ZAKAT: CONCEPT AND IMPLICATIONS TO SOCIAL AND ECONOMIC (ECONOMIC TAFSĪR OF AL-TAWBAH:103)

1. Post Graduate School, Universitas Airlangga, Airlangga 4 – 6, Surabaya, Indonesia, alfaiz165@gmail.com 2. Post Graduate School, Universitas Airlangga, Airlangga 4 – 6, Surabaya, Indonesia, tdindainsani@yahoo.com 3. Post Graduate School, Universitas Airlangga, Airlangga 4 – 6, Surabaya, Indonesia, sriherianingrum@feb.unair.ac.id Received : September 22, 2017 Revised : April 10, 2018 Accepted : May 1, 2018 ABSTRACT

I. INTRODUCTION
Zakat is an important principle in the islamic economics. Zakat in the Islamic economics has two main functions. First, as a tool of worship which giving the individual benefit to the person paying zakat. Second, giving the collective benefit for people in the environment which run the system of zakat (Sakti 2007).
A large body of literatures have attempted to discuss and examine the impact of zakat on poverty by employing modern economic tools (see : Ibrahim, 2006;Mila Sartika, 2008;Irfan Syauqi Beik, 2009;Firmansyah, 2013;Rina Murniati and Irfan Syauqi Beik, 2014;Irfan Syauqi Beik and Caesar Pratama, 2016). On the other hand, Islam has its own scientific khazanah such as Tafsir to explore a concept from the main source, Quran and hadith. Tafsīr is a method used by Islamic scientist in order to explore a concept and wisdom contained in the Quran.
This paper therefore aims to explore the concept and implication of zakat for social and economy by using method that have not been used by the majority of researchers i.e. Tafsīr method. In this case, the object of research is sūrah al-Tawbah: 103, which verse has more general content than other verses related with the concept and function of zakat.
This paper analyzed the meaning of Lafaḍ mentioned in sūrah al-Tawbah: 103. From that semantic analysis, the authors explore some concepts that exist in sūrah al-Tawbah: 103. This analysis refers to some book of tafsīr both of classical and contemporary books. Similarly, the authors combine the tafsīr bi al-ma'thūr book and tafsīr bi al-dirāyah book.
Based on the background above, the objectives research of this paper are to analyze how the concept of zakat based on sūrah al-Tawbah : 103 is; and how the implication of zakat for the social and economic equality is.

II. LITERATURE REVIEW
This section consists of theory and previous studies that support the research.

Theory 2.1.1 Zakat and Its Position in Islam
Zakat means (blessing, growth, holy, and kindness). While in terminology, zakat has the meaning of a certain part of the treasure that has been required by Allah to be given to the people who are entitled to receive it (mustahiqq). That treasure which taken is called zakat because it can increase its principal treasure and keep it from various danger (Qarḍawī 1973).
Zakat is perceived highly in Islam since it is one of the pillars which build Islam. This is as mentioned by Rasulullah: "Islam is based on (the following) five (principles): Syahadat Laa ilaaha illa Allah and (syahadat) Muhammad Rasulullah, to offer the (compulsory congregational) prayers dutifully and perfectly, to pay zakat, to perform Hajj, and to observe fast during the month of Ramadhan" (HR Bukhari) Allah pairs the command to pay zakat with the command to pray as much as 22 times in Quran. This indicates the crucialty of zakat in Islam. In addition, zakat is a unique and complete system about redistribution (Rivai 2013). Islam regulates quite clearly about zakat, therefore it takes a study to understand the function of zakat, so in the implementation zakat become an obligatory system not voluntary system.

Concept and Methodologi of Tafsīr
Tafsīr in epistemology means al-Īḍāh and al-Tabyīn (clarifying and explaining) (Ayyūb 2004). While in terminology means the study of explanation about the meaning contained in Quran and explore the laws, wisdoms, mau'iẓah and lessons in it (Yūnus 2002 Among the forms of tafsīr bi al-Ra'yi is interpret the Quran with the modern science such as astronomy, medical, economic, and management. This kind of interpretation leads to a lot of controversy among the Ulamā'. Some are allowed and some are prohibited. Ulamā' who forbid this interpretation are Shaikh Shaltūt and Sayyid Quṭb. On the other hand, Ulamā' who enables the usage of science to interpret meanings behind the Qur'ain verses are imam al-Ghazālī and al-Suyūṭī (Qarāwi 2002). According to al-Ghazāli, generally all the science is included in the deeds and the traits of Allah. While, the Quran explain the substance, deeds, and traits of Allah. And the science is not final. In Quran, there is only a global signal to the science.
In these case, there are some things that need attention for people who want to interpret Quran with science, that is (Qarāwi 2002): 1. Hold on to scientific facts not hypotheses. This is very important because when interpret Quran with hypothesis, the interpretation made will vary according to the existing hypothesis. 2. Stay away from self-imposed in understanding of naṣ. In this case, we are forbidden to impose a Naṣ of Quran with the meaning we want to conclude. However, we should take some meaning which compatible with the language and the existing Naṣ editorial flow. 3. Avoid to accusing people are fools. 4. When we interpret Quran with science, do not ever consider Islamic ummah or former Ulamā' are stupid for not be able to interpret as we do.

Previous Research
There are several researches conducted related to the impact of zakat. First, research conducted by Ibrahim (2006) entitled "Economic role of zakat in reducing income inequality and poverty in selangor". The subject of this research is the recipient of zakat in Zakat Selangor Center on nine regencies in selangor, while the object of research is the inequality of income and poverty. By using the Lorenz curve test kits, The Gini coefficient, and the Atkinson index and the five major poverty indices, the result shows that zakat can reduce income inequality and poverty (Ibrahim 2006). Second, a research conducted by Mila Sartika (2008) entitled "Pengaruh pendayagunaan zakat produktif terhadap pemberdayaan mustahiq pada LAZ Yayasan Solo Peduli Surakarta". The method used in this study is quantitative with the simple regression tools. This research makes mustahiqq with livestock assistance at LAZ Foundation Care Solo as the subject of research, while the object is the income level of mustahiqq. Based on the results of this study, it can be seen that there is a significant influence between the amounts of funds disbursed to income of mustahiqq. So the higher the funds disbursed, the higher of the income generated by mustahiqq (Sartika 2008).
Third, a research conducted by Irfan Syauqi Beik (2009) entitled "Analisis peran zakat dalam mengurangi kemiskinan: studi kasus dompet dhuafa republika". The subject of this research is 50 mustahiqq at the Institute of Amil Zakat National Dompet Dhuafa Republika, while the object of this research is the level of poverty. This study uses several analysis tools, including head count ratio to find out the number and percentage of poor families, the ratio of poverty gap and the gap of income to know the depth level of poverty, and the index of sen and Foster Greer and Thorbecke (FGT) to measure the severity of poverty. Through this research, it can be seen that zakat able to reduce the number and percentage of poverty, depth and severity of poverty ( irfan Beik 2009).
Fourth, a research conducted by Firmansyah (2013) about "zakat as an instrument of poverty alleviation and income gap". The method used in this study is descriptive qualitative in which discuss three studies about the role of zakat for mustahiqq. In the first research by Beik (2010) conducted a study of 1195 households in the province of DKI Jakarta. The result of this research is mustahiqq income up to 9.82 percent which in general can improve the standard living of mustahiqq. Besides, the result of this study is the zakat able to reduce the poverty rate of mustahiqq by 16.8 percent. Second research conducted by Beik et al (2011) using the study subjects of 1,639 mustahiqq from five different provinces. It is known that 21.11 percent of mustahiqq are able to move away from the poverty line. In a third study conducted by Mintarti et al (2012), using 821 poor households from 4,646 zakat recipient in jabodetabek as the subjects. The result is a poverty rate decrease to 10.79 percent. Based on the discussion conducted by Firmansyah, it can be concluded that productive zakat can give the impact in the form of poverty reduction and minimize the income gap (Firmansyah 2013).
Fifth, a research conducted by Rina Murniati and Irfan Beik in 2016 about the influence of zakat on human development index and poverty level of mustahiqq, case study about empowerment BAZNAS Bogor city. The subject of this study is BAZNAS Bogor city, while the object of this study is the level of income and poverty level of mustahiqq. By using statistical t tools, it is found that the utilization of zakat decreases the poverty level of mustahiqq and gives an effect on the income level of mustahiqq (Rina Murniati 2016).
Sixth, a research conducted by Irfan Syauqi Beik and Caesar Pratama (2016) titled "Zakat impact on poverty and welfare of mustahiqq: CIBEST model approach". The research subjects were 121 mustahiqq households from four villages in Bogor district, while the object of the study was the condition before and after receiving the productive zakat funds. This study uses the CIBEST index to measure the material and spiritual side. The results of this study, on the index of the material poverty decrease by 49.6 percent, the index of the spiritual poverty decreased by 1.6 percent, the absolute poverty index decreased by 12.3 percent, and the welfare index increased by 63.7 percent. In other words productive zakat has a positive impact in reducing the poverty of mustahiqq households (I. S. Beik and Pratama 2016).

