Introduction

Decision making has always been an important role in human life, especially in economics. In the market of oligopoly competition, a few companies set the price interdependent from each other, competing using the game theory. Each decision they make on the price determines their gains and losses. In economics, people serve as rational agents who make decisions that maximize their utility; however, this is not normally the case. Individuals’ decisions are affected by various factors (e.g. personality, educational background and also psychological state characteristics such as mood), and people do not always choose the profit-maximizing decision. The present study examines the relationship between personality and economic decision making. 

Personality is closely associated with behaviors, cognition, and emotions. Assessing personality can be used to predict behavior tendencies. One way to measure personality is by using the Big Five personality test. The Big Five personality traits includes conscientiousness (disorganized / impulse vs. careful / disciplined), agreeableness (ruthless / suspicious vs. soft-hearting / trusting), neuroticism (calm / secure / self-satisfied vs. anxious / insecure / self-pitying), openness (practical / conforming vs. imaginative / independent), and extraversion (retiring / reserved vs. sociable / affectionate) (McCrae & Costa, 2008). The Big Five personality factors are relatively stable in adulthood and appear to be present in every culture. It also clearly depicts the whole picture of individual’s traits and as a result, studies mostly apply the Big Five personality test for predicting behaviors. 

The present study employed the Dark Triad personality measure as a form of personality assessment. The Dark Triad is a measure of malevolent personality developed by Paulhus and Williams (2002). People who score high on the Dark triad are more likely to commit crime and cause social distress. The three dimensions of the dark triad are Machiavellianism, Narcissism, and Psychopathy. Narcissism stands for strong ego, greediness, and a sense of superiority and dominance. Machiavellianism stands for the manipulative, calculating, and amoral personality and people with high levels of Machiavellianism typically focus on self-interest and self-gain. Psychopathy, on the other hand, is marked by cruelness, impulsivity, and anti-social behaviors. The Dark triad, in general, is related to antisocial behaviors, such as aggression and violence, and emotional deficit, such as a lack of empathy and theory of mind (Paulhus & Williams, 2002). It is also related to poor well-being such as loneliness and anxiety. People who score high on the Dark triad are more likely to score low on agreeableness in the Big 5 personality. 

Personality has been linked with cognitive functioning, such as attention, reaction time, but also decision making. One study found that personality measured using the Big 5 questionnaire influences decision-making during the Ultimatum Game, which measures the willingness to share (Fiori et al., 2013). In the Ultimatum Game, the first participant was asked to divide 10 dollars with the second participant. If the second participant rejects, then neither can get the money. According to the theory of rational agent, the proposer will always offer the smallest amount, and the receiver will always accept. However, the experiment findings do not agree with the prediction. It was found that participants who scored higher on conscientiousness and lower on extroversion were more likely to accept the offer in the game (Fiori et al., 2013). Individuals with high neuroticism seem to have decreased willingness to take risk. In another study, participants were asked to choose between a lottery and a certain amount, six times. The lottery is always the same but the fixed payment varies in each choice. The task was repeated four times. For the first two times, the lottery is positive, and the lottery can incur a loss of $1 for the third time and $5 for the last time. The researchers would record the number of times participants choose the lottery over a fixed payment and associated these with the person’s personality (Anderson et al., 2011). In the experiment of sequential prisoner’s Dilemma, two players each had $5, and the first player decided whether to send $5 or $0 to the second player. The amount would double after the transfer. Then the second player decides to transfer back a number between $0 and $5. The researchers recorded the money transfers and found that agreeableness increases the fraction of first moves transferring $5 and increases also the average amount transferring back. Cognitive skills and IQ also influenced the result (Anderson et al., 2011). Another study found that individuals with Dark Triad personality traits were more likely to engage in opportunistic decision-making, which is the actions assessed toward an immediate circumstance in favor of the individual or company (D’Souza & Lima, 2015). In business, people with Narcissism are visionary; they seek superior position to influence over others and make decisions that are beneficial to their reputation. On the other hand, narcissistic people are also innovative with prominent leadership with the ability to lead their company to glory. Evidence has shown that most managers and people in executive positions in a corporation score low in Psychopathy; they are not self-centered, cruel, or cynical, but can contribute to the longevity and gains of the companies. People with high levels of Machiavellianism seek management and leadership positions, which allow them to manage, control and manipulate people inferior to them. They focus on their personal power and can easily influence others (D’Souza & Lima, 2015). 

Most previous research has been done on the Big 5 personality measure in relation to decision-making, including willingness to share and take risks and the game theory, while the relationship between the Dark Triad and various decision-making games has not been assessed in detail. Moreover, previous research mostly focuses on adults rather than younger population groups. The present study aims to explore the relationship between the Dark Triad personality and economic decision-making, specifically, the wiliness to take risk, in a female sample of high school students. It was hypothesised that the Dark Triad personality traits would be associated with high school students’ decision in risks-taking contexts. 

Method

Participants and sampling

Participants in the present study were female students from an all-girl high school in Virginia (US), with the age range from 14 to 20. Convenience sampling method was used: the questionnaire was sent to the school email, so every student in the high school had access to the questionnaire and could fill it out voluntarily. It was attempted to obtain a homogenous sample, with all participants being females with similar age range and similar education level. 

