Tabdzir prohibition education in overcoming consumptive behavior

The purpose of this research is to find out the opinions of the commentators regarding the concept of tabzir as an effort to avoid consumptive behavior contained in the Q.S. Al-Isra verses 26-27. Consumptive behavior is the tendency for someone to behave excessively in buying something or buying unplanned. The design used in this study was tahlili, namely interpreting Q.S. Al-Isra verses 26-27 by explaining all aspects contained in the verses interpreted by the commentators. The primary data in this study were Al-Quran and interpretive books. Secondary data was the result of previous research to complement primary data. The results of the study show that people who are wasteful and wasted their wealth will fall into immorality and would not be grateful, even if a Muslim must be able to manage his property proportionally and always be grateful. The implication was that humans must be grateful, avoid wasteful attitudes, and get used to living qana’ah (enough).

Introduction
Humans as creatures of Allah SWT often waste in various aspects of life, especially the waste of food and items that are not needed. Food that is not eaten due to waste expires and is allowed to rot, waste occurs at all stages of the food supply chain, from agriculture to household consumption which represents 50% of total food waste [1] - [4]. Food waste that exists in Indonesia from year to year is caused by spending excess money as part of consumptive behavior [5], [6]. Consumptive behavior is behavior in buying goods or services without considering their use [7], [8].
Research conducted by the Indonesian Institute of Science shows that Indonesians are ranked third out of 106 countries in the world in spending money to buy food, goods, and services they want even though they are not needed [9]. This phenomenon occurs in various circles of society including students who cannot place learning priorities over pleasure in associating with doing and buying things that are not needed [10], [11]. The impact of consumptive behavior is that someone becomes a materialistic and hedonistic person [12], [13].
Indonesian society, which is predominantly Muslim, has guidelines in overcoming consumptive behavior. Islam has established the basic principles in the economic field that are stated in the Al-Qur'an and Sunnah, both macro and micro. One of the provisions of the Al-Quran concerning the consumption aspect is the prohibition to act redundant (Tabzir). Like consumptive behavior, redundant means wasting wealth and spending on worldly interests [14] - [18]. An explanation of people behaving consumptive is describing in Q.S. Al-Isra verses 26-27, which mean: "And give to the families that are close to their rights, the poor, and those who are on the way and do not waste (your property) lavishly. The extravagant is the brother of Satan and Satan is very suspicious in his Lord" Based on the results of the research, the verse explains about doing good to parents, relatives and staying in touch, not wasting property lavishly and wastefully being the brother of Satan [18] - [20]. The waste that will be examined is based on the interpretation of Q.S. Al-Isra verses 26-27 focus on waste in consuming food. Of course, to see this in this study we will see how the opinions of the commentators, the concept of redundancy and its prevention, and the implications of the prohibition of tabdzir to prevent consumptive behavior.

Methods
The research design used was library research with a revelatory approach to Q.S. Al-Isra verses 26-27 concerning the prohibition of Tabdzir and interpretive books as primary data sources and journals and scientific writings as secondary data. The commentaries studied were Tafsir Al-Qur'an Al-'Adzim (Tafsir Ibnu Katsir) Volume 6, Tafsir Al-Azhar Juzu'15, Tafsir Al-Maraghi, Tafsir of the Islamic University of Indonesia, and Tafsir Al-Mishbah.
The procedure taken was to collect the opinions of the commentators through the commentary books, search for literary sources using the Mendeley application and books relating to QS. Al-Isra verses 26-27 concerning the prohibition of tabdzir and analyzing the verses that would be studied so that we got an overview of the educational implications of these verses. Analytical (tahlili), is the interpretation of the verses of the Al-Qur'an by describing all the aspects contained in the verses that are interpreted and explaining the meanings contained therein, by the expertise and tendencies of the interpreter who interpret the verses [18], [21].
The steps of the tahlili method are interpreting the sequence of the verses in the Mushaf as the first step. The second step is interpreting the vocabulary of the verse to be interpreted. The third step is to explain the asbab al nuzul ayat (reasons for the revelation of the verse). The next step is to explain the munasabat of the verse and the final step is to explain the interpretation of all aspects of all of them and provide an explanation of the content and purpose of the verse [21].

The sequence of Verses and Meaning of the Word Waste in Q.S Al Isra 26-27
This research choosed Q.S Al Isra verses 26-27 which contain the commandment of Allah SWT so that humans do good to their immediate family and poor people as part of social responsibility and prohibit people from being extravagant. This verse is a continuation of Verses 23-25 which explains the obligation to worship only Allah and pay respect and dedication to parents.
The word تبذيز (tabdzir) is taken from the word بذر (badzr) which means to separate. The origin of the word is to throw away the seeds which are associated with losing the property because they do not know its benefits [22]. From this, the scholars understand that the meaning of Tabdzir is waste or exclude what is not the truth, except to spend it for good [23]. Another meaning according to Imam Syafi'i, Ibn Mas'ud, and Ibn Abbas is to spend wealth, not in the way, while Imam Malik said tabdzir is getting property from the right way but spending it incorrectly [24], [25].

The Origin of the Verse
The history of Uthman bin Aswad explains the origin of the descent of QS Al Isra Verses 26-27, when Uthman bin Aswad once toured the mosque around the Ka'bah, with Mujahid and looked at a mountain in Mecca, then said: "As if someone Infaq like this (Ka'bah) for obedience to Allah, of course, this is not part of redundant behavior, and if he uses only one dirham to act against Allah then this is redundant [26] - [28].
Still, in the same history, Prophet Muhammad SAW once passed in front of Sa'ad who was performing ablution and Prophet Muhammad SAW reminded Sa'ad not to exaggerate in ablution even though he was in a flowing river. Prophet Muhammad said: "Allah SWT has warned about the bad behavior of mubadzir by categorizing it as a satan act, Allah SWT said (In fact, the extravagant is the brother of satan) [26] - [28]. Satan is the term for the devil who was reluctant to prostrate himself to Prophet Adam as described as a rebellious being as described in the Q.S. Al-Kahfi verse 50 [29]. Satan's reluctance to prostrate because he feels great is one of the kufr of Allah's favor. Favor kufr was often practiced by the Arab population during the Jahiliyah era, they often boasted that their tribe was nobler than other tribes, prioritizing lust over reason, spending wealth on gambling and drinking was a routine activity in their life [26], [30], [31 ]. Based on the bad deeds, the Q.S. Al-Isra verses 26-27.

Munasabatul Paragraph (Principles of Consumption Behavior)
Q.S Al-Isra verses 26-27 contain Allah's orders for humans to do good to families and poor people and prohibits humans from having consumptive behavior by spending their wealth lavishly [32]. This prohibition has the aim that Muslims can regulate their spending patterns carefully and only for goods that are needed, not to give assets to people who do not have the right to receive and give excess assets to others [33].
Further information regarding not being excessive, is contained in QS Al-Furqan verse 67 which means "And people who when they spend (property), they do not excessively, and are not (also) stingy, and are (the spending) in the middle. -the middle between that". It is superfluous in the sense of not being a large peg than a stake [23], [26]. This is as stated in Q.S Yusuf verses 47-48 and Q.S. Al-Hijr verse 3, that consumption must be done sparingly, not indulging in pleasures, lust, and neglect by desire will harm the future [34].
These verses provide learning about consumptive behavior, namely doing good by providing fulfillment for family, relatives, poor people, and Ibnu Sabil. It is not allowed to be wasteful (Tabdzir), miserly and exceed the limit kaemampuan because it is a vicious behavior. The principle of frugality is reflected in the policies implemented by the Prophet Yusuf who consumed wheat as needed so that he was able to face famine.