III. METHODOLOGY
The methodology used in this paper is descriptive qualitative methodology with content analysis approach. According to Berelson (1952) in (Stemler 2001), content analysis can be systematically defined as a replication technique used to clarify words from a text into several payload categories, based on the implicit rules. Meanwhile, according to Holsti (1969) in (Stemler 2001), content analysis is defined as a technique for interpreting and identifying a message characteristic specifically, objectively and systematically.
In approaching that content analysis, the authors use a method of tafsīr that has been used by Ulamā' to explore the laws and wisdom from Quran. In this case, the authors combine between tafsīr bi al-ra'yi and tafsīr bi al-ma'thūr. This refers to the opinion of al-Suyūṭī and al-Qarḍāwī which described before, that the interpretation with modern science should still refer to the naṣ and tafsīr bi al-ma'thūr as well. Therefore, in this research the authors explore some of the leading tafsīr literary, both classical and contemporary tafsīr. The classical tafsīr books that the authors used as reference are Tafsīr

IV. RESULT AND ANALYSIS
The object of this research is al-Tawbah: 103, that is "Take Sadaqah (alms) from their wealth In order to purify them and sanctify them with it, and invoke Allâh for them. Verily! Your invocations are a source of security for them, and Allâh is All-Hearer,  In sūrah al-Tawbah: 103 above, there are two interesting sentences for our analysis. First, the sentence of (Take alms of their wealth). Second, the sentence of (mayst purify them and sanctify make them grow). In the first sentence contains the concept of zakat, while in the second sentence contains the function of zakat. The second sentence in sūrah al-Tawbah: 103 above have a very strong connection with the first sentence. It has meaning that the function of zakat which describe in second sentence will run well if the concept of zakat described in first sentence well implemented.

The Concept of Zakat
In sūrah al-Tawbah: 103, Allah started with Lafaḍ (take) which is the form of the command word (fi'il amr). According to al-Rāzī in his tafsīr book Mafātīh al-Ghaib, the use of the command word in the form (take) as a ḍāhir show that the taking of zakat from muzakkī is obligatory (Rāzi n.d.). The opinion of al-Rāzī is in accordance with uṣul al-fiqh method which states that the word of command must be directed to the mandatory law as long as there isn't other Dalīl that directs the order to other than mandatory law.
The object (mukhāṭab) of the command word (take) in that ayah is Rasulullah who commanded by Allah to take zakat from the rich (Dimashqi 1999). However, although the mukhāṭab (object) of the command in the ayah is Rasulullah, but the command also applies to all judges or authority after him (Zuhailī 1998). The context of the command to the Rasulullah in the ayah is the command to Rasulullah as the head of Islamic government at that time. Thus, that command also applies to the heads of state afterwards.
The above opinion is also supported by the attitude of Abū Bakar to the opponents of zakat in his era. Abū Bakar -who was the head of state or khalīfah after Rasulullahwas very angry and even battled some Arabian who refused to pay zakat with argument that the obligation to pay zakat was no longer exist because Rasulullah has already death. In a Riwāyah Bukhāri and Muslim mentioned that Abū Bakar said "By Allah! If they refuse to pay even young goat which they used to pay during the life-time of Rasulullah, I will fight with them for it." (HR. Bukhāri and Muslim) In the implementation of zakat as describe above, the government can appoint some officers who handle the collection and distribution of zakat, known as amil zakat or 'āmilīn. This is as it was the assignment of Rasulullah who was a representation of the head of state at that time to the Mu'adz bin Jabal to take zakat in the Yaman. In the assignment, Rasulullah gave a message to Mu'adz to be presented to the Yaman residents. The message is "Indeed Allah has enjoined on them, the Zakat. And it is to be taken from the rich amongst them and given to the poor amongst them" (Muttafaq 'Alaih).
In addition, the use of Lafaḍ (take) in sūrah al-Tawbah: 103 and Lafaḍ (taken) in the above hadith also becomes a reinforcement that zakat is the right of the recipient (mustahiqq) to be taken from the rich (muzakkī). Thus, zakat is not a voluntary that is left entirely to the consciousness of each individual. In contrary, zakat is obligatory which when muzakkī refuse to give it, then zakat can be taken by force as done by Abū Bakar. This is as confirmed by Allah in sūrah al- Dzāriyyāt: 19 "and In their properties there was the Right of the beggar, and the Mahrûm (the poor who does not ask the others)"  There are two important wisdom proposed by al-Sha'rāwī in management of zakat by government, that is (Sha'rāwī n.d.): 1. The avoidance of mustahiq from disgrace for "raising their hand" to ask zakat from muzakkī. With the management of zakat by government, the mustahiqq no longer need to "raise their hand" to ask zakat because they will directly receive it from the government. 2. Avoidance mustahiq especially Faqīr and Miskīn from the negative psychological impacts that occur when receiving directly from muzakkī. For example, when the family mustahiqq A know that the muzakkī who gave the zakat is family B, then it will give own psychological impact to family A. it could be a child from family A will feel inferior when dealing with children from family B because he knows that family B is the person who gave zakat to his family. It is different when zakat is managed by the government, then there will be no poor people who feel contemptible and inferiority when dealing with the rich because they didn't know from whom the zakat their received. The poor know only that as a people, they get what their deserves from the government which supposed to prosper all his people. Based on the above description, we can know that zakat in Islam is obligatory not voluntary. This assumption is important for the functioning of zakat in society's life and economy. In the context of sūrah al-Tawbah: 103, the zakat system as obligatory indicated by Lafaḍ is a requirement for the functioning of the zakat function shown by Lafaḍ which will be explain in the next discussion.

The Function of Zakat in the Social and Economic Equality
In the sūrah Al-Tawbah: 103, Allah mentions two main functions of zakat, there are the function of zakat in the form of and the function of zakat in the form of . First, the function of zakat in the form of . Lafaḍ is a fi'il muḍāri' from maṣdar . While Lafaḍ (al-Taṭhīr) comes from the syllable or which means holy or clean. In Arabic grammar, Lafaḍ can be used for clean or holy physical ( ) as well as spiritual ( )(Zubaidī n.d.). The use of Lafaḍ for spiritual holy is also often used in the Quran. One of them is "are the ones whose hearts Allâh does not want to purify (from disbelief and hypocrisy); for them there is a disgrace In This world, and In the Hereafter a great torment" (QS.  Based on this, we can interpret the the function of zakat in the form of in the sūrah al-Tawbah: 103 as cleansing and purifying of the soul. In this case, zakat is able to cleanse the hearts those who pay zakat from the greedy and miserly which is bad characteristic of human nature. Allah said "Say (to the disbelievers): "If You possessed the treasure of the Mercy of My Lord (wealth, money, provision, etc.), Then You would surely hold back (from spending) for fear of (being exhausted), and man is ever miserly!"(QS.  In the other ayah, Allah is also said "Even though men's souls are swayed by greed" (QS. Al-Nisā': 128) As we have known, treasure is something that very precious and loved by human. In fact, sometime we find someone who loves treasure more than everything including family or even God. Therefore, it is no exaggeration if Rasulullah mentioned the treasure as a something green ( ) or pleasing to the eye and sweet ( ) as a recorded by Bukhāri dan Muslim. Excessive love of the treasure often invite greedy virus into human heart. This greed is describe by Rasulullah in a hadith recorded by Bukhāri "Had the man been given a valley full of gold, he would have wanted the second again. If he is given the second one, he wants another third. No one can fill his stomach apart from the ground. Allah is receptive to anyone who wants to repent." (HR. Bukhāri no. 6438) That greed in the end makes people reluctant to give his treasure to other even the smallest thing. It's more ironic when the little treasure gone, he is not as confused as when that little treasure asked by poor people (Faqīr or Miskīn). He will be very hard to give it.
From here, the obligation of zakat that has been established by Allah has an important role to gradually kill the viruses of greedy and miserly in the heart of human. With the existence of zakat, the rich are forced to give some of the treasure that Allah entrust to the person who needed. Obligations that charged continuously are expected to be able to get used to share and feel the lives of poor people. So the next time that rich not only pay zakat because of duty, but also he will give charity more than he pay zakat as has been done by the Prophet friends, Tābi'īn and successor.
Besides cleansing and purifying the heart of muzakkī from the greedy, zakat is also able to purify the heart of mustahiqq from envy, anger and jealous. This envy, anger and jealous are often triggered by high level of inequality. And then, this envy, anger and jealous become a main factor of criminality such as robbery. In this case, zakat is one of the instruments to distribute the wealth, which in Islam functioned to minimize the inequality. The small inequality leads to reduce the potential of criminalization. Thus, the existence of zakat is essentially able to keep the wealth of muzakkī from the robbery because of the inequality. This is fit with hadith Rasulullah "Keep your possessions (from destruction) by (issuing) zakat" (HR. Al-Ṭabrānī) Awareness of zakat and the correct and optimal management of zakat are also capable to give a positive psychological impact to mustahiqq. The mustahiqq will feel the benefit from the existence of the rich people which their zakat become one of the income for mustahiqq . This, makes the envy, anger and jealous to the rich turn into gratitude and love for them. This is as stated by Rasulullah: "The attitude of the heart is inclined to love those who do good to him and hate those who do bad to him." (HR. Al-Baihaqī dalam Shu'ab al-Iman 6: 2985).
In the social context of society, the role of zakat according to al-Qarḍāwī is able to provide the strong bond between the rich (muzakkī) with the surrounding community. The bond will always be framed by love and combine with brotherhood and help each other (Qarawī 1973). At the end the bond will create a sense of secure, peace, and harmony between them. The sense of secure, peace, and harmony in this community will become one of the pillars of successful economic development.
Second, the function of zakat in the form of . Lafaḍ is a fi'il muḍāri' from maṣdar . While Lafaḍ comes from syllable which in language has meaning (holy, blooming, blessing, and praise). The use of the word for the four meanings is found in the Quran and hadith. Based on the above, has a same meaning with . However, according to al-Rāzī, when called concurrently (affixed) with Lafaḍ , there must be a difference between the two (Rāzi n.d.). This is because in the Quran there can be no repetition of the same Lafaḍ without any different meaning or other implied utility.
Based on above description, there are three meaning given by mufassir for Lafaḍ in the sūrah al-Tawbah: 103, that is (Zuhailī 1998) (Baghāwī 1997): 1. Zakat is able to raise the people degree who pays it from the Munāfiq to the mukhliṣ (people who feel ikhlash). This opinion based on the assumption that sūrah al-Tawbah: 103 is a continuation of al-Tawbah: 101 which describe the Munāfiq people.