Design

This study was based on a non-experimental cross-sectional design. The dependent variable was decision-making/ risks-taking and the independent variable was personality based on the Dark Triad. 

Measures

Personality was measured by the short Dark Triad questionnaire (Jones & Paulhus, 2014). It contains 27 questions: 9 questions measured Machiavellianism (e.g. “I like to use clever manipulation to get my way”), 9 questions measured narcissism (e.g. “People see me as a natural leader”), and 9 questions measured psychopathy (e.g. “I like to get revenge on authorities”). Each item was rated on a 5-point Likert-type scale (strongly disagree-strongly agree; Jones & Paulhus, 2014). Sum scores for each of the sub-scales and the overall global scales were computed and higher scores indicated higher attributes of the respective trait.

Decision-making was measured by Iowa Gambling Task on the online simulation on Psytoolkit (Stoet, 2010; Stoet, 2017). Participants would be given $2,000 at the start and asked to choose from one of the four card decks (A, B, C, and D) for 50 trials. Deck A and B yield $100 each time and deck C and D yield $50. Each time a participant chooses a deck of card, there is a 50% chance to get a penalty. The penalty is $250 for choosing deck A and/or B and $50 for choosing deck C and/or D. Participants need to gain as much money as possible. Without knowing how much each deck of card yields and the amount of penalty it incurs, participants would have to make a decision each time. The online experiment recorded participants’ reaction times (used to determine cognitive skills), their choice, whether there was a penalty and money they gained and lost. This study focused on how many times participants chose deck A/B versus deck C/D to determine the willingness to take risk: the more deck A/B was chosen, the more willing the individual to take risk. An index was calculated to determine decision-making based on A/B choices divided by C/D choices and values >1 indicate risk taking and <1 indicate risk aversion.

Further, math ability was assessed with rating from 1 to 5 (very low – very high); math liking was assessed as 3 ratings (Yes/No/Neutral). Students reported whether they have studied Psychology or Economics (Yes/No) and also gave their mood ratings on a 3-scale (Low or negative mood/Neutral mood/High or positive mood).

Statistical analyses

All data analyses were performed on SPSS (IBM, version 25). Normality tests were performed on the outcome measure and revealed that decision-making was highly positively skewed (Skewness: 4.00 and Kurtosis: 17.70) and the Shapiro-Wilk test (p<.001) suggested violation of normality. Log-transformation (ln method) was applied and normalised the distribution (Skewness: 0.03 and Kurtosis: 2.70; Shapiro-Wilk test, p=.080) and hence the log-transformed data was used with parametric testing. Robustness checks were conducted prior to analyses. To explore whether individuals who dropped out prior to the DM task differed in personality from individuals who completed the survey three independent-sample t-tests were conducted. Findings showed that the three sub-scales Machiavellianism, Narcissism and Psychopathy were not statistically significantly different between the non-completers and the completers (all p’s >.410), suggesting that the final sample seems to be representative of the whole sample that took part. 

Results

Descriptive 

Table 1 depicts the characteristics of the sample. The sample was aged on average 16.12 years (SD=1.30). The average maths ability was 3.31 (SD=0.84). Most students had not studied Economics (80.8%) or Psychology (80.8%). 

From the personality traits, the average score on Machiavellianism was 3.27 (SD=0.59); the average score on Narcissism was 2.77 (SD=0.60) and the average score on psychopathy was 2.15 (SD=0.70). The global score of the three traits was 8.19 (SD=0.87). 

The mean score of decision-making after applying log-transformation was 0.09 (SD=0.87), suggesting that participants on average were more risk-taking.

The effect of personality on decision making

Bivariate Pearson’s correlation analyses were conducted and showed that Machiavellianism was not correlated with DM, r=.217, p=.286. Narcissism was also not related to DM, r=-.214, p=.293. Psychopathy was also not related to DM, r=.082, p=.690. Further, the global score was also not related to DM, r=.035, p=.865. This suggests that the three personality traits were not related to DM. The other study variables, i.e. maths levels, maths liking and mood did not associate with DM either (all p’s >.392).

A multiple linear regression model was run to explore whether there is an effect of personality on DM, when controlling for the other potential confounding variables (i.e. maths levels, maths liking and mood) and the findings revealed that none of the variables predicted DM, F(6,19)=.507, p=.796. 

Table 1. Study variables for the full sample.

Discussion 

The present study aimed to explore the relationship between personality and decision-making in the context of risk taking. The study employed young females as a sample. It was hypothesised that Dark Triad personality traits (Machiavellianism, Narcissism, and Psychopathy) would influence decision-making on risk taking. Decision making was measured with the Iowa Gambling task which assesses the risk taking vs the risk aversive behaviour in terms of gaining financial rewards and paying penalties for wrong decisions. The findings revealed that there were no relationships between Dark Triad personality traits and risky decision-making in the present sample. 

The results are different from previous findings, which have consistently found that the Dark Triad personality traits are influencing decision-making. One previous study indicates that people with the Dark Triad personality traits (indicating higher levels of Machiavellianism, higher levels of Narcissism, and higher levels of Psychopathy) are more likely to make decisions that are in favour of themselves (D’Souza & Lima, 2015). However, the present study shows no relationship between these personality traits and decisions that make them better off (i.e. gaining more money and paying lower penalties). One explanation for the null findings is that the sample had relatively low scores within each of the personality sub-scales and low variability in the sample in those personality traits reduces statistical power and hence impedes potential findings. 