Interpretation of the Mufassirs
Al-Quran is not a book that talks about one field of science but is a book of guidance, although to understand the verses it requires interpretation, especially the kauniyah verses, namely the verses concerning the signs of Allah's greatness in the universe, namely natural laws that apply to anyone. [35], [36]. Tafsir is a way to understand the contents of the Koran which means to explain [37], [38]. According to Al-Zarkasyi tafsir is a science to know the understanding of the book of God revealed to the Prophet Muhammad SAW so that it can explain the various meanings, laws and wisdom contained in it [39] - [41]. Interpreting the Qur'an means explaining/explaining the meaning that is difficult to understand from the verses of the Quran.
A person who has the perfect ability to know the meaning of Allah SWT in the Koran according to his knowledge is called Mufassir [42], [43]. Requirements to become an interpreter include having a good faith, not the following lust, understanding Ushul At-Tafsir, being good at Riwayah and Dirayah Hadith, understanding the basics of religion, understanding Ushulul-Fiqh, mastering Arabic and knowledge, having good faith. which is true, obey all the teachings of Islam and have the right goal [44] - [46].  [23].
Interpretation of the Islamic University of Indonesia. Q.S Al Isra verse 26 at the end of the verse states that Allah forbids the Muslims to consume their assets lavishly, but giving assets to close family, poor people, and ibn sabil is an obligation that must be fulfilled [33], [49]. Tabdzir is the brother of Satan as expressed in verse 27. A waster in this verse is a person who consumes his property in immoral acts and other acts that are prohibited by Allah SWT.
Tafsir Al-Azhar. This interpretation explains that extravagant is the meaning of the word Tabdzir. Mujahid said: "Even though he spent all his wealth on the right path, he was not redundant. However, even though he only took out a bushel of rice, it was not in the right way; it is redundant ". In this interpretation, there is a story, the experience of Hamka as a child, and learning from his father when Hamka dropped the fried peanuts. His father said to keep choosing those who fell, not to be redundant because it was still clean [24], [50]. If leftover food is better given to Ibn Sabil, so try not to overdo whatever is consumed so that the leftovers end up stale. The explanation for verse 27 is that the extravagant is a friend of Satan. A person's behavior usually depends on the friends around him. An Tafsir Al-Maraghi. In this interpretation, there is a story about how someone can consume their property. Narrated by Ahmad from Anas bin Malik, a man from Tamim asked Rasulullah SAW about how to spend his wealth. Rasulullah said, pay zakat, continue to stay in touch with relatives, give it to those who ask, neighbors, and poor people. When that person asked for relief, Rasulullah SAW said to give to close family, poor people, people on the way and do not waste your wealth lavishly [26]. It is also narrated from Ali that if you consume and spend wealth so that others can see and hear it, it will become a part of Satan.
Tafsir of Ibn Kathir. In this interpretation, Ibnu Mas'ud said that Tabdzir is improperly spending wealth and causing damage. Meanwhile, Ibn Abas said that if a person spends all his assets in the right way, it does not include mubadzir, but even if a little is spent on things that are not right, then it is called redundant. Qatada said, "At-Tabdzir is shopping in immorality against Allah, and on things that are not true and for damage" [48].
Tafsir Al-Mishbah. The word tabdzir/ extravagance is understood by scholars in the sense of expulsion which is not the truth. Therefore, if a person spends all his wealth in goodness or truth, he is not a waste. Sayyidina Abu Bakr ra. Submit all his assets to the Prophet to fight in the way of Allah. Sayyidina Uthman ra. Spend half of his wealth. Prophet Muhammad saw their livelihood received and he did not consider them to be wasters. However, washing your face more than three times in ablution is considered wasteful, even though at that time the person concerned was performing ablution from a flowing river. Waste has more to do with space than with quantity.

Anti Tabdzir Education: Avoiding Consumptive Behavior
The scholars stated that tabdzir was related to giving in the sense of giving or consuming excessively. Organizing and being wise in terms of managing finances is a solution to be able to empower needs and desires that are well organized. Anti-Tabdzir education will produce Muslims who are Qana'ah which means to feel sufficient. This trait will teach every Muslim to consume everything according to the needs of his body and not follow lust. Qana'ah is an attitude of soul satisfaction for the sustenance that is owned, even though a little is given but must still be grateful, it can be concluded that the meaning of qana'ah from the verse is by the explanation in the Contextual Sufism book is to accept the sustenance given and respond to gifts Him, namely by giving thanks (Husni, 2018).
Gratitude means giving praise to those who have given us goodness, in this case giving praise to Allah SWT who has given pleasure in living life. Gratitude is done verbally, other body members, and with the heart. Gratitude is a form of acceptance of the blessings of Allah SWT, not even denial of Allah's favors. Remembering the blessings of Allah SWT will result in a decrease in punishment. The fall of punishment, because humans are not grateful, is narrated in QS An-Nahl verse 112 which means "And Allah has made a parable (with) a country that was once safe and peaceful, its fortune comes to it abundantly from all over the place, but it is (residents) deny its favors. -bless Allah; therefore Allah feels upon them the clothes of hunger and fear, because of what they always do.
Anti Tabdzir education can be started from the family by involving parents as the primary and first teachers for their children. Al-Syaibani argued that the highest goal of Islamic education is to prepare for the life of the world and the hereafter. While the final goal to be achieved is to develop the nature of students, both spirit, physically, will, and reason dynamically, so that a complete and supportive person will be formed for the implementation of his function as Khalifah fil ardh. Muhammad Athiyah al-Abrasyi stated that the objectives of Islamic education are: (1) forming noble morals (2) preparing for the life of the world and the hereafter (3) preparing to seek sustenance and maintaining its usefulness (4) fostering a scientific spirit among students (5) prepare skilled professionals (Siregar, 2016).
Islam aims to form a society with a solid social order. In that order, each individual is bound by brotherhood and affection like a family. A universal brotherhood and not bound by geographic boundaries. This is because humans can never live with help without other people. Helping hands, providing material needs, is the survival of humans who need such assistance. Justice in Islam includes social and economic justice.
Social and economic justice can be achieved if humans can control their passions in possessing property. Told the story of Pharaoh who felt very powerful, but still lust destroyed him [24]. In essence, the assets owned by humans in the world are only limited to enjoying existing assets, not the true owners. For example, land can be used as shelter or agricultural land. If you indulge your lust, don't feel enough, don't give thanks, and are exaggerated, then Allah SWT can take all human assets easily. Thus the absolute owner of the property or everything on this earth is Allah SWT, human ownership is only relative, limited to carrying out the mandate of managing and utilizing according to His provisions (Batubara, 2018).
Anti Tabdzir Education is an effort to avoid consumptive behavior. Being grateful and qana'ah means accepting sincerely everything that Allah SWT has given, praying, and trying, patiently accepting Allah SWT's provisions, trusting and not being interested in the deceit of the world. "It is not wealth because of the abundance of wealth, but the wealth that is the wealth of the heart. Qana'ah does not prevent saving property because there are many uses for the property, for example, like paying zakat, one of them (H.R. Bukhari) (Husni, 2018).

Conclusions
The opinion of the mufassirs in interpreting Q.S Al-Isra verses 26-27 is that there is an order to the Muslims to give the right of a portion of their property to others in need. A Muslim is asked not to be consumptive by wasting his wealth so that it will lead to the path of evil. Al-Quran strictly forbids not to waste things that do not get benefits and bring benefits. Doing things like this would be tantamount to declaring himself a class of Satanists, of course, his haven is hell.
Mubazzir can be defined as excessive consumptive behavior and is wasteful, namely removing what is not his right. The impact of redundant behavior can lead a person to deviant things if one cannot determine where it is that leads to virtue values and where to place it that leads to evil values, so it is necessary to identify them first. The impact is to cause losses in his worldly life. Likewise, it can lead to big things in the afterlife, namely getting the wrath of Allah SWT.
Anti Tabdzir Education can be a foundation and a shield so that people do not behave consumptively. This can be done by instilling good education starting from family, building an attitude of empathy and sympathy, having the principle that in one's property there are other people's assets / rights, having qana'ah characteristics and being able to control lust in owning assets because the assets owned are a form of gift from Allah SWT. Always grateful for the blessings of Allah will make humans live calmly and peacefully in the world, and God willing, in the hereafter.


Continuous and Binary Sets of Responses are Not the Same: Evidence from the Field

This paper conducts a pre-registered study aimed to compare binary and continuous set of responses in questionnaires. Binary responses consist of two possible opposed responses (Yes/No). Continuous are numerical, where respondents can indicate an answer in a 0 to 10 horizontal blind line. We study whether binary and continuous feasible sets of responses yield to the same outcome (distribution) and the same cost (duration in minutes). We collect data from 360 households in Honduras who were randomly assigned to Yes/No questions or given a slider (visual scaling 0-10) to mark their responses, therefore, we provide causal evidence . We ﬁnd that respondents are 13% more likely to say “Yes” and spend 2.1 minutes less in the binary setting. Besides, we ﬁnd that most of the diﬀerences between binary and continuous settings arise from questions which include negative wording.