Lafaḍ
has the meaning (more purifying).

Lafaḍ
has the meaning (developing and giving blessings to the treasure). It's mean that Allah will make the loss of property due to zakat reason for develop, increase, and blessed of the treasure. From various meaning about Lafaḍ above, the authors agree with the third meaning. According to Ibn Mandzur, can also be derived from the syllable which has the meaning (evolved and result). Further Ibn Manḍur explain that Lafaḍ is used for everything that grows and develops (Miṣrī n.d.). Moreover, in another ayah, Allah explains the functions of zakat and alms as a developers and adders of treasure.
"Allâh will destroy Ribâ (usury) and will give increase for Sadaqât (deeds of charity, alms, etc.)"  Lafaḍ in the ayah above has the meaning of (reproduce and develop) (Dimashqi 1999). In this case, the function of which is in the zakat, according to al-Sha'rāwī occurs in every element in it, for the recipient (mustahiqq), the giver (muzakkī) and the treasure itself (Sha'rāwī n.d.).
Based on the above, we can conclude that one of the roles of zakat is able to add and develop economically. The role of adding and developing is applicable for the recipient (mustahiqq), the giver (muzakkī), and the treasure itself. In the context of economy, the role of zakat to recipients (mustahiqq) and givers (muzakkī) can be seen in the microeconomic outlook. While the role of zakat in adding and developing the treasure itself can be seen in the macroeconomic outlook.
The function of zakat as a micro can be seen from the role of zakat for thr recipient (mustahiq) and the giver (muzakkī). A very dominant group affected zakat is the mustahiqq, which their consumption level depend on the distribution of zakat (Sakti 2007). In other words zakat has a positive correlation on the consumption level of mustahiqq. It is described in a hadith "Zakat fitrah is the cleansing of fasting people of wasted talk and sinful acts, and as feeding the poor" (HR. Abū Dāwūd) In the above hadith, Rasulullah explained one of the function of zakat, that is as food for the poor (Miskīn). In the wider context, the function of zakat as a food has the meaning of zakat serves an addition for the poor in order to fulfill their consumption of basic necessities. With the zakat, the purchasing power of the poor to fulfill their basic needs is increase. In the context of microeconomics, the increasing of purchasing power is an increasing in demand.
In the zakat system, the process of zakat is allocate property based on two principles, that is to produce welfare and generate income level (Rivai 2013). The increasing of mustahiqq income automatically increases the purchasing power of mustahiqq. Theoretically, the existence of zakat will increase the demand curve through the demand aggregate that increase due to the purchasing power of mustahiqq society driven by the distribution of zakat (Sakti 2007). It means that the distribution of zakat will increase the purchasing power from mustahiq, and it is in accordance with the result of the research of (Ibrahim 2006) dan (Sartika 2008) The increasing of the demand curve as described above will certainly increase the price in the short time. However, the increasing of the price will has an impact of producer revenue. If it is assumed that this price increase information known to all market participants (symmetric information), then it will certainly attract new market participants to enter the market (Sakti 2007).
The entry of new market participants will increase the number of supply so it will increase the supply curve. As a result of the increase of the supply curve, then the price will be corrected. The equilibrium price after the zakat distribution will be relatively stable than compared to before the zakat distribution, but the number of goods has increased compared to before the zakat distribution. In addition, increasing demand for these goods will stimulate the production of goods and services (P3EI FE-UII 2016).
The positive effect of zakat on productivity and supply can also be seen from the enactment of zakat on money or idle financial assets (P3EI FE-UII 2016). Owners of unused property will be motivated to drain their wealth into the real sector so that zakat is only be taken on the yield, rather than its main assets. This is as conveyed by the Rasulullah "Remember, those of you who take care of the orphan's treasure, should he develop it in commerce and do not allow it to be eaten by zakat" (HR. al-Turmudhī) In the hadith, Rasulullah linked the prohibition to unemployed treasure by the application of zakat. Instead Rasulullah ordered to develop the treasure so that zakat can be taken from the results of treasury property rather than the main treasure. Thus, we can see how Rasulullah made zakat as an excuse for someone to always productivity the treasure and not let it idle. That increasing productivity is compatible with the result of research by (Rina Murniati 2016) and (Sartika 2008) In contrast to zakat, taxes have a negative effect on supply and productivity. Sales tax or value added tax will increase the average total cost, and if the price stays at the original price level, then this increase in cost means a decrease in profit. This can be happen because the revenue total is fixed while the total cost increases. The decline in profits also led to a shrinking range of production scales, as a result of the increase in the burden to be borne by market participants. The decrease in the scale of production means a decrease in the number of products produced which also means a decrease in supply(Karim 2012). In other words, imposition of sales tax or value added tax can decrease productivity and supply of goods.
Based on the exposure of the micro zakat function, it is evident that zakat can influence the economic behavior of mustahiqq and muzakkī. The following will be discussed using illustrations of demand and supply curves. From the side of mustahiqq, zakat can increase the purchasing powers which in the end also increase the demand. This will shift the demand curve from D to D 'so that the equilibrium point will shift from E to E'. Although the shift causes prices to rise from P to P', it only temporary. Because from the side of muzakkī, zakat will increase the productivity and supply that will shift the supply curve from S to S'. The shift will form a new equilibrium point from E 'to E''. At a new equilibrium point (E'') the price will be corrected and become relatively stable compared to before the distribution of zakat but with a larger quantity.
The macro zakat function can be seen from the function of zakat for investment and wealth distribution. There are several key elements that must be considered to understand the effect of zakat on investment. One of them is zakat collected on money or idle financial assets, such as gold deposits or money that exceeds a period of time a year and sufficient the nishab, thus encouraging people to invest or produce (P3EI FE-UII 2016). As has been explained before, zakat can encourage someone to manage and productive the treasure he has. In aggregate, it is able to encourage the investment. With the collected of zakat on stored treasure, that will be activated or invested (P3EI FE-UII 2016). Thus, zakat has a positive correlation with investment. The increasing in investment will ultimately increase the unit of production.
Contrary with the zakat that has a positive correlation with investment, Ribā has a opposite correlation. Ribā, which reflected in the interest rate, if its existence increases, it will cause a decrease in investment level. Interest rates as one of the financial instruments whose function is to control the money supply will greatly affect investment in the real sector. An increased interest rate will cause decreasing the money supply, so investment which is funding in the real sector will also decrease.
In addition, zakat also serves as one of the instruments of wealth distribution in Islam. As an instrument of wealth distribution, zakat has an important role in overcoming the problem of poverty. As we all know, one of the main concerns of development in developing countries is combat the source of the poverty and reduce the poverty (P3EI FE-UII 2016). Therefore, various development theories and economic policies were developed to reduce the poverty levels.
In general, there are several major causes of poverty, some of them are market inefficiency and imperfection, that is uneven distribution of wealth and income and economic and finance dualism which adds to the difficulty of achieving efficient market mechanisms. The market system will not automatically make a fair income distribution; and low human resource productivity that can be seen from the low level of education, the subtle high level of unemployment, and the low level of entrepreneurship (P3EI FE-UII 2016).
Income per capita of a country is calculated to be an indicator in determining economic growth. However, this economic growth does not guarantee that the increasing income enjoyed by every member of society equitably or evenly, does not reflect the high level of poverty, nor does it reflect how the process of improving the welfare is obtained fairly without harming the rights of certain groups of people and does not reflect whether welfare is followed in every aspect of life or not. There are at least two instruments of poverty alleviation agreed by Faqīh, one of it is zakat (P3EI FE-UII 2016). Viewed from the economic aspect, zakat is useful to avoid the accumulation of property on a few people, distribute the treasures more justly and equitably, prospering of the weak, and expected to produce a better economic order.
Zakat is a duty to distribute some of the treasures that the distribution process strongly associated with the efforts to reduce the level of poverty. Of the eight groups of mustahiqq, all the groups are in weak condition and can't be allowed to do Mu'āmalah or transact through market mechanism (P3EI FE-UII 2016). Zakat is also a religious command that considered as worship or good deeds, but muzakkī often indifferent to the benefits of the paid zakat. Yet the success indicator of zakat is not only seen from the paid zakat or not, but also how much the benefits of the paid zakat. There are some basic elements that must be considered to understand the effect of zakat on investment, one of that is a part of zakat distributed to muzakkī in the form of working capital (P3EI FE-UII 2016).
The use of productive zakat funds can be an alternative of capital financing for the poor who in the previous income analysis, their business turnover can fulfill household expenditures (Prodi Ekonomi Islam FEB-UNPAD 2016). Then explained that in the distribution of zakat as a source of financing to micro enterprises, should pass the filter process first. The filter process as the initial stage to filter and select the recipient or mustahiqq, so that later will get a prospective entrepreneur who has prospects and who have no prospects. Entrepreneurs who have good prospects will get the facility. One of the aspect that should be seen is the spiritual aspect (morale). It can be accurately used to disclose all the information needed in the screening process or as a filter in the selection process and the preparation of the recipient.
Implementation of Islamic social fund system provides an adequate space for the recipients of zakat to fully concentrate in the management of their business (Prodi Ekonomi Islam FEB-UNPAD 2016). In the zakat system, there is no obligation for the recipient of zakat to recover the funds they received. However, there are demands socially and spiritually when the business has grown, then there is the obligation to set aside part of his wealth on the zakat sector. The proceeds of the zakat funds will be redistributed to the other mustahiqq in need. Therefore, zakat will play a significant role in decreasing the poverty level. This is compatible with the result of research by (Ibrahim 2006), ( irfan Beik 2009), (Firmansyah 2013), (I. S. Beik and Pratama 2016).
In general, the function of zakat for social and economic which contain in sūrah al-Tawbah: 103 can be described in diagram bellow:

V. CONCLUSION AND RECOMMENDATION FOR FUTURE RESEARCH 5.1 Conclusion
Based on the analysis above, it can be concluded that the concept of zakat based on sūrah Al-Tawbah : 103 is zakat become an obligatory system, which the management should be done by the government; and the function of zakat for the social according to sūrah Al-Tawbah : 103 is create a balance of life in society with the creation of a sense of secure, peace and harmony in the community. While the function of zakat for the economy is an increasing the purchasing power, productivity, investment and poverty reduction. This is the main capital for the sustainable economic growth. In this case, it is compatible with the previous research.
Based on the study findings, the following recommendations are provided. First, Indonesia as a country with the majority of the Muslim population is expected to make zakat as an obligatory system to moslem population. This is because the huge potential of zakat from the Muslim community of Indonesia which is the largest Muslim country in the world. Second, consequences of the implementation of zakat as an obligatory system is the management of zakat expected can be handled by the government. Zakat management by the government is able to optimize the function of zakat for social and economic community; third, research related to Islamic economic applications written in the Quran can use the tafsir approach which is the real khazanah in reviewing.


Status Threat, Material Interests, and the 2016 Presidential Vote

The April 2018 article of Diana Mutz “Status Threat, Not Economic Hardship, Explains the 2016 Presidential Vote,” was published in the Proceedings of the National Academy of Sciences and contradicts prior sociological research on the 2016 election. Mutz’s article received widespread media coverage because of the strength of its primary conclusion, declaimed in its title. The present article is a critical reanalysis of the models offered by Mutz, using the data files released along with her article. Contrary to her conclusions, this article demonstrates that (1) the relative importance of economic interests and status threat cannot be estimated effectively with her cross-sectional data, and (2) her panel data are consistent with the claim that economic interests are at least as important as status threat. The preexisting sociological literature has offered interpretations that incorporate economic interests and, as a result, provides a more credible explanation of the 2016 election.

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Original Article
A first wave of sociological research on the 2016 presidential election has now been published, and a prominent theme of this research is the appeal of Trump's campaign to white, working-class voters. Analyses of Obama-to-Trump voters, along with the spatial distribution of votes cast, are both consistent with the claim that white, working-class voters represented the crucial block of supporters who delivered the electoral college victory to Trump (McQuarrie 2017; Lee 2017, 2018). To attract their support, Trump appealed directly to the economic interests of working-class voters, praising the dignity of their work and arguing that their past labor had given the country its mid-twentieth century prosperity (Lamont, Park, and Ayala-Hurtado 2017). He relied on folk beliefs about how the U.S. economy can be managed in order to argue that renegotiated trade agreements and restrictions on immigration would improve working-class economic standing (Swedberg 2018). These appeals echoed populist arguments of past insurgent Republican candidates, most notably Pat Buchanan, who also challenged the expansion of free-trade agreements that were promoted by mainstream Republicans, the business community, and centrist Democrats (McCall and Orloff 2017).
None of this sociological scholarship has argued that Trump did not also increase his support among white, working-class voters because of his willingness to stoke xenophobia and white nativism, in particular through false and disparaging characterizations of immigrants from Mexico and Muslims of all types. Trump also offered veiled appeals to long-held prejudice against black Americans, which is more prevalent among white, working-class voters (Morgan and Lee 2017), such as when he criticized the Black Lives Matter movement.
Yet it is also clear that Trump is an entirely different sort of politician than any recent presidential candidate, with a willingness to lie for effect and to provoke as necessary to achieve his ends (Hahl, Kim, and Zuckerman Sivan 2018). It is therefore unclear how much of Trump's nativism was appealing to white, working-class voters because of its core content. For some voters, it may have been received as an effective demonstration of workingman's bravado, intended as a critique of the excessive "political correctness" of highly educated elites and the politicians they support.
The overall conclusion of this first wave of sociological research would appear to be that we have more work to do in order to understand why so many white voters supported Trump. And although we may never be able to definitively decompose the sources of their support, four primary motives deserve further scrutiny: economic interests, racial prejudice, white nativism, and the appeal of the Trump persona. At the same time, it remains to be determined how much of the animus toward his competitor, Hillary Clinton, was crucial to his success, and whether that contrary sentiment is based on gendered evaluations of leadership competence or, instead, particular features of Clinton's biography and how they were used against her.

Status Threat as the Key Motivation for Trump's Support
In an April 2018 article published in the Proceedings of the National Academy of Sciences titled "Status Threat, Not Economic Hardship, Explains the 2016 Presidential Vote," the political scientist Diana Mutz (2018) concluded, Evidence points overwhelmingly to perceived status threat among high-status groups as the key motivation underlying Trump support. White Americans' declining numerical dominance in the United States together with the rising status of African Americans and American insecurity about whether the United States is still the dominant global economic superpower combined to prompt a classic defensive reaction among members of dominant groups. (p. 1) In support of this conclusion, Mutz claims that she is able to explain away the education gradient in support for Trump that has led prior analysts to focus incorrectly on the economic interests of white, working-class voters: Lack of a college education was persistently noted as the strongest predictor of Trump support. This pattern led journalists with limited data toward economic explanations. However, education is also the strongest predictor of support for international trade, a relationship that is not tied to income or occupation so much as ethnocentrism. Negative attitudes toward racial and ethnic diversity are also correlated with low levels of education. In this election, education represented group status threat rather than being left behind economically. (p. 9) These claims are far stronger than those of most other scholars who have analyzed similar measures, including the sociologists cited above.