Previous studies focus on adults, while the present study has been conducted using high school students, a sample in which potentially financial risk taking behaviour is rather low (as they experience these risk taking behaviour in daily life to a smaller extent). A previous study had shown that personality influences acceptors’ decisions in the Ultimate Game but not proposers’ decisions, and more honest people gained more and more introverted people accepted more often. The study was conducted on a sample aged 18 – 44 (Fiori et al., 2013), suggesting that these relationships might occur in a wider age range, including adults but not in younger samples. One study of 91 undergraduate students (aged 18-28) from Ohio University consisting of 90% Caucasian explored the Iowa Decision-making Task and found a relationship between personality and deck selection, however, personality traits and mood have been shown to impact the performance of the participants (Buelow & Suhr, 2013). The BIS/BAS scale was used for measuring personality and also mood measurements were implemented. The results show that participants with high level of drive and impulsivity tend to focus on short-run gain by choosing more from deck A and B. It also found that individuals with high negative mood selected more Deck B than Deck C (Buelow & Suhr, 2013). However, the present study showed no impact of mood on decision making, which might attribute to the low variability in mood. 

One limitation of the study was the sample. The sample was small and only included female students. If the study included both females and males, the result might have been different. The sampling was done in only one high school due to inaccessibility to other high schools in other locations. This led to sample bias as the sample of female students could not represent the whole population of this age group. Moreover, the sampling method might have led to a sample bias; the questionnaire was sent through the school email, and might have neglected those who do not frequently check their email. 

Second, the presentation of the Iowa Decision Making Task in the questionnaire is a limitation of the study. The data collected showed that some students randomly choose the card decks (A, B, C, D) or choose them in a pattern, which might be attributed to their misunderstanding of the Iowa Decision Making Task or to their impatience in the whole study data collection period. The study was observational and could not control for many confounding variables including the participants’ environment that might influence their performance in the task. Some students might perceive the whole decision-making game as too long so they randomly choose the deck instead of trying to gain more money. The introduction of the game might not be conspicuous or clear enough for some students to pay close attention to and understand the game. The Iowa Decision Making Task was not the first choice for measuring decision-making and the initial intention was to measure economic decision-making. However, due to inaccessibility of online simulation for other economic games and time limitation, the Iowa Decision Making Task was chosen. The task could not measure specifically economic decision making and is largely used to investigate participants’ cognitive skills and mental health (Bechara et al., 1994). The 50%-chance payment on deck A and B is $250 while money gained is $100 and payment on deck C and D is $50 while money gained is $50, so clearly the better way to ensure gaining money is to always choose deck C and D. Moreover, the participants were untold about the amount of payment, so they needed to figure this out by themselves from the first few trials they engaged in (Bechara et al., 1994). As a result, the Iowa Decision Making task might not be the best measurement for risk-taking in this context. One cross-section study has shown that as age increases, people are more likely to choose deck C and D to ensure money gained. Childred and adolescents are more likely to choose risky deck A and B (Buelow & Suhr, 2009). The present study agrees with these findings as the study showed that young participants are more willing to take risk. Some previous studies have used different versions of the Iowa Decision Making task specifically adapted to a younger adult sample, which might be more useful to explore their decision making behaviour (Buelow & Suhr, 2009).

Future research should aim to explore the Dark Triad personality traits in relation to economic decision-making. Economic decision-making would be measured for example with the prisoner’s dilemma or the Ultimatum game, which were tasks used in various previous research regarding decision-making, and these studies found correlations between the Big Five personality traits and economic decision-making (with e.g. higher levels of neuroticism relating to lower risk taking). If risk-taking is to be measured, the study could apply the lottery game utilized in the previous study (Anderson et al., 2011). The lottery game is designed to incur gains or losses in different trials, with the amount of incurred losses varying in different trials, which makes it a better for measuring risk-taking as it considers more aspects (Anderson et al., 2011), while the Iowa Decision Making Task only incurs fixed losses. The sample should include both female and male high school students in a larger range of locations and also a wider age width. The study could take place physically instead of providing questionnaires/ tasks online, in order to control some external factors that might influence participants’ performance. 

The study investigated the relationship between personality and decision making. The study was conducted in high school female students with the use of an online questionnaire and a decision making task. Personality was measured by the Dark Triad personality test by Paulhus and Williams (2002) and decision-making in a risk-taking context was measured with the online simulation Iowa Decision Making Task (Stoet, 2010; Stoet, 2017). The results showed no relationships between the Dark Triad personality traits (Machiavellianism, Narcissism, and Psychopathy) and decision making in female high school students, which provides no support for the present hypothesis that Dark Triad personality traits would influence decision-making. Other variables that were assessed in the present study included math-liking, math-ability, previous knowledge, and mood also no relationships emerged between these and decision-making. The study is subject to several limitations, which include the sample and the choice of the decision-making task. Future research should apply different economic games (e.g. the prisoner’s dilemma or the Ultimatum Game) and should be physically conducted on a larger sample in order to provide more reliable findings. 