Introduction
When dealing with opinion surveys, question design is a key aspect to obtain the desired level of detailed information and to avoid difficulties in expressing opinions. Both, capture the true response of the subject and avoid any bias from the design are of utmost importance. Therefore, the type of question used in a survey is crucial and will depend on the aim of the researcher.
The literature on survey design, and more specifically, the choice of the response format, offers a wide range of typology of questions to evaluate the degree of agreement with an issue [1; 2; 3; 4; 5]. Agree-disagree scales are used to obtain the level of agreement with an issue [6]. If the scale options directly refer to the issue under evaluation, item-specific scales are then recommended [7; 8]. In social sciences, dichotomous scales (binary responses), in which two responses with opposed directions are offered (typically Yes or No) are widely spread when the goal is to evaluate the direction of the answer (agreeing or disagreeing with an issue). This type of questions are very clear in meaning. There is considerable consensus on the meanings of the options [9] and they are easy for the respondents to understand. They also imply low costs and low time for both the researcher and the respondent [10]. In addition, in case of factual questions it has been showed that people correctly answer binary questions more often when the correct answer is "Yes" [3]. This format also requires less interpretative efforts compared to longer ones [9]. However, the lack of neutral responses may alter the results.
It may lead to acquiescence bias, which is the tendency for a respondent to agree with a statement without considering the content of the item or to please the researcher [11; 12; 13; 3]. Moreover, it could be problematic for respondents with neutral attitudes because of the lack of accurate mapping between moderate responses and the dichotomous options offered. As a result, it would require a major cognitive effort [14; 9; 15].
Continuous rating scales allow the researcher to obtain the exact level of agreement with certain issue. When continuous rating scales are numerical, respondents can indicate an option in a horizontal line, marking precise numbers which express the exact level of agreement for an issue with respect to a maximum number, usually 10 or 100. The use of this metric scale allows subjects to easily transform their opinions, whether extreme or mild, into replies, reducing distortion [16]. Nevertheless, point meanings may become less clear as the respondent cannot differentiate between one number and the other [9].
When choosing among binary and continuous rating scales, it is important to evaluate the advantages and disadvantages of each type of question.
In terms of statistic evaluation, continuous scales allow a wider range of procedures [17]. Regarding survey length and complexity, continuous scales require more time to explain the set of possible answers and to think about its conversion into numbers, implying higher reasoning costs [16; 10]. However, compared to binary responses, continuous set of feasible answers provide better quality of information [9]. In binary scales, where respondents have to convert mild responses into Yes-No answers, the respondent is forced to make computations to transform their opinion in the middle to an extreme answer, resulting in an inaccurate choice, which consequently may manifest a higher level of agreement that would not be reached if middle responses were available (i.e., acquiescence or satisficing [13; 3]). Moreover, binary scales do not allow for nuance in respondents' answers or to study respondents' expectations at an individual level. Therefore, if respondents have problems in computing their mild thoughts into extreme responses, biases arise.
This study investigates the extent to which the use of binary and continuous scales yields different results. Specifically, we study if the probability of agreement with a statement is the same when using binary and continuous set of responses. To do so, we conduct a field experiment in Honduras with 360 randomly selected participants. Following a random assignment, half of the participants were assigned to the binary treatment, in which subjects has to respond Yes or No. The other half were assigned to the continuous treatment, in which they had to answer the same questions but with a different set of responses. Specifically, subjects in the continuous treatment indicate the level of agreement with an statement using a slider with a 0 to 10 scale. To compare replies among treatments, we discretize continuous answers. We assign the binary option "No" if the option indicated is below 5. Those who claim to agree by more than 5 are assigned to the binary option "Yes". In case the option indicated is 5, the answer is randomly assign to "Yes" and "No". Additionally, we study whether question wording such as the use of negative words (e.g., no, anything) or prescriptive expressions (e.g., act as, take care of, limit activities) has also an effect on the probability of agreement. We also analyze the differences in terms of survey length.
We find that respondents are more likely to say "Yes" in the binary setting compared to the continuous setting. In particular, the probability of answering "Yes" is 2.3 times higher when asking in binary instead of continuous. Regarding the survey length, binary questionnaires took 2.1 minutes less, which implies on average a reduction of 41% with respect to continuous questionnaires. Our results hold when excluding males from the sample and those subjects with lower cognitive abilities.
We extend the analysis to the wording of the questions. The literature has shown that question wording also plays an important role in acquiring accurate responses. Negative wording has been defined as a question in which disagreement would be a positive answer [12]. Negative wording is usually introduced in surveys to avoid fatigue throughout the questionnaire, acquiescence [18; 19; 12] and to avoid respondents choosing the same answer [20]. Nevertheless, [12; 21; 22], among others, raise the attention to the additional processing effort that negative questions imply, overall in binary sets, where the respondent have to decide to agree or disagree with a question that includes negation, what may generate confusion (see [23]).
In the same line, our data show that when using negative wording in ques-tions, the probability of agreement jumps to 41% in binary sets compared to continuous.

Results I: Aggregate data
We start showing results of the behavior of participants along the entire experiment. Recall that every participant replied to 30 questions in a random order (52% starting from block A, and 48% starting from block E ). Figure 1 shows the average number of answers "Yes", along with the average time (in minutes) respondents spent on the entire questionnaire in both, continuous and binary answers. We observe that the probability of agreement with the survey question is higher when asking in binary, and that the average time in the binary setting is lower. Table 1 shows the regression analysis. Model 1 does not include controls. Model 2 includes controls for age, gender, and ethnic. Model 3 includes controls for age, gender, ethnic, school, sufficient income, having a daughter, education, and the order of the questions in the survey.
In addition, after controlling for these characteristics, the difference between binary and continuous is statistically significant at the 1 % level (Table 1).
It is important to recall that subjects were assigned to treatments randomly. Hence our results imply causality. Therefore, we conclude that: Result 1: Binary settings produce higher shares of Yes.   [24] developed by [25].
Then, we conclude that Results 1 and 2 are not driven by the respondents' cognitive abilities.
In conclusion, the use of binary (instead of continuous) set of responses make subjects respond more "Yes" (or less "No") and complete the survey much faster. These results are robust to excluding men and low ability respondents.   Figure 3 shows the average number of "Yes" (and "No") question by question. It shows that Result 1 (higher number of "Yes" when asking in binary) does not hold for every single question. Indeed we see that many items are almost identical in binary or continuous.

Results II: Question by question
In particular, we find that 10 out of 30 questions are different and most importantly the direction of the difference is always the same (see Model 3 in Table S7, p < 0.05). That is, from the ten questions that report different numbers of "Yes", all of them report higher probability of agreeing with the survey question when asking in binary instead of in continuous. This difference is statistically significant at a 0.05 level.
Result 3: In 63% of the cases there are not differences between binary and continuous settings. In the other cases, the differences are always in the same direction (positive) as in Result 1.
For sake of completeness we repeat the analysis for women only and for the sub-sample of subjects who passed the cognitive test. Results remain in the same direction (Table S8).
Our results also hold when we remove those individuals responding the neutral option 5 (see Table S9) or when we consider as a cut-off the value of 7 (see Table S10). We selected this value since it is the minimum qualification to pass exams at school in Honduras (over a total of 10) [26] and it could be considered as a turning point between agreement and disagreement.

The impact of wording
We next explore the role of question wording in explaining Results 3. To do this, we distinguish three types of question: negative, prescriptive, and other. Negative refers to questions which include words like "no" or "not".
Prescriptive refers to those questions that include words like "have to" or "must be". Other questions refer to those questions that cannot be classified First, we analyze the differences at the aggregate level when asking in binary and continuous for each group of questions (negative, prescriptive, and other). That is, we compare the total number of "Yes" that the subjects respond in binary and continuous. We start with the whole sample. Table S4 reports the average difference in the number of "Yes" when asking in binary instead of continuous for each group of questions. Regarding the group of negative questions, those who answered binary questionnaires said "Yes" 1.4 times (or 41%) more than those who answered the continuous questionnaires.
The average number of "Yes" is also higher in binary when considering the group of other questions (1.02 times or 8.43%). The difference is not statistically significant when considering the group of prescriptive questions.

Result 4:
The probability of agreeing with the survey question is higher when using binary set of responses and negative wording (41%).
We repeat our analysis for women and removing those subjects with low ability according to the results obtained from [24] (see Table S4 middle and bottom) and Result 4 is confirmed.

Discussion
We investigate how answers change when questions are asked using binary Yes-No answers or by offering a continuous rating scale.
Using data from 360 households in Honduras where subjects were randomly assigned to treatments this paper provides causal evidence regarding differences in the probability of agreement and the time length of the questionnaire. We also provide non-causal evidence of the impact of negative wording.
We compare binary and continuous responses by discretizing the continuous variable, collapsing values into 0 ("No") and 1 ("Yes") answers. That is, those which indicates an option below 5 are assigned to 0, while those indicating more than 5 were assigned to 1. Thus, those answering the neutral option 5, are randomly assigned either to 0 or 1.
Our first result shows that, on average, the probability of agreement is 13% higher in binary sets compared to continuous sets. The fact that the binary setting yields to higher agreement rates is not new since this typology of questions is affected by acquiescence, the tendency to agree with the question without considering the content of the item (see [13; 3]).
Second, we control for the length of the interviews and, as expected, Result 2 shows that continuous settings require more time than binary settings.
Specifically, opting for Yes-No responses reduced the average length of the survey in 2.1 minutes, which implies a reduction of 41.5%. This result is also expected given the wider range of options offered to the respondents, which may need more time to accurately map their opinion in a 0 to 10 interval.
Furthermore, the use of the slider to collect answers is also time consuming.
This is in line with [16; 10], that declared longer completion times by using sliders. Sliders are more engaging than other visual scales [27; 28], and increase the tie and effort needed to provide an answer [15]. Sliders also allow respondents to communicate exact values and its use simultaneously convey the respondent how precise the expected answer should be [29]. In Additionally, we consider respondents' ability using an extension of [24] (see also [25]). We find that 71% of the sample responded correctly to these questions. Using this sub-sample, we test our data and find similar results: high ability respondents require on average 2.3 minutes less when using a binary rather than a continuous questionnaire, which implies a reduction of 45%, 2pp less compared to the whole sample. We also find that the probability of agreement is higher in binary sets compared to continuous sets. Specifically, respondents answer "Yes" 2.4 times more (or 14.1%) in binary sets. [13; 3].
Third, we also estimate the differences of responses for every single question. Result 3 shows that 37% of total questions present significant differences between binary and continuous sets, showing in all cases higher agreement in the binary setting. These results hold when we only consider women and when we take the high-ability respondents' sub-sample. To go a step further, we analyze whether the role of question wording has an effect in our results. In particular, we distinguish negative wording questions, which include negative adverbs in the sentence. Specifically, 8 out of 30 questions include negative words, from which 7 of them show significant differences when we compare the binary and the continuous treatment. This specific group of questions report 41% more agreement in binary than in continuous sets. The group of questions classified as "other" also report 8.43% more agreement in binary than in continuous sets. However, the group of questions with prescriptive words show no differences.