Media Attention to the Status-threat Explanation
Mutz's article offered bold claims that deliver clarity on an election outcome still regarded as a puzzle by many. The article was promoted in a press release from her university that noted its publication in the journal sponsored by the National Academy of Sciences, a journal with a title that implies that its contents are first presented in front of a body of the country's leading scientists. The press release, like the original article, reduced the economic interests of white, working-class voters to current and recent "pocketbook" concerns. 1 Because of the press release, as well as the prestige of the article's publisher, Mutz's article received widespread media attention. The New York Times published "Trump Voters Driven by Fear of Losing Status," summarizing Mutz's article as follows: Ever since Donald J. Trump began his improbable political rise, many pundits have credited his appeal among white, Christian and male voters to "economic anxiety." Hobbled by unemployment and locked out of the recovery, those voters turned out in force to send Mr. Trump, and a message, to Washington.
Or so that narrative goes.
A study published on Monday in the Proceedings of the National Academy of Sciences questions that explanation, the latest to suggest that Trump voters weren't driven by anger over the past, but rather fear of what may come. White, Christian and male voters, the study suggests, turned to Mr. Trump because they felt their status was at risk. "It's much more of a symbolic threat that people feel," said Diana C. Mutz, the author of the study and a political science and communications professor at the University of Pennsylvania, where she directs the Institute for the Study of Citizens and Politics. "It's not a threat to their own economic well-being; it's a threat to their group's dominance in our country over all." (Chokshi 2018) For CNN, the summary was similar but with the introduction embracing a bold truth claim: "The media largely stuck to the conventional wisdom that economic anxiety helped drive support for President Donald Trump during the 2016 election. The only problem? It isn't true." (Waldow 2018) Some pundits used Mutz's article as a provocative way to begin a column, such as the Washington Post's Michael Gerson in his "How Do We Tame Trumpism's Virulent Nostalgia for an Old Status Quo?" (Gerson 2018 The big myth about the 2016 presidential election was that economic suffering drove most of Donald Trump's "base" directly into his hands in states such as Wisconsin, Pennsylvania, and Michigan. The story goes that while Democrats were tied in knots about identity politics, Trump's attacks on China, free trade and open-ended immigration appealed to struggling workers who believed he could bring back their jobs.
The problem with the narrative is that we keep learning it is not true.
Some Democrats have responded to the widely circulated misconception about why Clinton lost by insisting that the party needs to move away from identity politics -issues revolving around gender equality and racial justice-and focus in on economic issues.
Instead, Democrats should be basing their 2020 election strategy on what is actually true.
A just-published study in the Proceedings of the National Academy of Sciences by the political scientist Diana Mutz found that white, Christian, male voters were attracted to Trump out of fear that their social status keeps dwindling. (Zelizer 2018) Similar, but shorter, write-ups of Mutz's article were offered by both the Economist on its blog ("Trump Voters" 2018) and the Atlantic (Khazan 2018).
The only critical journalistic piece in a major outlet in the days following the press release, an interview with Mutz published for Slate (Chotiner 2018), has revelatory content, such as the following: Mutz's position that "trade is not an economic issue in terms of how the public thinks about it" is a bold claim. It is also a pivotal assumption of her analysis that drives her conclusions, as I will show below.

Motivation and Plan for a Reanalysis
Mutz's claim that status threat alone provides a powerful explanation for the 2016 election outcome is at odds with the sociological literature that places similar identity responses within a constellation of motivations that can become mutually reinforcing. My primary goal in this article is to provide an analysis of the data files released with Mutz's article in order to demonstrate that her conclusions about status threat are far too strong. 2 My secondary goal is to provide a more complete perspective on voting motivations in the 2016 election, drawing on the sociological literature cited above that has analyzed white, working-class voters. Accordingly, I argue in the course of the empirical analysis that Mutz's concept of "economic hardship" is too narrow, relative to the existing literature on material interests and the 2016 election, in part because her concept of "status threat" is too broad. Thus, in addition to assessing Mutz's conclusions using her own models and measures, I offer additional analysis from this broader material-interests perspective. 3 After providing a brief orientation to my methodological approach, I offer results in response to three specific research questions. The subsequent analysis demonstrates that measures of economic hardship, material interests, and status threat are all sufficiently intertwined in Mutz's cross-sectional data that one cannot deliver credible estimates of their relative importance to construct a compelling causal explanation. In addition, the panel-data models offered by Mutz do not provide additional clarity, while more straightforward versions of those models contradict her interpretations.
I conclude with additional discussion of the 2016 election, questioning whether, in light of the foregoing results, the proposed status-threat mechanism has scope to improve upon explanations that rely more narrowly on traditional measures of racial prejudice. Finally, I argue that public-facing social science research can be counterproductive when the 2 A bit more formally and with loss in precision, my argument will be that the sociological literature provides convincing evidence that the causal explanation of the 2016 election is at least as elaborate as an interactive function, f (A, B, C, D). I contend that Mutz argues for an alternative function, f(S), where S is the union of the sociologists' A and B and where the distinction between A and B can be deemphasized because status threat is a unifying narrative. 3 For readers who recognize the Marxist overtones of the concept of "material interests," I should be clear that the phrase "economic interests" can be and will be used interchangeably throughout this article on the basis of context. I use "material interests" for fidelity with (my reading of) the literature on class politics, not to assert that class conflict is essential to their relevance for political support. evidentiary basis of claims is too weak to merit widespread media coverage.

Methodological Approach
I adopt the approach of a "fair critic," writing from a sociological perspective and seeking to assess (1) whether the models that Mutz presents are the models she estimated, (2) how she interprets the models she estimated, and (3) whether her overall conclusions are robust to alternative reasonable choices about what models could be estimated with the data Mutz shared with readers of the Proceedings of the National Academy of Sciences.
I do not evaluate any of the initial steps in Mutz's analysis, including how the data were collected and initially coded. I also do not consider whether other measures available in her data that she chose not to incorporate into her analysis could have better supported or further undermined her conclusions.
This approach, which I label a critical reanalysis, is a reasonable and productive way to evaluate published results, insofar as the reanalysis holds constant many of an author's analysis decisions in order to enable a clean assessment of the consequences of a few important decisions that determine the conclusions. 4 To facilitate further reanalysis of Mutz's results, and to enable a critical reanalysis of the models I present in this article, the Stata code I have written is publicly available on GitHub (at https://github.com/stephen-l-morgan in a public repository named status-threat-analysis-for-socius).