References

Mentor: Dr. Bianca Serwinski, Northeastern University

Anderson, J., Burks, S., DeYoung, C., & Rustichini, A. (2011, January). Toward the integration of personality theory and decision theory in the explanation of economic behavior. In Unpublished manuscript. Presented at the IZA workshop: Cognitive and non-cognitive skills. 

Bechara, A., Damasio A.R., Damasio H., Anderson S.W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7-15.

Buelow, M. T., & Suhr, J. A. (2009). Construct validity of the Iowa gambling task. Neuropsychology review, 19(1), 102-114.

Buelow, M. T., & Suhr, J. A. (2013). Personality characteristics and state mood influence individual deck selections on the Iowa Gambling Task. Personality and Individual Differences, 54(5), 593-597. 

D’Souza, M., & Lima, G. A. S. F. D. (2015). The dark side of power: the dark triad in opportunistic decision-making. Advances in Scientific and Applied Accounting, São Paulo, 8(2), 135-156. 

Fiori, M., Lintas, A., Mesrobian, S., & Villa, A. E. (2013). Effect of emotion and personality on deviation from purely rational decision-making. In Decision making and imperfection (pp. 129-161). Springer, Berlin, Heidelberg. 

Jones, D. N., & Paulhus, D. L. (2014). Introducing the Short Dark Triad (SD3): A brief measure of dark personality traits. Assessment, 21, 28-41.

McCrae, R. R., & Costa, P. T., Jr. (2008). The Five-Factor Theory of personality. In O. P. John, R. W., Robins, & L. A. Pervin (eds.), Handbook of personality: Theory and research (3rd. ed.). New York: Guilford.

Stoet, G. (2010). PsyToolkit – A software package for programming psychological experiments using Linux. Behavior Research Methods, 42(4), 1096-1104.

Stoet, G. (2017). PsyToolkit: A novel web-based method for running online questionnaires and reaction-time experiments. Teaching of Psychology, 44(1), 24-31.

About the author

Sijia Dong

The post The Relationship Between the Dark Triad Personality Traits and Decision Making appeared first on Exploratio Journal.

I. Introduction

According to World Health Organization or WHO’s description, Coronavirus is “a large family of viruses which may cause illness in animals or humans” (Source 21). As is well-known now, the whole world has been impacted by this novel virus, COVID-19, that started in Wuhan, China in December, 2019. This has spread fear understandably but it also has had a real impact on people’s lives both socially and economically and has endangered public health. In particular, low income workers have been affected adversely and the purpose of this paper is to describe and analyze the impact on such workers in China.

In Asia, such as Korea, the first firmed case was a 35 years old woman came from Wuhan in January 20th. In the same week, three more cases occurred as travelers from Wuhan tested positive in the airport. There was the first confirmed local case in Korea in January 30, a man who ate a meal with the confirmed case 3. On February 18, a 61 years old woman tested positive for COVID-19 at Shincheonji Church of Jesus in Daegu. Within the next few days, 15 more people were confired with covid-19, and one month later, thousands of people who had the connections to the church were confirmed as well. In March 25th, the Shincheonji Church accounted for 5,080 confirmed cases. Until April 17th, there’re 10,635 cases confirmed (Source 1). On April 16, Prime minister Shinzo Abe in Japan declared nationwide state of emergency due to the worsening situation in the country (Source 6), and it will remain in force until May 6. In addition, the Olympic Games Tokyo 2020 was postponed to July 23, 2021, due to the outbreak of COVID-19 (Source 7).

In Southeast Asia, Thailand had the first confirmed case outside of China in January 13th, but the case remained under 100 for nearly two months. However, cases increased from 82 on March 14th to 721 on March 23, and the majority of those cases were community spreading. In Malaysia, because of large religious gathering and the unrestricted , the case increased increased from 29 on March 1st to more than 1500 on March 23 (Source 2).

Outside of Asia, in Europe, the first case is reported on January 24th. In an online briefing on April 16, the WHO’s regional director Hans Kuge said, “Case numbers across the region continue to climb. In the past 10 days, the number of cases reported in Europe has nearly doubled to close to 1 million”, which means 50% of the global burden of COVID-19 was in Europe. Until April 16, according to ECDC (European Centre for Disease Prevention and Control), there are 925,536 cases in total. More importantly, WHO said that the coming week of April will be very critical for Europe. Comparing with other countries’ data of COVID-19 in Europe, Spain, Italy and Germany had more significant amount of confirmed cases (Source 4). The first two confirmed cases in Italy was on January 31, until April 16th, there are 165,155 cases in total reported in the country. At the same time, Spain also had its first reported case on January 31, and until April 16, there’re 177,633 confirmed cases. The first confirmed case in Germany was on January 28, until April 16, there’re 130,450 confirmed cases in total. Also, the foreign secretary Dominic Raab in Britain announced there will be at least three more weeks of lockdown due to the pandemic (Source 8).

The Latin America country has been most affected is Brazil. The country has its first confirmed case on February 26. Until April 16, there’re 28,912 cases confirmed. Peru has its first confirmed case on March 6, a 25 years old man who traveled back from France, Spain and Czech Republic.

