Tightness and collectivism are separate constructs, while collectivistic cultures tend to have tighter norms, the correlation is far from perfect.Across 26 nations, collectivism and tightness correlate at r =.49.In other words, 75% of the variation in tightness is separate from collectivism.For example, Brazil and Thailand score high on collectivism but low on tightness , thus the rationale to test the constructs separately.We focus on China’s farming histories as a source of cultural variation.Recent research has found that China has large-scale cultural differences between the north and south that trace back to wheat versus rice farming.Paddy rice farming required twice the labor per hectare as dryland crops like wheat, corn, and millet.To deal with labor demands, rice farmers in southern China formed cooperative labor exchanges.Paddy rice also involved shared irrigation systems and these irrigation systems increased labor demands and forced farmers to coordinate water use and labor for upkeep.There is evidence that the tight inter-reliance in historical rice farming has made parts of southern China more interdependent.In contrast, historically, wheat required less labor and relied mostly on rainfall, which reduced farmers’ need to coordinate.Why would these farming differences be related to pandemic responses?Some researchers have argued that the long-term prevalence of infectious diseases shapes cultural differences.For example, cultures in areas with more disease may be less open to outsiders as a way of defending against people with diseases that are not already present in the community.Because China is such a large country,planting gutter covering climate zones from the tropics to Russia, it has significant variation in climates and disease rates.We tested for the influence of different regions’ experience with diseases by analyzing data on provinces’ SARS cases per population and for the long-run prevalence of flu cases per population.We chose SARS cases because the SARS outbreak captured public attention and thus may have influenced culture.
We chose flu cases as an indicator of more stable differences in respiratory infections across China.Flu cases catch less media attention than SARS, but they affect far more people.If rice farming influences behavior and social coordination, it can potentially explain cultural differences in mask use outside China.By early February, 2020, mask use became widespread in Japan, South Korea, and Vietnam.Newspaper articles in the US and the UK have argued that culture played a role in mask use in East Asia.Thus, it’s possible that countries with histories of rice farming like China, Japan, South Korea, and Vietnam might have been quicker to adopt masks and were thus less affected by COVID-19.However, comparing nations makes it hard to pinpoint the effect of culture.For example, is South Korea’s strong response related to its history of rice farming or the policies Korea put in place after the local MERS outbreak? This is why exploring cultural differences within China is empirically valuable.By looking at differences within a single nation, we can compare people who share the same national government, healthcare system, language family, and other factors.Although this does not eliminate confounding variables, it limits them far more than cross-country comparisons.If rice farming can explain differences within a single country, we may gain insights that could be applied to “messier” cross-country comparisons.The epicenter of the outbreak also provided a rare opportunity to test for regional differences.This is because Wuhan is located in the middle of China.Had the epicenter been in the north or the south, the relationship between proximity and mask use, as well as other cultural factors, would have been harder to gauge.To measure rice farming, we used the percentage of farmland devoted to rice paddies per prefecture.To represent historical rice farming, we used the earliest prefecture-level rice data available from provincial statistical yearbooks.For Study 3, because the search engine data are reported by province, we used provincial rice statistics from the 1996 Statistical Yearbook—the earliest statistical yearbook we could find.To test whether the rice statistics represent historical farming patterns, we compared the statistics to rice data available for a subset of regions from 1914.The 1914 data correlate highly with modern rice statistics.This suggests that the more complete, recent data adequately represent historical rice-farming patterns.Additional analyses found that the results were robust to alternative operationalizations of rice: using provincial rice data instead of prefectural rice data and a simple dichotomous rice-versus-wheat variable.As time progressed, regional governments introduced mask policies.
Mask policies arose first in Wuhan on January 22.Yet even then, many people publicly questioned the effectiveness of masks.At the same time, most reported cases were confined to Wuhan, in Hubei Province.By January 24 , most cities in Hubei were quarantined.Other provincial governments began to introduce their own mask policies afterwards.We measured top-down policies by gathering news reports of official city-level notices requiring residents to wear masks, as well as regulations banning public gatherings and events.Table S8 lists the policy dates and newspaper reports for each observation area.Section S5A describes the data collection in more detail.One plausible alternative explanation to rice theory is that mask shortages determined mask use.Since masks were in short supply, perhaps people in some regions did not wear masks because they could not find one.We investigated this possibility by tracing reports of mask shortages across China.We searched newspaper reports that documented when masks first sold out in different regions.We searched for the word “masks shortage” using Chinese search engines and were able to trace reports and dates for all observation sites.We coded the earliest day a mask shortage was mentioned in the local news.In this approach, we followed the methodology previous researchers used to retrace unfolding events like the 2008 financial crisis and were able to identify that masks were in short supply across all of our observation window.This allows us to explore the possibility of how mask shortages might impact mask use during the early days of the outbreak.As a robustness check, we tested whether rice farming predicted searches for common search terms not related to COVID-19.we ran the same analyses using four of the most popular internet search terms: weather, map, calculator, and translate.This can rule out methodological artifacts.For example, perhaps internet activity was generally higher in rice provinces during this time.If so, rice would predict more searches in general, not just for masks.The results for these common search terms showed no evidence of a spike in rice regions during the initial outbreak.For example, people from rice-farming provinces were no more likely to search for “weather” before the outbreak, in the early days, or in the later days.People from rice provinces did search for “translate” more than people in wheat provinces, but this difference was consistent before, during, and after the emergency declarations.In sum, these results suggest that rice-wheat differences in the early days of the COVID-19 outbreak were not the result of general search differences.