Three Questions for Reanalysis
I organize my critical reanalysis in three sections. In each, I pose a question and offer an answer. Thereafter, I explain what I take to be Mutz's position on each question and then offer analysis that justifies my answer. Only for the first question are Mutz's conclusions in line with mine. And here, as I show next, a simple extension of her analysis provides valuable additional insight.
Question 1: Did voters change their positions on trade and immigration between 2012 and 2016, and were they informed enough to recognize that Trump's positions were much different from Mitt Romney's, in comparison to Clinton's and Obama's? Answer: Voters did change their positions on trade and immigration, but only by a small amount. They were also informed enough to recognize that the positions of Trump and Clinton were very different from each other on these issues and also in comparison with the positions of Romney and Obama four years prior.
On this question, Mutz's results are well supported under reanalysis, and they are a unique and valuable addition to the literature. With panel data collected online by GfK a few weeks before the elections in both 2012 and 2016, Mutz shows that the positions taken by voters on both trade and immigration changed, on average, only very modestly between the two elections. In 2016, respondents had slightly less favorable attitudes toward international trade and slightly more favorable attitudes toward inclusive immigration policy. Because these issue positions were reported by the same respondents, such panel-based results that show considerable stability are a valuable addition to findings from surveys that also suggest stability but are based on independent cross-sectional samples. 5 In contrast to the stability in their own issue positions, voters perceived strong differences between 2012 and 2016 in the issue positions of the presidential candidates. For Obama and Romney, voters saw little or no difference between their support of additional free-trade agreements and only a small difference on whether China is a threat to jobs and security. For Trump and Clinton, wide differences emerged, with Trump seen as much more opposed to free-trade agreements and much more likely to regard China as a threat to jobs and security. On immigration, voters perceived a substantial difference on inclusive immigration policy between Obama and Romney, but they saw the difference increasing markedly for the comparison of Clinton with Trump.
These results, which are presented in full in Appendix Table A1, imply that voters were informed enough to recognize differences between Trump and Clinton on at least a few headlining policy priorities of the 2016 campaign. More important, because these results are based on repeated measures from the same respondents, they demonstrate that the same voters recognized that Trump's positions were much different from those of Romney.
Mutz does not report in her article whether these changes differed by respondents' levels of education. In supplementary results for this reanalysis (again, see Appendix Table A1), I show that education level is not a substantial predictor of these changes, implying that these policy issues were salient enough in 2016 that voters of 4 In this case, it is also the only choice. The data and code Mutz released do not permit a "soup to nuts" replication, followed by a full reconstruction of an alternative set of models alongside hers. The two data sources Mutz analyzed are privately held by her research group, and only the final analysis data files were released to readers of the Proceedings of the National Academy of Sciences. With a few exceptions, the files include only the variables that are specified in the models. In addition, three measures of local economic context were deleted from the files for data-privacy reasons, under the rationale that the ZIP codes of respondents could possibly be determined by a third party. 5 In particular, the result is inconsistent with the speculation that "shy Trump voters" declined to participate in polls and surveys during the 2016 election cycle (assuming no selective panel attrition).
widely different education levels were sufficiently aware of candidate differences between 2012 and 2016.
I will return to Mutz's panel data below, but it is more natural to shift now to Mutz's second data source, a 2016 cross-sectional data set, collected by the National Opinion Research Center through its omnibus, mixed-mode AmeriSpeak poll, in the weeks just before the 2016 election. Mutz uses this data source to address the following question: Question 2: Can the relative appeal of Trump to white voters with lower levels of education be attributed to status threat rather than their material or economic interests? Answer: No.
On this question, Mutz's article implies that she would answer affirmatively; in fact, she makes the case in her article that education "represented" status threat (see the quotation presented earlier). Here is the crucial interpretation from her results section: The Meaning of Education. The cross-sectional survey replicates the strong relationship with education shown throughout the election. More importantly, it provides a better understanding of what precisely education represents. In Table  S5, model 1, I replicate the strong relationship between lack of college education and Trump support using only demographics as predictors. In model 2, I examine what happens to education's predictive power when measures of personal economic wellbeing are also included in the model. Finally, in model 3, I drop the economic variables and instead, include indicators corresponding to status threat toward dominant groups. As summarized in Fig. 3, regardless of which outcome measures I examined, including indicators of economic status did not eliminate the impact of education. It reduced education's impact somewhat for the feeling thermometer measure, but for Trump/ Clinton vote, the impact of education remained constant. However, after the relationship between Trump support and perceived status threat is taken into account, even lack of a college education no longer predicts Trump support for any of the measures. These findings strongly suggest that group-based status threat was the main reason that those without college educations were more supportive of Trump. (Mutz 2018:8) Table 1 presents the results Mutz interprets in this paragraph, as well as a reanalysis using the 2016 cross-sectional data she released. The outcome in the first panel is a relative thermometer rating, with poles of warm/approve and cold/ disapprove, scaled such that higher numbers favor Trump relative to Clinton. The outcome for the second panel is intended vote choice: Trump rather Clinton among those who intended to vote for one or the other. 6 Mutz's interpretation is based on the coefficients in rows 1 and 4, which I have copied from her article. As noted above, three measures of local economic context were not provided in the data files she released, and so, for reanalysis, they cannot be included. In rows 2 and 5, I show for the reanalysis that their omission has only minor consequences. 7 Note: Standard errors are in parentheses for the results from the reanalysis. Mutz does not provide standard errors. OLS = ordinary least squares. 6 Mutz's article includes a third outcome: Trump as the favored candidate versus all others, including third-party candidates. The results are sufficiently similar to the head-to-head analysis of Trump and Clinton that, for brevity, I omit results for this third outcome. The results are, nonetheless, produced by the code that generates the reanalysis and so can be accessed by an interested reader. 7 These variables have very little predictive power in Mutz's results throughout her article and are one basis for why she argues that "economic hardship" is not an important factor for understanding the 2016 election. Without access to these variables, I cannot assess whether they are valid measures. At face value, it does seem a strong assumption to maintain, for example, that a static measure of percentage manufacturing employment averaged over recent years in a respondent's five-digit ZIP code is a sufficient measure of the extent to which voters believe that deindustrialization since 1970 has altered the economic standing of different types of workers in their own regional labor market.
The goal of Mutz's analysis, consistent with her language of providing an "understanding of what precisely education represents," is to show that the "effect" of low education, as a measure of being in the working class, is a spurious causal effect that can be attributed instead to status threat, not material or economic interests.
All rows of Table 1 show the same basic pattern, but the final row gives the most interpretable metric: the average marginal effect of not having a bachelor's degree on the likelihood of voting for Trump. The coefficient of 8.41 (with a standard error of 1.3) suggests that Trump's vote share was higher by 8.4 percent among those without a bachelor's degree, after adjusting for other demographic characteristics. When economic indicators are included as additional adjustment variables, Trump's excess vote share falls to only 7.1 percent, suggesting that material interests measured by economic indicators cannot explain away much of any of the net association between education and voting. But when adjusting for measures of status threat, the vote share falls to less than 1 percent, which leads Mutz to the conclusions specified earlier.
What are the specific adjustment variables used? Table 2 lists the three groups of variables selected by Mutz, along with an alternative categorization that the sociological literature suggests a fair critic would prefer.
Four of Mutz's six baseline background variables are also in the critic's baseline set, but two are categorized differently. Although political scientists have devoted tremendous attention to the relationship between party identification and voting, many researchers view party identification as an endogenous outcome, especially when collected only a few weeks before the election. In the modern language on causal inference (see Morgan and Winship 2015), party identification can be considered a descendant of the education variable that lies on a directed path that reaches the outcome variable of intended vote. 8 In the analysis below, I will therefore estimate models with and without party identification in the baseline conditioning set. In addition, I will use the indicator variable for non-Hispanic whites in two ways, as the basis for analyzing only the sample of whites and as an adjustment variable when analyzing all respondents.
In addition to the four variables Mutz considers "economic indicators," I consider views of the nation's economy to be a relevant member of the alternative "material interests" category. The rationale for including it among the critic's measures of material interests is that it is a lay judgment rendered from the vantage point of the respondent, reflecting to some extent one's own experience with the economy, not that of a depersonalized economic planner who renders a professional judgment after considering all the facts. 9 The major difference shown in Table 2, however, is the category of "status threat," which includes many more variables for Mutz's analysis. Two of these variables, support for negotiating more free-trade agreements and the opinion that China is a threat to jobs and security, are considered measures of status threat for Mutz but measures of material interests for the critic. As noted in the introduction, Mutz does have an arguable rationale for her decision: such responses reflect ethnonationalism more than anything else. A critic, however, would note that these questions are typically asked in a survey context in batteries on respondents' own economic standing (and this is the case for Mutz's data, according to her questionnaire map). In addition, the standard position in the literature is that respondents make cognitive connections among globalization, trade, and their own economic standing (see McCall and Orloff 2017). And perhaps most important, Trump, as a presidential aspirant, tied both issues directly to working-class economic security, as shown by Lamont et al. (2017) and Swedberg (2018). Trump's own statements are an undeniably important part of the context for how these questions would be interpreted by respondents only a few weeks before the 2016 election. Recall also that for question 1 above, Mutz showed how these same respondents demonstrated that they were aware of how much Trump's positions on trade differed from those of Romney, Clinton, and Obama. It seems unlikely that they would recognize this difference but entirely ignore Trump's economic rationale for it.
The critic then includes a separate category for three variables-support for inclusive immigration policy, support for isolationism, and terrorism as a threat-that I label "material interests and foreign policy," because they are an entwined set of attitudes that, for some respondents, may be strongly shaped by their economic interests, whereas for others they may only reflect cultural values and their judgments about the nation's relationship with other countries. For this reason, the critic treats attitudes toward inclusive immigration policy and support for isolationism as sufficiently distinct from both status threat and clear measures of material interest that it is sensible to place them in their own intermediate category. Finally, the terrorist threat variable is included in this category because Mutz uses it in the immediately prior Table S4 but then drops it for her analysis of the education gradient. For a fair critic, it is a sensible measure of foreign policy and immigrant threat, especially given Trump's linkage of terrorism to his proposed "Muslim ban." The final row of Table 2 then categorizes the national superiority variable as a status-threat variable, which is where it is placed for Mutz's more general analysis in her Table S4.