Section 2 begins with a brief chronology of COVID-19 outbreak in China. It summarizes how did CVC start and spread in China, and the government’s measures taken to halt the virus. In addition, it compares the current virus situation with that due to SARS in 2003. Section 3 describes COVID-19’s impact on the global economies as well as the local economy in China. Using data and graphs it describes the effects of COVID-19 around the world but especially in China. Section 4 describes the specific type of workers this paper will focus on i. e. the low income workers in China. Section 5 discusses how COVID-19 impacts low-income workers in China, and compares it with SARS’s case of 2003. Section 6 presents the Chinese government’s measures to help the local economy and especially the low-income workers. The paper also analyzes measures taken by other countries’s to point out possible future policies for improving the needs of low-income workers after the COVID-19 crisis.

II. Spread and containment of COVID-19 in China

COVID-19 is the infectious disease that is caused by the recently discovered Coronavirus, and the accompanying symptoms are fever, dry coughing, runny nose, or diarrhea. The virus can spread from person to person through small droplets from the nose and the mouth when a person who carries COVID-19 coughs or speaks. Once the virus is in the body, it takes hold first in the upper respiratory tract, so the patients begin to experience fever, dry cough or shortness of breath (Source 22). The symptoms become more and more severe once the virus moves to the lower respiratory tract where it may cause severe problems like bronchitis and pneumonia. It happened in Wuhan, China, first in December before the outbreak, but until March 13, 2020, there are 142539 cases happened in the world and 5393 death in total. On March 11, WHO declared Coronavirus as a pandemic. In the following days, Italy, America and Spain announced national states of emergency in their efforts to deal with this pandemic. China reported to WHO a few numbers of pneumonia cases on January 4, starting from January 23, cities in China like Wuhan, and other provinces like Hunan, Guangdong and Zhejiang started to lock down the cities and impose travel restrictions. Most people paid no attention in the beginning as the CVC occurred, however, as the infection numbers exponentially increased in the country, government immediately posted policy – requiring people to stay homes and the schools started online classes.

Comparing with SARS, or Severe Acute Respiratory Syndrome, that happened in 2003, the case of COVID-19 is much more serious and devastating. The first identified case was in Foshan, Guangdong, China, and it is primarily transmitted through person to person. According to WHO, the epidemic of SARS affected 26 countries and resulted in more than 8000 cases in the world. But, until April 12, 2020, 09:25 GMT, there are already 1,787,069 people infected to COVID-19, which is almost 223 times greater than SARS (Source 24).

III. Impact on the global as well as local economy in China

In China, according to the China National Bureau of Statistics, industrial production, sales and investment all fell in the first few months of the year compared with the same time period in 2019. Industrial output fell 13.5% in January and February of 2020, retail sales dropped to 20.5%, and fixed asset investment fell 24.5%. According to the latest first season China GDP from the National Bureau of Statistics, the Primary Industries and Resources fell by 3.2%, and it includes crop planting, forestry, animal husbandry, aquaculture and other direct production of natural products as the object of production. The Second Industries and Resources fell by 9.6%, and the Third Industries and Resources fell by 5.2%. In total, the first season of China’s GDP fell by 6.8% (Source 11). This is the first time that China’s GDP shrinks for the first quarter of the year, and it mainly because of the COVID-19 crisis. Looking closely at the chart below, comparing with the previous years, agriculture, industry and service industry all grown by negative percent. Comparing with the Second and the Third

Industries, COVID-19 had the least impact on the Primary Industries. However, agriculture and animal husbandry was still effected by different provinces’ travel restriction and African swine fever, which makes it hard for farmers and vendors to sell and circulate their products. In Fenghuang News, an interview of a vegetable vendor from a small town near Wuhan exemplify some dilemmas that people at Primary Industries faced. On January 23, one day before he’s going to sell those vegetables to the city, the Wuhan government announced level I major public health emergency – closing the exit channels of all provincial, county and township or even some village roads, and forbidding the passing of all vehicles and personnel. It is very hard for him to go to the field to pick his vegetables, not to even speak of selling those. This is absolutely a huge loss for those farmers, but at least the local government gave some measures to help on maintaining their basic living and small amount of selling. Although the first season of Primary Industries have a negative growth percent, most of people at this industry slowly returned to work in March. According to the press spokesman Shengyong Mao from National Bureau Statistics on April 17, inside of this -3.2%, agriculture (farming) is still on its stablest level, and it has a year-on-year growth of 3.5%. Because climatic conditions in the main agricultural areas are generally favorable, which is beneficial for the spring sowing. In the end of March, the first and second type of winter wheat’s area sown was 87.2%, which is 3.5% higher than 2019. The output of poultry eggs increased by 4.3% and milk increased by 4.6%, except the total value of output of animal husbandry was fell by 10.6%. Because the animal husbandry holds nearly 45% of the total Primary Industries, which kind of slows down the growth rate.

The Second Industries and Resources fell by -9.6%, and this is a result of declining in domestic and foreign demands brought by COVID-19.

