations in normal times," had the highest knowledge level. 519

Respondents belonging to Class 1 always had food and beverage stocks according to the 520 governmental recommendations in normal times. In contrast, respondents belonging to Class 521 3 had the lowest knowledge level, and their food and beverages stocks were below the 522 governmental recommendations in normal times. Furthermore, the LCA results show that with 523 a share of 51% of the total population, Class 3 was the largest class. Against the background 524 of future crises such as the climate crisis, which could at least temporarily influence the food 525 supply negatively, the results of the LCA highlight the importance of raising awareness among 526 the Swiss population to build up stocks according to governmental recommendations. 527

However, the results of the logistic regression indicate that compared with Class 1, "Informed 528 and always following stockpiling recommendations of the government in normal times," Class 529 3, "Uninformed and rarely following stockpiling recommendation by the government in normal 530 times," showed a lower probability of building up more food stocks than usual during the first 531 lockdown in 2020. This finding implies that respondents belonging to Class 1 acted in a crisis 532 such as the COVID-19 lockdown contrary to their knowledge and to their stockpiling habits in 533 normal times. The fear-based stockpiling behaviour of Class 1 was reinforced during the first 534 lockdown, causing food stockpiling to occur above the recommended level. Therefore, Class 535 1 might be at least partially responsible for the phenomenon of panic buying and the resulting 536 empty supermarket shelves. However, our findings do not imply that governmental stockpiling for an emergency situation, such as a one-week power outage, is still necessary and important. 539

Governmental information on stockpiling, especially targeting people who do not stockpile in 540 normal times, is therefore indispensable. 541

The regression results further reveal that sociodemographic characteristics were only partly 542 able to explain the decision of whether to stockpile food to a greater extent than usual during 543 the first lockdown in 2020. Excessive stockpiling seems additionally to have been driven by 544 psychological and purchasing-related factors. In particular, respondents who were very worried 545 that certain food products would disappear off supermarket shelves entirely or would be in 546 short supply stockpiled more food than usual, more so than respondents who were not worried. 547

Moreover, respondents who decreased their shopping frequency during the first lockdown in 548 2020 showed a higher probability of building up more food stocks than usual as compared with 549 respondents who maintained their shopping frequency. 550

In Switzerland and in many other developed countries, the food supply was at no time in danger 551 during the first lockdown. Therefore, excessive food stockpiling can be considered an irrational 552 and fear-based behaviour. Against this background, our findings highlight the necessity of 553 intensified communication by policymakers and retailers, especially at the beginning of a crisis, 554 to prevent panic buying of food and excessive stockpiling. In particular, communication in the 555 beginning of a crisis should emphasise that stockpiling food and beverages according to 556 governmental recommendations is sufficient. In this context, communication should 557 additionally highlight that panic buying is an irrational and fear-based behaviour that should be 558 avoided because it temporarily jeopardises food supply chains and food availability. 

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