Status threat
Note: The description of each variable reflects the question wording and is not always equivalent to the labels Mutz used. For the critic's categorization, discrimination against high-status groups is represented by three 2-item subscales provided by Mutz: more discrimination against men than women, Christians than Muslims, and whites than blacks. It is excluded from her analysis of the education gradient in Table S5, and for consistency I reintroduce it into the critic's categorization. Table 3 presents alternative conditioning results, enacting the strategy suggested by the final column of Table 2, in four separate configurations: for the full sample and for whites only cross-classified with whether party identification is included in the baseline variables. A fair critic would likely favor the models that do not adjust for party identification and that are estimated for whites only, given that the white working-class narrative is what inspired Mutz's analysis (and that narrative suggests that white, working-class voters, not all working-class voters, were the ones to push Trump across the threshold of victory). These are the models in the first and fifth rows of Table 3, which show the same basic pattern.
To understand the point estimates, consider the models for vote choice in the fifth row. Trump's vote share was 24.2 percent higher among whites without a bachelor's degree after adjusting for baseline variables. Further adjusting for material interests, the share decreases to 7.3 percent of white voters. Finally, the share falls to 1 percent of white voters when the immigration and foreign policy variables are included. This adjusted share is similar to the share in the final column, which is produced only by adjusting for the baseline background variables and status threat measures.
Overall, three patterns are clear in the table. First, material interests explain away far more of the education gradient than Mutz's "economic indicators" above in Table 1. When the variables for "material interests and foreign policy" are then included as conditioning variables, the adjusted effect of education mostly vanishes, without needing to use any of the measures of status threat. Second, the explanatory power of these variables increases slightly when party identification is considered endogenous and therefore excluded from the baseline background variables (because the baseline-adjusted education effect is larger). Third, the explanatory power also increases when the sample is limited to whites only.
What would an analyst attuned to the modern literature on causal inference conclude on the basis of these results? First, Mutz's "horse-race" conditioning strategy is a reasonable beginning point. However, explicit assumptions, ideally encoded in equations with exclusion restrictions or in a causal graph, are needed to warrant causal conclusions. The categorizations of conditioning variables in Table 2 are too far from explicit assumptions to support disciplined causal inquiry. As such, the models in Tables 1 and 3 are stylized descriptive models that seek to determine which variables can account for observed patterns of association in the data set analyzed. Second, given what the analysis can accomplish, the horse race is probably a tie. Material interests can account for a lot of the education association with Trump's support and status-threat variables somewhat more. When immigration and foreign policy variables are used alongside the material interest variables, the difference is narrowed so much that it is untenable to conclude, as Mutz does in her article, that education "represented" status threat in this election rather than concern for material interests or other sorts of policy evaluations. Mutz claims to offer panel-data models that allow her to confidently assert that status threat, rather than material interests, explains the outcome of the election. Unlike the results for the prior section, Mutz's conclusions are not based on the comparison of multiple models, with varying sets of adjustment variables in a comparative conditioning analysis. Instead, for her Table 1, she offers an overall characterization of coefficients for the same specification of predictor variables, deployed for the analysis of two outcome variables measured in both 2012 and 2016: thermometer advantage for the Republican candidate and voting for the Republican candidate rather than the Democratic candidate. In this portion of her analysis, the models are represented as "all-cause" specifications in which simultaneously estimated coefficients are interpreted as warranted net direct causal effects.
Before examining Mutz's panel data, it is important to show the rationale that she offers for her embrace of a paneldata approach. She reasons in the opening to her article, Because elections are not amenable to experimentation, it is difficult for scholars to make strong causal claims. As a result, most interpretations of election outcomes either rely on crosssectional associations in survey data or are inferred from aggregate data on voting patterns by geographic areas. Neither approach is the best that can be done.
In observational settings, panel data are widely acknowledged as the ideal basis for causal conclusions. When analyzed appropriately, they have the ability to eliminate most potentially spurious associations. (Mutz 2018:1) When introducing her specific models several pages later, she expresses strong confidence in their power to reveal causal effects, as well as the interpretive criteria she will use: Fixed effects panel analyses provide the most rigorous test of causality possible with observational data. Because the goal is understanding what changed from 2012 to 2016 to facilitate greater support for Trump in 2016 than Mitt Romney in 2012, I estimate the effects of time-varying independent variables to determine whether changes in the independent variables produce changes in candidate choice without needing to fully specify a model including all possible influences on candidate preference. Significant coefficients thus represent evidence that change in an independent variable corresponds to change in the dependent variable at the individual level. In addition, the net change over time in these independent variables must be in the direction helping to explain increased support for Trump. (Mutz 2018:4) Mutz does not discuss a major assumption of this type of model: extrapolation from within-person variation to all variation, which is sometimes referred to as a "constant coefficient" assumption. Consider the association between income and vote choice. The assumption is that an estimated change in vote choice from the Democratic candidate to the Republican candidate, and vice versa, that is predicted by shifts in income between 2012 and 2016 is equal to the effect of income on changes in vote choices between individuals whose income is stable. For Mutz's data, the income variable appears to be household income from all sources, including at least wages and salaries from all adults in the household, Social Security and pension payments, other government transfers, and investment income. The variable in her released data set is binned into 19 categories for 2012 and 21 categories for 2016. The model uses variation only from individuals whose incomes changed to higher or lower bins, and it pays no attention whatsoever to whether the changes are produced by exogenous economic shocks, like an unforeseen layoff, or instead annual raises, a voluntary job change, retirement, or a household-changing life event such as marriage or divorce that alters income pooling. Then, whatever association is calculated from such within-household, binned-income change between 2012 and 2016 is implicitly extrapolated to those who have stable income across both time periods.
For a concrete example from Mutz's data, 55 individuals who voted in both 2012 and 2016 had incomes in the "$20,000 to $24,999" bin in 2012. Four years later, 21 of these individuals remained in the same bin, 6 fell to lower bins, and 28 moved to higher bins. For a fixed-effect model, the 21 individuals who remained in the same bin are ignored. These stable-income individuals represent 38 percent of this income stratum, each of whom could be described as having persistently low income in 2012 and 2016. If these are the individuals who were Obama-to-Trump voters, and if they were Obama-to-Trump voters in part because they were frustrated by their stable and relatively low incomes, a fixed-effect model assumes that their stable income-induced support for Trump can be estimated effectively by comparisons of vote-choice changes across individuals whose incomes fluctuated across bins between 2012 and 2016.
The constant coefficient assumption that equates all sources of variation is, therefore, very constraining for interpretations.
It does not mean that fixed-effect models are not worth estimating. Table 4 presents 12 fixed-effect models of this sort, in which the outcome is the same thermometer advantage for the Republican candidate analyzed earlier, but now for a pooled sample with two observations for each individual, one for each election. For this outcome, the thermometer advantage takes on 20 values, and only about 15 percent of respondents favor the Republican candidate more than the Democratic candidate by the same amount in both years. This represents a sufficient amount of within-person variation for a fixed-effect model to be successfully estimated with a sample of this size (unlike vote choices, as I discuss later).
The first two columns summarize nine separate bivariate fixed-effect regression models, one each in which the variable in the row label is specified as the sole predictor variable. The first coefficient, -0.43 (with a standard error of 0.12), suggests that individuals who indicated that their current personal finances were better this year than last year (by one point on a five-point scale) rated the Republican candidate lower by 0.43 units on the 20-point thermometer-advantage scale. This is a meaningful association, but not a large one. It corresponds to 1.2 percent of the variance of withinperson change in thermometer advantage, which is a typical result for a fixed-effect model because of the unreliability of these sorts of scales.
The other eight bivariate models have a similar pattern, with some variation in strength. The strongest, accounting for 7.9 percent of the variance of the outcome, is in the second row for the rating of the nation's economy. Overall, the first eight models have negative coefficients, and all are in line with the relevance of economic factors, suggesting that material interests played a role in changes in relative thermometer ratings between 2012 and 2016 (although, again, immigration could reflect status threat as well). The last coefficient is positive and summarizes the effect of changes in social dominance orientation on changes in relative thermometer ratings. Its effect is also small, and it accounts for less than 1 percent of the variance.
For the remaining columns, I present three fixed-effect multiple regression models. For model 1, seven variables that a fair critic would argue represent material interests account for 8.5 percent of the variation in relative thermometer ratings and, altogether, are consistent with a material-interest narrative. I do not regard the point estimates for the seven variables as warranted net causal effects, but collectively they do capture how well these variables predict changes in relative thermometer ratings between 2012 and 2016.
For model 2, which is the panel data equivalent of the status-threat model, I give the immigration variable over to it. (A fair critic might not be so generous, but this seems reasonable given that the additional status threat variables collected for Mutz's cross-sectional 2016 data were apparently not collected for her 2012 and 2016 panel data; see Table 2 in this reanalysis for these additional variables.) Even when given the immigration variable, the status-threat model accounts for only 3.5 percent of the variance in relative thermometer ratings, or less than half of what is explained by the material interest-only specification for model 1.
Finally, model 3 is a kitchen-sink model, and it shows that the variation that generates the coefficients for models 1 and 2 is largely unrelated. This may seem surprising given the cross-sectional analysis reported above. It is not. The crosssectional results were driven by stable differences, not changes expressed as variance in time, in how these predictor variables are related to relative thermometer ratings, which is one reason that the fixed-effect models are not as conclusive as Mutz claims. Regardless, model 3 shows that changes in status threat do not explain away changes in material interests as predictors of changes in relative thermometer ratings. Overall, Table 4 does not support Mutz's conclusion that there is "overwhelming" evidence that status threat is the sole or even the primary explanation of the 2016 election (Mutz 2018:9). Mutz does not present these simple models, which have straightforward interpretations. Instead, she offers a variant on model 3 that specifies many more variables. First, she includes party identification, even though the model suggests that it is endogenous, with an estimated negative coefficient that implies that individuals who shifted from Obama to Trump moved away from the Democratic pole and toward the Republican pole on her three-point scale (and vice versa for Romney to Clinton voters). The inclusion of party identification in the model robs some of the predictive power of the other variables.
Mutz also includes six additional predictors for the absolute differences between individuals' own positions on trade, immigration, and China and their perceptions of each candidate's positions on the same issues. For two reasons, the absolute-value distance variables do not improve the models. First, positive and negative differences from perceptions of candidates' positions are not distinguished, and the measures have floor and ceiling bounds because of the seven-point scales that are differenced. Second, ratings of closeness to a candidate are themselves endogenous to thermometer ratings (and vote choice; see below) because individuals support candidates who share their own issue positions. As a result, like party identification, including these variables in the model also robs some of the predictive power of the other variables in the model.
Finally, Mutz includes wave interaction terms for most predictors (i.e., a dummy variable for the 2016 wave multiplied by the 2012 value of each predictor, after copying that value into each 2016 person-record so that the interaction is not zero in all cases). These variables are meant to offer simultaneous tests of the differential salience of each relevant predictor, under the rationale that a substantial estimated coefficient for an interaction term indicates greater predictive power in 2016. An interaction with wave could provide additional insight if it were the case that a crucial singular causal effect was at the center of analysis, and models without wave by factor interactions were offered as a baseline for comparison. In this case, because no baseline is offered, and interactions are present for many variables, I do not see how the additional coefficients clarify Mutz's interpretations of net direct causal effects in either 2012 or 2016.
Regardless, these wave interactions are not required to evaluate what Mutz aims to evaluate. A fair critic would argue, instead, that the only models that need to be estimated, in the absence of a defendable set of assumptions in a well-articulated causal graph, are those presented already in Table 4. And those estimates do not support Mutz's conclusions. 10 What about vote choice? Mutz claims to offer fixed-effect logit models for vote choice that are analogous to those for relative thermometer ratings, and she uses the same specification of predictor variables in the models that she reports. However, the code she released shows that instead she estimated a generic logit regression model, and in reanalysis it is clear why this was necessary. A fixed-effect logit model for her validated voter sample cannot be estimated because of a lack of variation for the modest sample size at her disposal. Only 51 respondents flipped their votes in the sample: 25 from Obama to Trump and 26 from Romney to Clinton. 11 In contrast, for the thermometer-advantage models, nearly 1,000 respondents reported differences in relative thermometer ratings between 2012 and 2016. For this reanalysis, I verified that one cannot get a fixed-effect logit model to converge for a specification even simpler than Mutz's.
What is wrong with an ordinary logit? In some respects, such a model has advantages, insofar as it does not rely only on within-person variation, which is then extrapolated for interpretation to all between-person variation. But in this case, as judged by her own rationale for using fixed-effect models (recall her statement quoted above: "Fixed effects panel analyses provide the most rigorous test of causality possible with observational data"), her generic logit model falls short of the rigorous test she aims for. In particular, because no person-level effect is implicitly fit in these models, additional predictors are needed to protect against confounding. Two very obvious ones are missing from Mutz's models of vote choices: respondent's race and respondent's education.
Without these variables, and probably others, the only reasonable interpretation of her pooled logit model on vote choice in her Table 1 is that it is misspecified, for the very reasons Mutz argues make genuine fixed-effect models so valuable to estimate. The omission of race and education imparts the sort of bias that is likely to operate in the opposite direction of the "overcontrol" bias that results from the inclusion of endogenous variables-in her case, respondents' closeness to the issue positions of each candidate (and, for a sociologist, party identification as well). It is technically possible that these biases cancel, but this result seems unlikely.
Nonetheless, the vote-choice data can be inspected in more basic fashion to assess whether they align with her conclusions. In Appendix Table A2, I offer eight subgroup means of each of the predictors from Table 4 as well as party identification: for 2012 and 2016 and across four types of voters (Romney-Trump voters, Obama-Clinton voters, Obama-Trump voters, and Romney-Clinton voters). In general, movement in these mean values across years for different types of voters is consistent with the pattern shown above in Table 4 for relative thermometer ratings. For this reason, the thermometer-advantage models, I would argue, are sufficient to reveal the extent to which the panel-data models are informative.
Overall, what do the panel data reveal? For question 1, I noted already that the panel data showed that respondents were informed enough to appreciate how the positions of Trump and Clinton did and did not differ from the prior positions maintained by Romney and Obama. The panel-data models presented in Table 4 (which are consistent with the results in Appendix Table A2) show that respondents who voted for and supported Trump, rather than Clinton, had positions that matched his positions more closely, both for material interests and, as well, for Mutz's measures of inclusive immigration policy and social dominance orientation. These relationships could possibly be part of an explanation for why Trump's voters lined up behind him as their preferred candidate.
However, as Mutz discusses as well, these responses, offered only a few weeks before the elections, could simply reveal the extent to which voters had aligned their sentiments and positions with their preferred candidate. Consider the evaluation of the nation's economy. Trump voters saw the economy as weaker than Clinton voters did. A materialist explanation would suggest that this evaluation reflects particularities of their own economic circumstances. But a "reflection" interpretation would suggest that respondents are simply parroting Trump's criticism of Obama's legacy and attaching it to the record of Clinton. If this is the case, then the value of panel data collected only a few weeks before the election is considerably lower than Mutz claims that it is. And, if that is the case, then it also applies to status threat as well, such that individuals preparing to vote for a candidate such as Trump may well have been preparing to do so while rationalizing their intended choice in the just the sort of way that the social dominance orientation measures are designed to pick up.