Concluding remarks
This paper tests whether asking questions using binary or continuous sets provide different responses. To do so, we design a quasi-experimental survey where subjects were randomly assigned to the binary or continuous treatment, hence we provide here causal evidence. The experiment was conducted in Honduras where 360 participants were randomly selected. At the aggregate level, we find that subjects are more likely to say "Yes" in the binary setting (Result 1). We also find that subjects in the binary treatment spend 41.5% less time to respond the whole questionnaire (Result 2).
Thus, continuous set of responses comes at a higher cost. We test whether both results still hold when the sample is only composed by women (87% of the sample) and results are similar. We also test whether results remain constant when we control for cognitive abilities. The proportion of subjects classified as low ability is 29%. Results removing this subgroup of participants are similar to the ones obtained using the whole sample. Then we estimate the difference between binary and continuous treatment question by question. We find two relevant results. First, the differences between binary and continuous sets do not occur in every single question but in 37% of them. However, the difference between binary and continuous always goes in the same direction. That is, the probability of agreeing with the survey question is higher when using binary set of responses instead of continuous.
Finally, we analyze the impact of question wording. To that end, we differentiate questions with negative, prescriptive and other wording. We find that the negative wording makes subjects increase the probability of answer "Yes" in a 41%. We find no differences between binary and continuous when using prescriptive wording; in sum, our paper provides causal evidence of the difference between binary and continuous set of responses. We find that the type of answer is important and could yield different results. In addition, question wording could play an important role in explaining its results.

Methods Protocol
The experiment took place in Santa Rosa de Copán (Honduras) between  We also are interested in comparing the time expended when using binary instead of continuous set of responses. To that end, we recorded the time (in minutes and seconds) devoted to complete the whole questionnaire by each participant (note that, given the paper-based nature of the questionnaire, we could only account for the time of the whole questionnaire, not question by question). Finally, to account for respondents' ability, we make use of an extension of [24] developed by [25]. This test consist of several questions

Sample
The total sample consists of 353 subjects. However, 7 of them did not answer all questions. Table S5 presents descriptive statistics of the key variables in our analysis. All the subjects surveyed were residents in Honduras at the time of the survey and had a child between 6 and 9 years old in one of the 12 schools considered (see Table S6 for further information). The average age of the participants is 34 years. They were mostly women (87%) with secondary or less than secondary education, and with a daughter living at home (65%). Most of the individuals have not a particular ethnicity (70%), and 29% are low ability (see S3 for more details about measuring ability).
In addition, 77% of the individuals in the sample indicate that they have enough money to feed their children.
All the subjects answered the same questionnaire with only one difference, the type of answer (binary vs continuous). 52% of the participants answered binary questions (Yes/No questions), and the remaining 48% answered continuous questions.
The average number of times that the subjects answered "Yes" is higher in binary questionnaires (see standarization below). In addition, the response time in binary answers is lower (about 2 minutes per questionnaire less).
Finally, we provide the balance of the randomization across treatments in our experiment (Table S5,  If the individual claims to agree with a statement by indicating more than 5 (r > 5), the binary equivalent is "Yes" and it is assigned to 1. In case of being less than 5 (r < 5), the equivalent is "No", and it is assigned to 0. Finally, if the individual indicates the neutral response 5 (r = 5), we randomly assign the answers to 0 or 1.
We opt for splitting 5 answers as it represents the mean point in the continuous set. Using the slider instead of a "Yes/No" question, we can measure the intensity of an opinion. Similarly to our case, the well-known Likert scale questions [34] also measure this intensity. The original 5-point Likert scale questions use the mean value to indicate "neither agree nor disagree", values above the mean indicate agreement with the statement, and values below the mean indicate disagreement with the statement [35].
Following a similar reasoning and using our continuous questionnaire, we assume that: (1) when the answer is greater than 5, it means agreement with the survey question; (2) when the answer is less than 5, it means disagreement with the survey question; (3) when the answer is equal to 5, it means neither agree nor disagree. Then, as a result, when the individual answers 5, we randomly assign him/her to "Yes" or "No".

Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on request.


INTEGRATION INTERVAL TYPE-2 FAHP-FTOPSIS GROUP DECISION- MAKING PROBLEMS FOR SALT FARMER RECOMMENDATION

The Covid-19 pandemic has caused the development of the marine economy, especially the salt business of Madurese Indonesian people, to experience a decline. Some government programs are marketplace training, providing people's credit assistance, providing appropriate technical assistance such as geomembranes, and online marketing. There are difficulties in running and implementing government programs, so a decision-making system is needed for mapping salt farmers. The purpose is construct a multi-criteria group decision-making model by performing a hybrid interval type 2 FAHP and FTOPSIS method for mapping salt farmers with interval Triangular Fuzzy Number (TFN). The contribution is to determine recommendations based on TFN with the same middle point and group support system. Interval type-2 FAHP is used to determine the most influential indicator on salt farmer mapping, Interval type2 TOPSIS is used to determine salt farmer recommendations. Fuzzy type-2 provides more accurate modeling, better rating value performance, and high flexibility. The findings of this study are indicators that most influence 2 KUSTIYAHNINGSIH, RAHMANITA, PURBANDINI, RACHMAD, PURNAMA recommendation of salt farmers are marketing models, production yields, and profit. The results of the recommendations stated that 45% received good recommendations, 35% entered into moderate recommendations and 20 % entered into bad recommendations.

INTRODUCTION
Indonesia's position as an archipelagic country with a very wide sea causes each region to have the potential to produce salt. The largest salt-producing island in Indonesia is Madura. Based on statistical data from the Ministry of Marine Affairs and Fisheries in 2018, Sampang Madura became the largest salt-producing district in Indonesia with a total production of 312,061 tons [1].
The Covid-19 pandemic has caused all sectors to be paralyzed, including salt farmers in Sampang Madura. The government has also made new policies regarding Large-Scale Social Restrictions, physical distancing and is currently entering the new normal [2].
This policy affects the market share in the sale and production of salt in Sampang Madura.
Various government program efforts (Marine and Fisheries Service) in helping salt farmers face the acceleration of handling Covid-19, namely through technology training programs, providing People's business credit assistance, providing appropriate technical assistance such as geomembranes to increase the amount of production and online marketing training [3]. The problem with this research is that many salt farmers have experienced a decrease in salt production and have difficulty selling salt products due to the impact of the Covid-19 pandemic, so a mapping model for salt farmers is needed to help run the office's work program during the Covid-19 pandemic and the 4.0 industrial revolution.
This mapping model is guided by several indicators, namely land area, production output, number of workers, ownership, operating profit, marketing system, market place. Some indicators contain information that is not clear, contains uncertainty and inaccuracies in the data, thus requiring appropriate decision-making methods. The purpose is to construct a hybrid interval type 3 FAHP-FTOPSIS GROUP DECISION-MAKING PROBLEMS 2 fuzzy AHP, TOPSIS method for mapping salt farmers. Fuzzy type-2 provides more accurate modeling, better rating value performance, and high flexibility [4][5] [6] [7].
Linguistic modeling interval type-2 fuzzy is clear and has preferred accuracy than fuzzy type-1 [8]. Determine entropy weight, AHP method has advantage of solving problem of many complex criteria into a hierarchical structure to get a suitable and easy-to-understand model [9][10] [11]. In testing expert or respondent judgments and optimizing multi-criteria decisions, the AHP method has advantages in testing the consistency of criteria assessment results [12][13] [14] [15]. The use of the consistency index has also been used in several previous studies on multi-criteria-based decisions to measure the performance of SMEs [15] [16] [17].
In determining selection and recommendation of salt farmers, TOPSIS method is capable and suitable for multicriteria decision-making based on the Euclidian distance method. TOPSIS has a calculation concept that is simple, easy to understand, and computationally efficient [18] [19]. AHP and TOPSIS methods have been widely used in solving decision-making problems. Several studies have used a hybrid method of AHP and TOPSIS to evaluate and select software quality [18][20] [21]. Several studies have used a hybrid method of AHP and TOPSIS to evaluate and select software quality [6] [21] [22]. Several journals use the fuzzy method to handle data that contains uncertainty and inaccuracies, fuzzy method is also hybridized with AHP, ANP, and TOPSIS for weighting and data selection [23] [24][14] [4]. This research uses a hybrid Fuzzy multi-criteria method and an optimization model using Mathematical Optimization Programming (MOP). FAHP was used for weighting allocation water, Fuzzy TOPSIS method was used to determine the best performance of various water resources [25].
Based on previous research, there has been no research on salt mapping using the interval fuzzy type-2 method, the indicators used are also different because they are adjusted to conditions and needs of the Sampang Madura area. The contribution of this research is mapping salt farmers using a decision-making model based on group by optimizing decisions based on a modification of the Triangular Fuzzy Number (TFN) interval point. The decisions made based on group decision-making have a higher consensus than individual decisions [10] [26]. Therefore, this study uses an integration of fuzzy type-2 AHP, TOPSIS method to determine the most influential indicators and recommendations for improvement salt farmer facing the Covid-19 pandemic.