Most of the industrious production in China stopped from January to February, but along with the returning to work on March, industrial output in March narrowed to -1.1% from – 13.5% in the previous two months (Source 12). But, because of the spread of COVID-19 all over the world, some orders were canceled instead of increasing. The Third Industries was fell by -5.2%, catering

industry drops 44.3%, retail industry drops 19%, but the information industry increases 13.2%. In early January to February, the Third Industries drops 12.2%, but in March it narrowed by 3.9 percent point. Because most of the activities became online, new economies such as fresh delivery, online education, telecommuting and online health care are growing quickly. From January to February, China’s mobile Internet access traffic reached 23.5 billion GB, up 44.2% year on year. Revenue from online game services above a certain size grew by 7.7% year on year, Internet platforms by 9.5%, Internet search services by 10.3% and Internet data services by 21.4%.

Meituan is one of the biggest food service companies in China, according to its report on March, most of local living services (catering, entertainment, education, etc.) open in late march or later. At the same time, all kinds of merchants generally face the difficulty of no customers. As many as 71.7% of residents chose to order food delivery at Meituan during the outbreak of COVID-19. Online orders exceeded offline consumption (53.7%), and 41.6% of residents chose to buy daily necessities through Meituan or Meituan Paotui. In their data, delivery accounted for 53.6% of the merchants’ revenue, up to 42.9% of the merchants took delivery more than 70 %. Moreover, because of COVID-19, some middle- aged and elderly people who have not been exposed to online consumption are also trying online services (Source 14).

According to the press spokesman Shengyong Mao from China National Bureau of Statistics, the surveyed urban unemployment rate fell in the first quarter of this year, but the overall employment situation remained stable. In the first quarter, 2.29 million new urban jobs were created, and the surveyed urban unemployment rate was 5.9% in March, down 0.3 percentage points from February. In addition, the total labor force for migrant workers is 122.51 million people.

Comparing SARS and COVID-19, this two crisis have different impact on world’s economy. For SARS, almost 8000 lives died from this virus, which shaved 0.5% to 1% off China’s growth in 2003. In addition, China accounted for 4.2% of the global economy in 2003, while it controls 16.3% of the world’s GDP in 2020. It means the slowdown of Chinese economy because of coronavirus will certainly impact the world’s economy now. All provinces in China extended the lunar new year holiday by 10 days to contain the spread of COVID-19 (Source 26).

Quarantine and travel restrictions create both short-term and long-term economic consequences on the globe by disrupting normal activities, production and trade. World Trade is expected tofall between 13% to 32% in 2020 since the COVDIS-19 disrupted most of the economic activities. It is certain that every country faces dilemma because of COVID-19, but some developing countries are in a worse economic situation in particular. Nearly 100 countries closed boarders, and DESA predicts that it may leads to a global economic contraction of 0.9 percent by the end of 2020 or even higher. Lockdowns in Europe and North America hit service industries that involves in physical interactions hard, but those industries account for almost a quarter of jobs for economies. ETUC (European Trade Union Confederation) reported that unemployment rate has risen by at least 4 million since the crisis begin, for example, France has the highest known number of workers on short-time work at 3.9 million. Developing countries that depended on tourism and commodity exports are also hit hard by the travel restrictions and quarantine policy (Source 25). The graph below show that the stock market is on a downturn while facing the outbreak of the coronavirus.

First, it began in the oil market made its way through the global financial system, then it added concerns on a large amount of the investors about the state of the economy (Source 28). They fell more than half in March, but the oil price provides 90% of Iraq’s state revenue. However, the gold price is relatively stable compare with the oil price. It is considered as a safe heaven” for most of the investors, but the price still tumbled briefly in March.

Right after President Donald Trump announced that the US economy maybe facing recession, the Dow Jones Index closed 12.9% down (Source 23). Until March 23, 2020, the US stock market triggers a market wide circuit breaker four times. The travel industry has been badly damaged, with airlines cutting flights and tourists canceling business as well as vacation trips. Governments around the world have introduced travel restrictions to try to contain the virus. The EU banned travelers from outside the bloc for 30 days in an unprecedented move to seal its borders because of the Coronavirus crisis. In the US, the Trump administration has banned travelers from European airports from entering the US. Data from the flight tracking service Flight Radar 24 shows that the number of flights globally has taken a huge hit. The Bureau of Statistics announced that the numbers of unemployed persons who were jobless increased by 1.5 million in less than 5 weeks to 3.5 million. In addition, the Labor Apartment reported on April 23th that 4.4 million Americans filed for unemployment insurance in the week ending with April 18th, bringing the five weeks in total to 26.5 million (Reference 37).

According to OECD, the world’s economy could grow in its slowest rate since 2009 because of the coronavirus outbreak. Now, as data suggested, Chinese businesses that survived the outbreak are back at work. According to the cnbc website, official and third-party figures say the resumption of work rate is generally 70% or even higher”.

The International Monetary Fund predicts that the global economy will contract by 3% in 2020. Migrant workers are one of the most vulnerable groups since they tend to work in sectors and have less protections under this circumstance. In India, the government has welfare measure to people below poverty line, but migrant workers rarely have access to it. Jan Sahas, a nonprofit, conducted a survey about the impact of COVID-19 on migrant workers. The survey showed that 62% of workers did not have any emergency welfare measure and 37% did not know how to how to access the existing schemes (Reference 38). Also, in Singapore, there’s almost a quarter of the city’s 5.7 million residents are migrant workers, and the average migrant workers earn $400-465 a month, according to TWC2 (Transient Workers Count Too).