The three complementary studies suggest that rice-wheat differences impacted mask use early in the pandemic.This is partly explained through the mediation of tightness.Rice-farming provinces in China have tighter norms , and people in places with tighter norms wore masks more.Tight norms may have helped pre-modern rice farmers deal with the large labor burdens of rice and coordinate shared irrigation networks.Although this emphasis on social norms emerged for farming, it seems to have helped people in rice regions react faster to the COVID-19 pandemic.However, tight norms explain only a portion of the cultural differences.There are other reasons to think that people from interdependent cultures would wear masks more.For one, research has found that people in interdependent cultures are more focused on risk prevention, whereas people in independent cultures focus more on potential gains and positive outcomes.The data here fits with prior evidence that East Asia is more concerned about virus than people in other parts of the world.A study of the Swine Flu outbreak East Asians reported greater concern about the virus than Westerners, and East Asian air traffic decreased much more severely.A more recent mask observation study found that even with more than 40-days without local COVID- 19, nearly 60% of Shanghai residents still wore masks in public settings , thus pointing to the continual ‘concern’ for others even with no risk of infection.In addition, seeing society through an interdependent lens may make people see the dual purpose of masks: to protect not just the self, but others.Although our studies found little evidence for provincial collectivism measures explaining the differences, it is possible that self-report measures of collectivism do not reflects cultural differences.The fact that cultural differences diminished as the pandemic progressed suggests three theoretical implications: First, cultural differences may help fill in the blanks during ambiguous times.In the early days of the outbreak, the virus was surrounded with unknowns: Is it dangerous? Can it spread from human to human? Is this a true crisis or are people overreacting? It was during this period of uncertainty that cultural differences were the largest.At first, rice areas wore masks.Over time, as cases spread and risk increased, places with looser norms caught up.Culture seemed to help fill in the blanks, before policy, science, and media come into play.There is some evidence that people use stereotypes in the same way.But when people have access to richer, gutter berries individuating information , they rely on stereotypes less or not at all.Second, the results suggest a boundary condition for the influence of culture.Policy efforts can override cultural differences.
While regions responded differently at the beginning of the outbreak, they converged as awareness spread and the government enforced mask policies.This finding can help policymakers understand when cultural differences are likely to influence human behavior and when external circumstances will override initial differences.Third, the data are also consistent with the idea that people do not respond strictly to objective risk factors when they decide how to respond to a public health crisis.For example, death rates were higher for older people, yet they were less likely to wear masks.In addition, COVID-19 cases were less common in places farther from Wuhan.People in wheat farming regions responded to that distance—more people wore masks in places closer to Wuhan, and fewer people wore masks in places farther from Wuhan.Yet people in historically rice-farming regions wore masks at the same rate regardless of distance, local COVID-19 cases, and history of infectious diseases.In sum, ground data from China during the early days of COVID-19 revealed some behaviors that fit with risk calculations and some that did not.It is rational to expect that more people would wear masks in places with more cases, places closer to the epicenter, and places with denser populations.It is also rational to expect that mask use would approach 100% as the outbreak spread and local governments required masks.Yet objective risk factors explained only a portion of human behavior.Non-obvious cultural factors also predicted whether people wore masks, how early people started wearing masks, and whether people searched for masks online.Although COVID-19 was only discovered in 2019, people’s reactions mapped onto their long-run cultural histories.The definition and status of smart farming, sometimes referred to as digital farming , varies from country to country.Smart farming solutions apply information and technologies to increase the economic yield of crop and livestock production, and to optimize farming inputs and processes that extend to the transportation, distribution, and retail phases of the food supply chain.These technologies rely on Big Data Analytics and include cyber systems that afford monitoring, smart predictions, decision support, automated control and future planning.Although there are many definitions for smart farming, the main conceptual elements found in the literature are similar and include combining Big Data Analytics and information communication technologies such as the Internet of Things , and Edge and Cloud computing with farm equipment, GIS technology, robotics, satellite images, unmanned aerial vehicles and algorithms to accomplish farming practices innovatively and efficiently.In addition, smart farms are expected to optimize food production by improving the application of nutrients to the soil, reducing the use of pesticides and water consumption in irrigation.Precision agriculture, precision irrigation and AgInformatic systems are prime examples of current technologies that could integrate ICT for optimizing farm inputs.Yet, the smart farming concept was meant to be more holistic and include frameworks for establishing optimal farm processes, networking of on-farm systems , monitoring the distribution of farm products and marketing food commodities.Finger et al.predicts that smart farming solutions could narrow the productivity gap between developing and industrial countries.