Interval type-2 Fuzzy Sets
The fuzzy theory was first conveyed by Zadeh (1975). Fuzzy Interval Type 2 is an extension of fuzzy type-1. Fuzzy theory is also called fuzzy type-1, this theory can handle the problem of uncertainty in the data by having membership functions and linguistic variables. When compared to fuzzy type-1, interval type-2 fuzzy provides more accurate modeling, better rating value performance, and high flexibility. type-2 fuzzy is suitable for MCDM-based decision-making problems for real problems, can handle more judgments for data that has uncertainty and subjectivity. Type-2 fuzzy is widely applied in the real world because it contains computational stages that are easier to implement. Interval type-2 Fuzzy Sets A  in the universe of discourse X as follows: 2. The Subtraction operation of different Interval TFN C, K:    The decision-making model in interval TFN with different middle points where ml > mu point is shown in Figure 4.

Construction of K Matrix.
This is the interval type-2 FTOPSIS method step, while steps 1 to 5 are interval type-2 FAHP method. This step refers to number 2 based on the Interval Triangular Fuzzy Number model, alternative decision matrix against criteria denoted by K can be stated as follows: The distance each alternative from negative ideal solution is denoted by i d − which is expressed as follows: The average relative proximity is denoted by i RC which is stated as follows:  Table 1. By using the same middle point on the Triangular Fuzzy Number (TFN). The most important 5. After determining the membership function of fuzzy TFN type-2 interval and the linguistic scale, the next step is to conduct a questionnaire to 3 assessors, namely fisheries and marine service, the head of a farmer group, and the salt expert team. because this research is based on a support system group, this study uses 3 assessors, as shown in Table 2, Table 3, Table 4.
Next is to determine calculations of several raters using the Geometric Means Aggregation method, which is shown in Table 5. After determining Geometric Means Aggregation (GMA), proceed with calculating weighted normalization as in Table 6. The final step is the fuzzification process used to determine the weight of each Criteria in Table 7.      Based on Table 7 Table 8. The fuzzy type-2 interval membership function for alternative (Salt Farmer) in Figure 3. Determine same middle point linguistic scale for assessment of salt farmers can be seen in Table 9.       Table 11. Data for each salt farmer for criteria A3 and A4     Table 17. Negative Ideal Solution 10. Calculate the relative proximity distance. The relative proximity of the ideal solution and average relative proximity is denoted by i RC can be seen in can be seen in Table 18.

CONCLUSION
The conclusion of this research is that indicators most influence recommendation salt farmer are marketing models, production yields and profit. The integration of interval fuzzy type-2 AHP and TOPSIS is suitable for salt farmer map recommendations and type-2 fuzzy interval method has a yield range between one alternative and another which is smaller than fuzzy type-1. The results of recommendations salt farmer stated that 45% received good, 35% entered into moderate and 20 % entered into bad recommendations. This research can be developed with different case studies and method can also be developed using a trapezoid fuzzy type-2.


A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model

This paper develops a threshold-augmented dynamic multi-country model (TGVAR) to quantify the macroeconomic effects of the Covid-19 pandemic. We show that there exist threshold effects in the relationship between output growth and excess global volatility at individual country levels in a significant majority of advanced economies and several emerging markets. We then estimate a more general multi-country model augmented with these threshold effects as well as long term interest rates, oil prices, exchange rates and equity returns to perform counterfactual analyses. We distinguish common global factors from trade-related spillovers, and identify the Covid-19 shock using GDP growth projection revisions of the IMF in 2020Q1. We account for sample uncertainty by bootstrapping the multi-country model estimated over four decades of quarterly observations. Our results show that, without policy support, the Covid-19 pandemic would cause a significant and long-lasting fall in world output, with outcomes that are quite heterogenous across countries and regions. While the impact on China and other emerging Asian economies are estimated to be less severe, the United Kingdom, and several other advanced economies may experience deeper and longer-lasting effects. Non-Asian emerging markets stand out for their vulnerability. We show that no country is immune to the economic fallout of the pandemic because of global interconnections as evidenced by the case of Sweden. We also find that long-term interest rates could temporarily fall below their pre-Covid-19 lows in core advanced economies, but this does not seem to be the case in emerging markets.

economy. For an extensive survey of the latest theoretical developments in GVAR modelling and the numerous empirical analyses, see  and Chapter 33 of Pesaran (2015).
Our counterfactual results show that the pandemic could reduce the world real GDP by 3 percent below its modelgenerated path without the shock by the end of 2021. While China and other emerging Asian economies are estimated to be less severely affected, the United Kingdom, and several other advanced economies may experience deeper and longerlasting effects. Among non-Asian emerging market economies, however, the economic impact of Covid-19 varies substantially, depending on domestic factors (economic structures, health preparedness, and the scale of lockdowns) as well as external disturbances (plunging trade, collapsing tourism, volatility in capital flows, and fluctuations in commodity prices). There is a significant degree of uncertainty around all these counterfactual outcomes which we try to quantify (while abstracting from the large-scale fiscal support that followed later in 2020). Importantly, our findings underscore the role of spillovers which we quantify for the case of Sweden considering its different approach toward the pandemic (i.e., a less stringent strategy). We show that no country is immune to the economic fallout of the pandemic because of interconnections. We also estimate that the Covid-19 pandemic would lower long-term interest rates temporarily by about 100 basis points below their historical lows in core advanced economies. In contrast, the impact on long-term interest rates in emerging markets has a wide range, including significant upside risks.
Our paper also relates to a rapidly growing literature that investigates the macroeconomic effects of Covid-19. McKibbin and Fernando (2020) explore the global macroeconomic effects of different scenarios of how Covid-19 might evolve sing a hybrid DSGE/CGE model. They underscore the importance of spillover effects and show that even a contained outbreak could significantly impact the global economy in the short run. Bonadio et al. (2020) study the impact of Covid-19 on output growth in 64 countries and investigate the contribution of global supply chains to these adverse effects. Ludvigson et al. (2020) quantify the macroeconomic impact of Covid-19 in the United States using a VAR framework and a gauge of the magnitude of the Covid-19 shock in relation to past costly disasters. Baqaee and Farhi (2020) consider possible non-linearities in response to the Covid-19 shock in a multi-sectoral model. They develop a disaggregated structural model with input-output linkages, as well as downward nominal wage rigidities and a zero lower bound constraint on nominal policy rate to study the effects of supply and demand shocks associated with Covid-19. They demonstrate how these shocks are amplified or mitigated by nonlinearities, and quantify their effects using disaggregated data from the United States. Another paper which highlights the importance of nonlinearity is Céspedes et al. (2020). They build a threshold macroeconomic model of a pandemic and show how such a shock can have large magnification effects. Finally, Milani (2020) uses a GVAR model to underscore the importance of countries' interconnections in the evolution of Covid-19 and its unemployment effects.
The rest of the paper is organized as follows. Section 2 highlights the importance of threshold effects in the growth-global volatility relationship. Section 3 develops the threshold-augmented dynamic multi-country (TGVAR) model. Section 4 quantifies the macroeconomic effects of Covid-19 under uncertainty using TGVAR. Finally, Section 5 offers some concluding remarks. Additional results are provided in three appendices.