IV.  Who are Low Income workers in China

According to the policy, poverty line in China are people who earn 3535 CNY per year or less. Low income group refers to people whose income is lower than the annual income or monthly income stipulated by the state or the province. Most of low-income people are the unemployed, destitude families and the disabled people. In China, each province sets its own minimum wage according to its local economic situation. For example, in Beijing, the monthly minimum wage is 2200 CNY, while in Loud, Hunan, the monthly minimum wage is 1,130 CNY (Source 39). China’s labor force rate dropped to 68% compared to 68.5% in 2018. Unemployment rate increased to 3.6% in Dec, 2019. According to Chunyan Wang, the deputy director general of the poverty relief office under the state council: A total of 20.8594 million migrant workers in 25 provinces have left home for work, accounting for 76.43 % of the total number of migrant workers last year. It’s an increase of 6.659 million and a 24.4 percentage points since March 6 (Source 15).

Most of low-income people in people received limited education or none, and they are often migrant but not always. For example, low-income families that receive the minimum living guarantee from the government are disabled people (old or young) who cannot work, some left- behind children’s families, and etc.

IV. How are low-income people impacted because of COVID-19

Wuhan was placed under lock-down since January 23, and people except doctors, nursers or workers for food delivery and supermarket can continue to work. Most of white-collar people can do their jobs remotely, while food delivery workers and some small shops owners cannot stop working because they lack of saving. In addition, months of restrictions on travel, work and daily life are putting huge pressure on low-income families at the margins of society. According to a survey of 120,000 people conducted last week by the China Household Finance Survey and Research Centre, a respected independent consultancy in Chengdu, “A fifth of Chinese households can survive only 2.3 months without any income, while 40 per cent cannot last past three months” .

Chunyan Wang from Poverty relief office of the state council said that the decrease in consumer demand and the travel restriction because of COVID-19 has indeed affected the sales of agricultural and sideline products, and there are some places appear unsalable, such as Hainan’s winter melons and vegetables, Yunnan’s flowers and so on. Most of low-income families are lack of social support, and they cannot get payed regularly because of the travel restrictions. Most of them face the poverty line, and have to pay other expenses as well. Some of migrant workers inside of this group have bigger dilemma since they cannot go back to home or the working place, and they are separated with their families. This situation made it hard for ow-income people to keep cash coming in, or even sustaining their basic living. One type of them are people who are infected to covid-19. The health code for many patients are still red, meaning that many low-income patients, even if they recover, cannot work outside the home. Affected by physical damage, health code, employment discrimination, which means new patients will be absent from work for a longer time than ordinary people, or bring new illness to the poverty line again. In addition, many migrant workers do not have unemployment insurance. Moreover, the reporter from xueqiu.com said that the outbreak has worsened the plight of some already poor families, depriving them of their last source of income, such as informal workers such as waste- pickers and small traders. Some households ran out of savings and began to borrow the money from different means. In addition, most of these families suffer from poverty caused by illness, loss of the main labor force, single parent, inter-generational support and other situations (Source 35).

Although most of factories try to return to business as soon as possible, as of late February, only 30% of China’s small and medium-size companies had returned to normal operations, according to a survey by the Ministry of Industry and Information Technology. According to Wall Street Journal, in a survey of more than 8,000 working professionals by Chinese recruitment website Zhaopin in mid-February, one-third of respondents said they had observed companies cutting jobs, and 46% said they knew of firms that hadn’t paid salaries on time.

VI. China’s measurements on helping low-income groups during COVID-19 period

According to the data from National bureau of statistics, from January to February, retail sales of consumer goods fell by 20.5 per cent year-on-year. Among them, merchandise retail sales fell 17.6 percent, and food and beverage revenue fell 43.1 percent year on year. In the face of considerable decline, it is necessary for governments at all levels to take active measures to boost consumer confidence and promote economic recovery. For helping most of families, and particularly low-income groups, lots of cities announced the issuance of consumption vouchers to the public to encourage and guide residents’ consumption and relieve the pressure on some enterprises and industries caused by the COVID -19 outbreak. According to incomplete statistics, more than 30 cities in China have issued consumption vouchers, which sums up to more than 5.6 billion yuan. Among them, Nanjing, Hefei, Hangzhou and other places chose to issue consumption vouchers through alipay, with an amount of more than 4 billion yuan. As of April 7, more than 10 million offline businesses across the country had benefited from the vouchers, more than 90 percent of which are small, medium and micro businesses. Also, the central government has allocated 139.6 billion yuan for poverty alleviation, and construction has begun on more than 260,000 poverty alleviation projects in 22 provinces in the central and western regions. According to Shengyong Mao, “In terms of income, the per capita disposable income of some provinces with a large number of low-income people, such as Sichuan, Guangxi, Tibet, Guizhou and Qinghai, rose by 5.3%, 4.6%, 9.5%, 4.8% and 3.1% in the first quarter in nominal terms, respectively, significantly higher than the national level”.