Threshold effects: global volatility and growth
Threshold effects have been used in the literature primarily in the context of autoregressions pioneered by Tong (1990). These models are known as threshold autoregressions (TAR) and allow the parameters of the autoregressive model to switch between two or more values. Extension of TAR models to multi-variate systems has been considered by Tsay (1998). Hansen (2011) provides a recent review of TAR models, and discusses their empirical applications in economics in areas such as growth dynamics, stock return volatility and forecasting.
Given the global nature of Covid-19, and to capture its non-linear economic effects, we focus on a global measure of realized volatility, and consider its possible impact on country-specific output growth. To measure global volatility, we use the following where w $ i is the PPP-GDP weight of country i, we index countries as i ¼ 0; 1; . . . ; n, and rve it is the country-specific realized volatility of equity returns during quarter t, computed from daily observations: where r it s ð Þ is the equity return during day s in quarter t in country i; r it ¼ D À1 t P Dt s¼1 r it s ð Þ, and D t is the number of trading days in quarter t. The country-specific realized volatility measures, rve it , have been used in the literature to investigate the effects of uncertainty on growth. Here we focus on a global volatility measure, as opposed to country-specific ones, to better capture the effects of global uncertainty, which is more akin to Covid-19.
One could use other measures of global volatility as well. For example, instead of averaging country-specific realized volatilities, we could first take the average of equity returns across countries and then compute the realized volatility of global equity. Let r gt s ð Þ be the global daily equity return defined by using PPP-GDP weights as before. Then realized volatility of global equity returns is given by A widely-used alternative measure of global volatility in the literature is the option-implied volatility, or VIX. While the VIX captures the stock market's expectation of volatility based on S&P 500 index options and is only available from 1990, our preferred measure of global volatility, grve t , is based on realized equity returns for a large number of countries and is available for the last four decades. Note that the correlation of grve t and VIX is 89.8%, therefore, grve t seems to follow the VIX index well but not too closely (see Fig. 1), thus capturing, to some degree, some of the volatilities that originate from emerging economies. Appendix B systematically compares these three measures of global volatility and shows that grve t is superior to other measures in terms of fit. 1 Therefore, in what follows, we report the results based on the grve t measure of global volatility.
We begin our econometric analysis with the following simple multi-country threshold-augmented dynamic output growth model: for i ¼0; 1; 2; . . . ; n; and t ¼ 1; 2; . . . ; T; where Dgdp it ¼ gdp it À gdp i;tÀ1 , is the first difference of the logarithm of real GDP for country i during quarter t, and Þis the global volatility threshold variable, I A ð Þ is an indicator variable that takes the value of unity if event A occurs and zero otherwise, grve t is the global realized volatility defined by (1), and c is a threshold parameter, assumed to be the same across countries under consideration. e it is the idiosyncratic error assumed to be serially uncorrelated with a zero mean. The above specification only allows for intercept shifts in output growth equations, thus treating the threshold variable, z tÀ1 c ð Þ, as another common factor, with the threshold parameter estimated by pooling across countries. Modelling other forms of threshold effects, including country-specific thresholds, will unduly complicate the modelling exercise and is beyond the scope of the present paper. We model the threshold effects with respect to lagged values of the realized volatility variable, and rely on standard common factor analysis to capture possible simultaneity between output growths and volatility. See Section 3.
The results of estimating Eq. (4) for advanced and emerging economies are summarized in Table 1. In addition to reporting the estimates of c and u i , we also report the estimate of the proportion of times that the threshold is exceeded, denoted by p. The threshold parameter, c, is estimated by grid search and is allowed to take different values for advanced and emerging economies. The estimate of c for advanced economies at 0:156 is slightly larger than the value of 0:129 estimated for emerging economies. Given these estimates, the global volatility threshold variable is statistically significant in 15 out of the 19 (80%) advanced economies in our sample. But the results are much weaker for emerging economies, with only 4 statistically significant effects out of the 14 emerging economies in our sample. Nevertheless, all statistically significant threshold affects were negative, suggesting that excessive global volatility is generally associated with lower output growth subsequently. There are a numbers of channels through which excessive global financial market volatility can adversely affect economic growth. They include higher precautionary savings by households, lower or delayed investment by firms due to increased uncertainty and weaker demand prospects, and a higher cost of raising capital owing to higher funding costs in a volatile environment. See, for example, Cesa-Bianchi et al. (2020) and the references therein. In normal conditions where global volatility is low (below the threshold c), financial markets are able to price the volatility risk, and therefore weaken the relationship between output growth and the volatility factor. But during periods of heightened volatility, that occur very rarely, it is difficult to price the volatility risk properly and it is thus more likely for excess volatility to show up as statistically significant in output growth equations.
Overall, while we manage to detect threshold effects in the output growth-global volatility relationship in many advanced economies (80 percent of cases), similar results for emerging markets are less pervasive (with threshold effects being statistically significant in 30 percent of cases) for the following reason. Compared to advanced countries, exposure to global equity markets is lower in emerging market economies (that is, they have less developed capital markets and rely more on the banking sector for credit intermediation). Nonetheless, in these countries output growth could well be non-linearly affected by localized events (e.g., natural disasters; banking, currency and sovereign crises) or external shocks (e.g., commodity price 1 See in particular Fig. B.1 in the Appendix, which compares the average Mean Square Errors (MSE) of the threshold-augmented dynamic output growth   (1), and c is the threshold parameter. The estimation sample is 1979Q2 to 2019Q4. Statistical significance is denoted by Ã ; y and à , at 10%, 5% and 1% levels, respectively. volatility and capital flow reversals), and be exacerbated by country-specific characteristics (internal and external imbalances). However, these types of threshold effects will not be captured by our econometric specification that focuses on the global realized volatility of equity returns that is more reflective of developments in advanced economies. 2 The above findings underscore the importance of allowing for threshold effects in studying the macroeconomic consequences of Covid-19-which, considering its unprecedented nature, generated a sharp tightening in global financial conditions (Fig. 1). Specifically, global equity markets sold off sharply through mid-March 2020 as the pandemic spread across the world. From end-2019 to trough, the S&P 500 index in the United States fell 30 percent. Stock prices in other major economies experienced declines of similar magnitude and flight to safety resulted in sharp capital outflows from emerging market economies. Notwithstanding the improvement in global risk sentiment since March amid large-scale liquidity injections by major central banks and a gradual relaxation of lockdowns in many countries, output recovery is expected to be gradual with GDP growth in many economies expected to be negatively affected by the previous bout of financial market volatility (Fig. 2).

A threshold augmented GVAR (TGVAR) model with global latent factors
In what follows, we study the implications of Covid-19 for the global economy. In doing so we build on the GVAR literature and develop a threshold-augmented dynamic multi-country framework, which we refer to as Threshold-augmented GVAR, or TGVAR for short. The proposed modelling framework takes into account both the temporal and cross-sectional dimensions of the data; real and financial drivers of economic activity; interlinkages and spillovers that exist between different regions/countries; and the global common factors, as well as network effects (e.g., through trade linkages). This is crucial as the impact of shocks (and importantly that of Covid-19) cannot be reduced to a single country but rather involves multiple regions/countries, and this impact may be amplified or dampened depending on the degree of openness of the countries (both trade and financial) and their economic structures. Informed by the results in Section 2, we also allow for threshold effects in the output growth equation arising from global financial market volatility. Moreover, in contrast to the standard GVAR models that rely on trade-weighted averages to capture both local and global effects, we treat these effects separately. Before describing our model specification, we provide a short exposition of the methodology and data.

Data and variables
We consider a world economy composed of n þ 1 interconnected countries. Our focus is to model output growth and its responses to common shocks, either directly or indirectly through equity and bond markets. Specifically, for each economy i, we include the logarithm of real GDP (gdp it ), nominal long-term interest rate (lr it ), the logarithm of real equity prices (eq it ), and the logarithm of the real exchange rate (the nominal exchange rate deflated by the consumer price index), ep it . Data on these variables are obtained from the updated GVAR data set which includes 33 countries and covers the period 1979Q2 to 2019Q4. For a detailed description of data sources and related transformations see Mohaddes and Raissi (2020).
To avoid highly persistent variables we work with first-differences and denote the endogenous country-specific variables by The U.S. economy is denoted by i ¼ 0, with the remaining economies indexed by i ¼ 1; 2; . . . ; n, in no particular order. For estimation we suppose that y it is available over the period t ¼ 1; 2; . . . ; T, although the bond and equity variables, Dlr it and Deq it , are not available for some of the countries in our dataset. Therefore, the dimension of y it , which we denote by k i , differs across countries. Let k ¼ P n i¼0 k i , and collect all country-specific variables in a single k Â 1 vector Given the focus of our analysis, we augment the standard GVAR model with observed and unobserved external common factors. We consider changes in log oil prices, Dpoil t , and global volatility, grve t , as common observed factors and include them in the 2 Â 1 vector In addition, we use the weighted cross-sectional averages of the following four variables to capture unobserved common factors: where gdp i is the PPP-GDP weight of country i, and similarly lr One could also apply the principal component analysis (PCA) to y t , but that would involve estimating 2 As a robustness check, we are able to detect the presence of threshold effects in a larger number of economies if we start the sample from 1990Q1. See Appendix B for the estimation results and other details. To link the global aggregates to the country-specific variables, we introduce a k Â k weights matrix, W $ , and define it such thatỹ In addition to global aggregates, we also allow for transmission of shocks through the trade channel and consider countryspecific trade-weighted averages are the trade weights, j ¼ 0; 1; . . . n; w ii ¼ 0, and P n j¼0 w ij ¼ 1.For empirical application, the trade weights are computed as three-year averages: 3 where T ijt is the bilateral trade of country i with country j during a given year t and is calculated as the average of exports and imports of country i with j, and T it ¼ P n j¼0 T ijt (the total trade of country i) for t ¼ 2014; 2015 and 2016, in the case of all countries. A similar procedure is followed for the construction of Dlr Ã it ; Deq Ã it , and Dep Ã it . As it is well known in the GVAR literature, the country-specific aggregates y Ã it relate to y t through the following links where W i is the k i Â k matrix of trade weights for country i. The main difference between the two sets of aggregates,ỹ t and y Ã it , for i ¼ 0; 1; . . . ; n, is the weights used in their construction. These two variables are likely to be highly correlated contemporaneously. The reason for considering both aggregates (cross-section averages) is to distinguish the effects of global factors from local (trade related) effects, captured by countryspecific trade linkages. Also to avoid multi-collinearity and related identification problems, we include only lagged countryspecific cross-section averages, y Ã i;tÀ1 , and rely on global cross-section averages,ỹ t , to capture contemporaneous effects of global factors.