From January till now, Alibaba announced a program called “Tianmao Zhunong” (天猫助农/爱⼼助农). It aims at helping some low-income farmers to sell their unsalable products such as Jiangsu’s local eggs, Dandong’s strawberries, etc. Thirty leading Chinese agricultural industry experts have teamed up with Alibaba’s “love to help farmers” program to help farmers who are facing dilemma because of COVID-19. More importantly, China has a strong online selling system, which also greatly helps those low- income farmers too. According to Xinhua net, Ten thousand farmers start to sell vegetables through live video at Taobao, and more than 300 anchors participated into these lives to help those farmers. Ali’s program helped farmers sell more than 30,000 tons of agricultural products. Jingdong reached 10,000 tons of unmarketable agricultural products cooperation with 14 regions; Pinduoduo’s “live broadcast room for mayors and county heads” won 170,000 orders and sold more than 1 million kilo of agricultural products (Source 18).

(Picture 1: One county leader and an anchor started to sell their local specialty at Taobao. Picture 2: three different famers also tried to sell their own fruits through live videos at Taobao)

The chairman Jingpi Xi said on March 6 that the governments should support the low-income labor workers employment in priority, and they should prefer to use and organize them in face of some opening of major projects or constructions. The governments should encourage enterprises to employ more people from low-income areas, and giving “ID card” for those low-income people. In addition, there is always a petty loan program for low- income farmers in China (Source 19). On January 31, sichuan province issued a document to give proper preference to the credit policy, flexible adjustment of repayment arrangement and reasonable extension of repayment period for the poor people who have temporarily lost their income source due to the epidemic. Those who raise objections or complaints against bad credit records due to epidemic prevention and control should be timely verified and properly handled. Taking Guizhou province as an example, during the COVID-19 period, a total of 19,400 households extended small-amount credit loans for poverty alleviation with a total amount of 943 million yuan, and 27,200 households with a total amount of 1.138 billion yuan. Also, on April 25th, the Ministry of Civil Affairs in China posted a new policy on helping low-income families who suffered from COVID-19. According to the degree of difficulty, each family will receive temporary aid from 1000 yuan to 10000 yuan (Source 36). So far this year, local governments in China have provided 3.71 billion yuan in temporary aid to help people who live with subsistence allowances and those in extreme poverty. So far it benefited over 81.689 million people. In accordance with the standard of no less than 500 yuan for urban residents and no less than 300 yuan for rural residents, Hubei government provides daily material assistance to both urban and rural low- income residents (Source 20).

VII. What are low-income group’s remaining needs & possible policies

On early April, India announced $22.5 billion stimulus package to “be disbursed through food security measures for poor households and through direct cash transfers.” Also, South Korea announced cash payments of up to $186 per household except for the top 30%. In America, the White House announced A $19bn “food assistance program under the new pandemic”. It provides assistance to affected farmers, and distributors, also ensures the safety and stability of the food supply chain.

There are still some families in need did not receive temporary help from the government on time. And there are still a large numbers of vulnerable people above the government’s poverty line and subsistence allowance, who are not included in the government’s aid system. And even those on subsistence allowances, for some struggling families, cannot cope with the extra impact of this COVID-19. Also, some families who have multiple children cannot even pay for their tuition since they are not receiving free education. If the government cannot give any support to those low-income families in the rural areas, they will have maybe hundreds or thousands tuition fee, which is a lot for them.

1. Government should strictly follow the rules of helping every low-income families as much as possible
2. Learning from countries like Japan and Korea, giving direct cash grants to low-income groups.
3. Giving education about COVID-19 to low-income families, raising their awareness on this global pandemic
4.  Working harder to alleviate poverty through employment, and ensure that jobs are stable and expanded; supporting leading enterprises and workshops in poverty alleviation to return to work as soon as possible.

VIII. Concluding Remark

The outbreak of COVID-19 influences the world tremendously in terms of health, everyday living and studying. Until April 26th, there’re total 2,939,386 people confirmed with COVID-19, and 203,703 people died from this pandemic. My paper focused on one of the most vulnerable parts of the population – the low-income groups. I summarized the impact of COVID-19 in China and also the globe in the third section, but specifically focused on China. In the next section, I analyzed how the low-income workers in China are affected because of COVID-19, and my major findings are:

1. Quarantine and travel restrictions create both short-term and long-term economic consequences on the globe by disrupting normal activities, production and trade.
2. China has its uniqueness on advanced delivery and online shopping services, which brings tremendous help to people during the CVC period.
3. Most of low-income families are lack of social support, and they cannot get payed regularly because of the travel restrictions. But the Chinese government already gave some measurements to help those families such as providing cash and cash coupon in different cities.
4. Taobao Zhunong and other similar programs combine the power from local governments and social influencers bring a lot of benefits and help to farmers or vendors. These program are very special and efficient, and only occurred in China.

References

This paper has benefited from the following references, however, for simplicity, we have not always make explicit mention of these references in the body of the paper. Paper Mentor: Dr. Tayyeb Shabbir, Wharton School

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About the author

Amy Ren

Amy is a rising senior at the Williston Northampton School in Massachusetts.

The post Impact of COVID-19 Crisis on Low-income Workers in China appeared first on Exploratio Journal.