Global and individual country specifications
We specify the following equations for g t andỹ t 3 As an additional robustness check, we also re-estimated the model using weights based on data on a longer period from 1990-2016 and obtained very similar results. These results are not reported here but available upon request. Let f t ¼ g 0 t ;ỹ 0 t À Á 0 , and write (11) and (12) as where is the vector of reduced form global shocks, with We consider the following country-specific threshold-augmented models: The country-specific models in (14) are intended to capture the individual country VAR dynamics as well as common factors, f t , and local effects, y Ã i;tÀ1 . We have also confined the threshold variable, z tÀ1 c i ð Þ, to affect the intercepts only, but we do allow for these effects to vary across countries. These are pragmatic choices that we had to make to keep the model sufficiently parsimonious so that to obtain reasonably reliable estimates of the unknown parameters. We expect the threshold event, namely periods where grve tÀ1 > c i , to be quite rare and therefore to be most important in terms of its direct effect on output growth, which is best characterized by a shift in the intercept term. The possible effects of such rare events on growth persistent is likely to be of second order importance. Moreover, we limited the heterogeneity in c i by estimating only two threshold values, one pooled value for advanced economies and one for emerging markets.
We allow the country-specific error vectors, u it , to be cross-sectionally weakly correlated. Adding non-granular (local) cross-section averages, of the dependent variables, y Ã i;t , also known as spatial lags, to the right side of (14) is common in the spatial literature, where the contemporaneous correlation (after controlling for lags and common factors) is fully parametrized by introducing spatial lags, which, if specified correctly, would then identify geographical origins of the shocks. We do not attempt to fully parametrize the cross sectional correlation of u it . For this reason, we only include lagged values of y Ã it in the country-specific models.
Another important reason for why we do not include y Ã it as a regressor is the fact that, for a number of countries, some trading partners have a large weight in country-specific trade-weighted averages y Ã it . For example, U.S. is the dominant trading partner of Canada, with its trade weight exceeding 70 percent of the total. In such cases, we would expect that contemporaneous values of y Ã it to be correlated with u it , even if n ! 1. Including non-granular contemporaneous cross-section averages of the dependent variable in (14), would consequently result in inconsistent least squares estimation, owing to correlation between u it and the non-granular cross-sectional averages (spatial lags) even when n is large, and a full maximum likelihood or GMM type estimation will be required.
Eqs. (11) and (12) are specifications for global variables and global aggregates. Eq. (14), for i ¼ 0; 1; . . . ; n, relates to country specific models. Eq. (15) links the threshold indicator with the vector of global variables. Moreover, we have two additional linking equations. Eq. (8) links global aggregatesỹ t with y t , and links the vector of country-specific trade-weighted averages y Ã t ¼ y Ã0 0;t ; y Ã0 1t ; y Ã0 2t ; . . . ; y Ã0 n;t 0 to the full k Â 1 vector of endogenous variables, y t , and

The TGVAR representation
Substituting (13) for f t in (14), we obtain Note that the substitution of the global model (13) for f t in the country-specific models (14) avoids the possibility of the rank-deficiency problem discussed in Section 4.1 of . In addition, it allows for an error structure that explicitly features common and idiosyncratic shocks. Our approach differs from the earlier GVAR models in the literature that only rely on the conditional country-specific equations in (14) and the marginal equation for the observed common variables g t in (11). In contrast, (17) utilizes the Eq. (12) forỹ t as well. This approach is also adopted by Cesa-Bianchi et al. (2020) and Chudik et al. (2020). Stacking (17) for i ¼ 0; 1; 2; . . . ; n, we obtain where d ¼ d vector of threshold indicators, and Substituting identity (16) for y Ã tÀ1 in (18 ), we have and substituting (8) forỹ t , we obtain Substituting identity (8) forỹ tÀ1 in equations for g t in (11), we have Stacking (19) and (20), we obtain the following TGVAR representation for the full set of observables, the k þ 2 ð ÞÂ 1 vector where c ¼ d where e t is a vector of reduced form shocks, composed of global (v t ) and idiosyncratic shocks (e t ). Given our focus on the output effects of the Covid-19 shock, and to keep the analyses empirically manageable, we consider the effects of the threshold variable on the output growth variables only, and accordingly set k y;i ¼ k Dgdp;i ; 0; 0; 0 À Á 0 .
Without loss of generality, we identify advanced economies by i ¼ 0; 1; . . . ; n a and the emerging market countries by i ¼ n a þ 1; n a þ 2; . . . ; n. Moreover, currently available time series observations do not allow for the estimation of countryspecific threshold effects which capture rare events. Thus, we can only estimate two threshold parameters, distinguishing between advanced and emerging economies:

Identification of the Covid-19 shock
Identifying the economic effects of Covid-19 is not a straightforward task as our historical sample is not informative about such a shock. Also considering the truly global nature of the pandemic, it is not possible to compare economic outcomes from countries affected by Covid-19 with a control group that has not been buffeted by the Covid-19 shock. At best, we could compare economic outcomes across countries that followed different strategies to mitigate the spread of the pandemic. This approach is also limited as most countries have followed very similar approaches (for example, lockdowns) with just a few exceptions (such as Sweden which we consider below), and they differ mainly in terms of the timing of the implemented social distancing policies (for example, Germany and the United Kingdom). Here we adopt a historical country-specific approach and identify the Covid-19 shock by comparing the IMF's GDP projections for Q1 to Q4 of 2020 formed at the end of 2019-before the spread of the pandemic and when no one (including the IMF) could have predicted the global economic disruption that was brought about by the pandemic-to the same projections prepared in April 2020. We attribute the IMF's GDP projection revisions in 2020Q1 to the Covid-19 shock. In effect, we assume that the short-term IMF projections at the end of 2019 were free from systemic bias and can be used as potential outcomes in the absence of the Covid-19 shock. We believe this is a reasonable identifying assumption, in view of the unprecedented nature of the Covid-19 pandemic. Accordingly, we assume that up to 2019Q4 (t ¼ 1; 2; . . . ; T), e t is given by (23), but for Q1 to Q4 of 2020, it is given by e Tþq ¼ x Tþq þ Cv Tþq þ e Tþq ; for q ¼ 1; 2; 3; 4; ð26Þ where x Tþq corresponds to the Covid-19 shock in the period T þ q. We assume x t ¼ 0 for t 6 T, but it is nonzero for To identify x Tþq we will use the size of IMF's projection revisions at the end 2020Q1.

Let Dgdp
April i;Tþq be the April 2020 IMF World Economic Outlook (WEO) GDP growth projections for country i in quarters q ¼ 1; 2; 3; 4 of 2020, and Dgdp Jan i;Tþq be the associated January 2020 IMF WEO projections. We compute the April-January projection revisions as j i;q ¼ Dgdp April i;Tþq À Dgdp Jan i;Tþq ; for q ¼ 1; 2; 3; 4; and i ¼ 0; 1; 2; . . . ; n. Assuming that developments surrounding Covid-19 were the only dominant developments behind the projection revisions, we use j q ¼ j 1;q ; j 2;q ; . . . ; j n;q À Á 0 , for q ¼ 1; 2; 3; 4 to infer x Tþq . Note that growth outturns in 2020 were subsequently affected by the massive scale-up of policy actions by governments (for example, global fiscal support was increased from $3.3 trillion in April 2020 to $16 trillion in April 2021). To the extent possible, we try to abstract from these factors in our counterfactual analysis to identify the Covid-19 shock from policy responses to mitigate its effects. For this reason and as expected, the actual growth outturns in 2020 (reported in Table 2) were somewhat different from the April 2020 growth projections of the IMF that served as our baseline. 4

Using IMF projections to infer x Tþq
Let us define S as the matrix that selects all output growth variables from the vector x t , namely Sx t ¼ Dgdp t ¼ Dgdp 0t ; Dgdp 1t ; . . . :; Dgdp nt ð Þ 0 : We set individual elements of x Tþ1 that correspond to GDP to be given by the corresponding j i;1 , and use the historical correlations of the reduced form errors to estimate the remaining elements. This yieldŝ x Tþq for q ¼ 2; 3; 4 are computed recursively aŝ In (27) and (28), we infer the sequence of shocksx Tþq ; q ¼ 1; 2; 3; 4, using revisions in the IMF projections for the paths of GDP and the corresponding predictions of our global model. Specifically, we define the Covid-19 effects by the difference where x c Tþh is a counterfactual realization of the global economy after the Covid-19 shock hit the economy, namely x Tþj ¼x Tþj È É 4 j¼1 , and x 0 Tþh ¼ E x Tþh jI T ð Þis the conditional expectation of global economy without the Covid-19 shock, conditioning on the information I T ¼ x T ; x TÀ1 ; . . .

The effects of Covid-19 on economic activity and long-term interest rates
Our model includes 33 economies, which together cover more than 90% of world GDP, see Table 3. For presentation of our results, we create a euro area block comprising 8 of the 11 countries that initially joined the Euro in 1999: Austria, Belgium, Finland, France, Germany, Italy, Netherlands, and Spain. The time series data for the euro area are constructed as crosssectionally weighted averages of the domestic variables, using Purchasing Power Parity GDP weights, averaged over the 2014 to 2016 period. In addition to the euro area we also consider a further seven regions, see Table 3.  comes given the constellation of shocks experienced over the past four decades. Note that the median impulse responses for 2020Q1-Q4 match the April 2020 growth projections of the IMF by design. Uncertainty around these projections are pervasive because of the severity and duration of the Covid-19 pandemic, global spillovers, financial market volatility, the efficacy of policy actions to protect firms and households, and the success of pharmaceutical interventions to contain the spread of the virus. Overall, the Covid-19 pandemic would leave the 2021 GDP about 3 percentage points lower than the model-generated forecast of global GDP in the absence of Covid-19. The adverse impact on Advanced Economies is particularly large-ranging from 2 percentage points below pre-crisis path of GDP by the end of 2021 in the euro area to 6.5 percentage points in the United States. 6 Among emerging markets, however, the economic impact of Covid-19 varies substantially. In addition to domestic shocks (health crisis and lockdowns), these countries faced a range of external shocks in early 2020 as well (plunging trade, collapsing tourism, capital outflows, falling commodity prices), albeit to varying degrees, and have different economic structures (those relying heavily on certain sectors are naturally more vulnerable to the adverse macroeconomic effects of the pandemic). China appears to be an exception largely because most of the country had reopened by early April and its lower size of inward spillovers. Emerging Asia excluding China is expected to be less affected by Covid-19 than Latin America. This is partly due to higher commodity dependence of the latter and tighter financing conditions initially, as well as being less successful is containing the pandemic.   Fig. 4 reports the results of our counterfactual exercise for real GDP of Sweden under two scenarios: (i) growth shock in all 33 countries in our sample arising from the Covid-19 pandemic and (ii) growth shock in all countries except for Sweden. Comparing the two sets of results highlights the importance of spillovers through disruptions in global supply chains, travel, and tourism. The results in Fig. 4 illustrate that no country can shield itself from the adverse economic effects of Covid-19 by following less stringent lockdowns.

Real GDP effects are large and persistent
Figs. 5 and 6 report the results of our counterfactual exercise for the evolution of real GDP following the Covid-19 shock relative to the 2019Q4 output levels rather than the model-generated forecasts of real GDP in the absence of Covid-19. In other words, we assess how long it will take for different countries/regions to return to their 2019Q4 real GDP levels following the Covid-19 shock. Informed by the April 2020 vintage of the IMF growth projections and our counterfactuals, global activity is estimated to trough in the second quarter of 2020, recovering thereafter. The estimated recovery in global activity is mainly driven by Asian economies (most notably China) while the United States and the United Kingdom are less likely to recuperate income losses by the end of 2021 (that is, their GDP is projected to end 2021 below 2019Q4 levels by about 2 and 1.5 percent with high probability, respectively). Consistent with the results in Fig. 3, China's real GDP is expected to recover rapidly the lost ground, while non-Asian emerging economies are likely to take longer to achieve a full recovery. In all cases, there is a significant amount of uncertainly around the counterfactual GDP paths. See also Table 4 for likely growth outcomes. Note that these growth outcomes will be significantly worse if one were to use the updated projection revisions of the IMF in June 2020 to gauge the size of the shock rather than the April vintage (see Appendix C for details). This is because in June, the IMF updated its GDP projections based on more information about the pathway of the pandemic and the intensity and efficacy of containment efforts. The IMF, therefore, downgraded its projections of growth in the first half of 2020 and portrayed a more gradual recovery thereafter than previously projected.

Long-term interest rates face downward pressures
Global public debt is expected to reach its highest recorded level (about 100 percent of global GDP and higher than the post-World War II peaks) partly driven by the massive fiscal response to the Covid-19 pandemic (9.2 percent of 2020 GDP in additional spending and foregone revenue globally as of April 2021). At the same time, the 10-year government bond yields are at their historical lows and negative in several advanced economies (Fig. 7). To the extent that borrowing costs stay at low levels, they make it easier to service public debt. Fig. 8 reports the results of our counterfactual exercise for long-term interest rates between 2020Q1 and 2021Q4. For advanced economies (especially those that are perceived to be safe havens), long-term interest rates are estimated to temporarily fall even below their pre-Covid levels as the crisis raises precautionary savings and dampens investment demand. However, the same cannot be said about emerging market economies where borrowing rates can increase rapidly as shown by the upper range of our counterfactual exercise in Fig. 8. Among safe havens, Japan appears to be an exception possibly driven by Bank of Japan's policy of yield-curve control.  These predictions are largely borne out by actual data. For example, in the first half of 2020, 10-year government bond yields in the United States, the United Kingdom, and Germany fell by 111, 44, and 10 basis points, respectively, and that of Japan increased by 10 basis points-all within our counterfactual ranges displayed in Fig. 8. Among emerging markets, South Africa experienced a 137 basis points increase in its 10-year government bond yields during the first half of 2020again within our prediction ranges.

Concluding remarks
The Covid-19 pandemic has been a shock like no other, initiating simultaneous demand and supply disruptions. In addition, it led to a sharp tightening in global financial market conditions during the first quarter of 2020. Using quarterly data over the past four decades, we first showed that global financial market volatility (beyond certain thresholds) can adversely affect economic growth in a significant majority (80 percent) of advanced countries and for several emerging market economies. We then developed a threshold-augmented dynamic multi-country model, or TGVAR for short, to study the global macroeconomic effects of Covid-19. We identified the country-specific Covid-19 shocks by comparing IMF's GDP projections for Q1 to Q4 of 2020 formed at the end of 2019 (before the spread of the pandemic) to the same projections prepared in April 2020. Finally, using the TGVAR model we quantified the range of likely macroeconomic outcomes following the Covid-19 shock.
Our results showed that the Covid-19 pandemic could lead to long-lasting declines in world real GDP with varied effects across regions/countries. The impact of Covid-19 on the UK, and several other advanced economies could be particularly severe, while China and other emerging Asian economies are estimated to fare better. We also estimated that the Covid-19 pandemic can temporarily lower long-term interest rates below their pre-Covid-19 lows in core advanced economies, but the reverse outcome is predicted for emerging markets, with implications for debt servicing costs. Our findings highlighted the importance of policy interventions to restore the normal functioning of financial markets, as well as adopting other measures (fiscal and liquidity) that can limit bankruptcies of viable firms and support incomes of households. These measures would likely limit the amount of scarring.
Note that the pandemic resurfaces in waves-that is, with every easing of social distancing restrictions, the infection rates rise again, requiring the re-imposition of local restrictions, as we saw in Europe and the US in late 2020 and India latelydampening economic activity and confidence. Appendix C shows how more up-to-date information about the pathway of the pandemic (considering its rarity) can affect our predictions.

Appendix B. Threshold effects
This appendix provides additional results on the choice of the global volatility measure (Section B.1) and and on the robustness of the threshold effects to the choice of the sample period (Section B.2).

B.1. Alternative measures of global volatility
In addition to our preferred measure of global volatility, or grve t -given by (1)-, we consider the realized volatility of the global equity returns, or rvge t -given by (3)--, and the VIX index, vix t (http://www.cboe.com/vix). The VIX index is available on the CBOE website from 1990. 9 To differentiate among these measures, we compute the average of squared residuals (MSE) of the threshold-augmented dynamic output growth models Dgdp it ¼ c i þ q i Dgdp itÀ1 þ u i z tÀ1 c ð Þ þ e it . We plot the minimized MSE for different values of the threshold parameter (c). We consider indicator-specific grids of thresholds defined by the set S p;T ¼ j=T; p min < j=T < p max f g ; namely, the indicator-specific grid of thresholds is given by 1 À p ð Þquantiles of the given indicator, p 2 S p;T . The results clearly show that the grve t measure performs best in terms of MSEs in all cases considered, and is preferred over the other two measures. Note that the underlying regression models contain the same number of estimated parameters and as a result the MSE outcomes across the three measures are comparable. Therefore, we conclude that data speaks in favor of grve t , which happened to be our choice based on our a priori arguments spelled out in the paper.

B.2. Robustness of threshold effects to the choice of sample period (1990Q1-2019Q4 subsample)
We also investigate the robustness of our threshold effects to the choice of time period. To this end we consider the subsample 1990Q1-2019Q4 which excludes the 1987 stock market crash whose macroeconomic impact was rather short lived, avoids breaks in error variances due to the so called ''Great Moderation", and better captures the trade and financial market globalization of emerging economies that gathered pace post 1990. The results, in Table B.1, show that in 16 advanced economies and 5 emerging markets (out of 19 and 14 countries in each income group, respectively), global volatility (grve t ) (i.e., beyond the threshold c) is associated with lower output growth subsequently. While the estimate of c remains unchanged, we are able to detect the presence of threshold effects in a larger number of economies. 9 Historical VIX data can be downloaded from here: https://www.cboe.com/tradable_products/vix/vix_historical_data/. This data is subject to a change in methodology that occured in September 22, 2003. Appendix C. Counterfactual analysis using June-January IMF projection revisions The IMF revised its growth projections down for most countries in June as the extent of the Covid-19 shock and its economic fallout became clearer. In this appendix, we use these updated projections to gauge the size of the GDP shock arising from the Covid-19 pandemic. Let Dgdp June i;Tþq be the June 2020 IMF World Economic Outlook (WEO) GDP growth projections for country i in quarters q ¼ 1; 2; 3; 4 of 2020, and Dgdp Jan i;Tþq be the associated January 2020 IMF WEO projections. We compute the June-January projection revisions as j i;q ¼ Dgdp June i;Tþq À Dgdp Jan i;Tþq ; for q ¼ 1; 2; 3; 4; and i ¼ 0; 1; 2; . . . ; n. Assuming that developments surrounding Covid-19 were the only dominant developments behind the projection revisions, we use j q ¼ j 1;q ; j 2;q ; . . . ; j n;q À Á 0 , for q ¼ 1; 2; 3; 4 to infer x Tþq and repeat the counterfactual analysis in Section 3. The results are reported in Figs. C.1,C.2,C.3 and Table C.1. While the new estimates are qualitatively similar to those reported in the main text, they indicate worse growth outcomes and longer recoveries quantitatively. Notes: Our threshold-augmented dynamic output growth model is given by Dgdp it ¼ c i þ q i Dgdp itÀ1 þ u i z tÀ1 c ð Þ þ e it , where Dgdp it is the first difference of the logarithm of real GDP in country i during quarter t and zt ¼ I grve t > c ð Þ .I A ð Þ is an indicator variable that takes the value of unity if event A occurs and zero otherwise. grve t is a measure of global volatility defined by (1), and c is the threshold parameter. The estimation sample is 1990Q1 to 2019Q4.