Conclusions
Many analysts have argued that Trump's agenda to "make America great again" appealed to white voters, especially strongly identified Republicans who find "political correctness" distasteful and who are less comfortable with recent social change. Some scholars have seen this attraction as a type of racial resentment, or group-based status threat, driven by a felt loss of relative status.
What Mutz (2018) appears to argue is that economic interests, especially the appeal of Trump's vigorous campaigning for renegotiated trade deals to boost growth and provide economic security to the working class, no longer need to be included in explanations of the 2016 election outcome. A crucial piece of her argument is that (1) attitudes toward trade and immigration are unrelated to economic interests, which are based on current pocketbook concerns, but (2) attitudes toward trade and immigration are valid measures of group-based status threat, which has both national and international dimensions because of the nature of ethnonationalism.
In this article, I have offered a reanalysis from the perspective of a fair critic, attuned to the recent sociological literature, and I conclude that Mutz's data do not support her overly strong conclusions. Material interests and her measures of status threat are sufficiently entangled among white voters, especially those in the working class, that it is impossible with her data to estimate their relative importance with any clarity. In addition, the panel-data models that she offers are misspecified and do not reveal the causal effects of interest. Instead, alternative panel-data models and basic mean differences suggest the alternative conclusion that material interests are at least as important as group-based status threat.

Discussion
The core of the analysis I have offered is centered on measurement and modeling assumptions, as well as the range of interpretations that can be given to estimated statistical models. Broader matters of conceptualization should also be discussed in conclusion, insofar as they structure the components of effective explanations.
What role did economic interests play in the 2016 election outcome? Mutz takes a position that is at odds with the literature on material interests and how it has been invoked to analyze Trump's victory. For Mutz, economic interests can be narrowly defined-current and recent economic hardship or lack thereof-while for the material-interests literature, a broader conception of economic interests is appropriate. For this more encompassing alternative, economic hardship is not irrelevant, but it is only a subset of concerns that can be the basis of political support. The prospects of policy to shift economic distribution in the future, as well as to assign blame for economic distribution in the past, are critical to political support for alterative parties and candidates, especially at the end of a two-term presidency when the electorate considers the advisability of a disruptive change in course.
Even though I maintain this position and thus argue that Mutz gives short shrift to the economic interests of many of Trump's voters, I agree with Mutz's conclusion that noneconomic interests are vitally important to consider. Indeed, in prior work with my colleague Jiwon Lee, I have argued that many working-class voters mobilized by the Trump campaign are unlikely to be able to apportion their enthusiasm between policies and rhetoric that promote working-class economic interests and those that supposedly facilitate a return to a past when a less multicultural United States had a more prominent and secure position in the world economy. (Morgan and Lee 2017:678) After analyzing Mutz's data, I am further convinced of this position. I do not argue in this article that Trump's appeals to white nativism and ethnonationalism did not matter, only that they did not matter so much that little else did. Other scholars may see more promise in the capacity of statistical models to apportion the motivations of white, working-class voters, but the standard we set for strong conclusions on the basis of such models should be a high one in public-facing research, as I discuss later.
Turning from what can be excluded from an explanation based on economic interests, what should be included within an explanation based on status threat? I cannot determine from Mutz's article what the full answer to this question is, but it should be clear from the preceding analysis that I believe her operational definition is too expansive.
The challenge I see for the prospects of status-threat explanations is how to differentiate them, with empirical evidence, from explanations that rely on prejudice and racial animus instead. A baseline prejudice-incorporating explanation of the 2016 election outcome, which also gives appropriate scope to economic interests, would have these nine components: were excited by Trump's white nativism because it aligned with their preexisting prejudice, and many other less prejudiced white voters were willing to tolerate it. 6. Most nonwhite, working-class voters found Trump's white nativism offensive, so his appeal to their working-class interests was ineffective. 7. Some centrist white voters with college degrees were offended by Trump's white nativism and/or wanted to elect the nation's first female president, and thus some of these voters swung away from Romney toward Clinton. 8. The swing of centrist white voters with college degrees toward Clinton was much smaller than the swing of white, working-class voters toward Trump, in part because Clinton was framed in the primary campaign as less interested in working-class economic security than Bernie Sanders, and in part because she defended global trade in the general election against Trump. 9. Because of these swings in support, the education gradient increased among white voters, and the white working class was the crucial group of voters that put Trump over the top in the electoral college.
This skeletal narrative is not meant to be a full explanation for the 2016 election, but it has the features of an explanation that incorporates both prejudice and economic interests in order to demonstrate how they can interact to shape an electoral outcome. It is also consistent with two pieces of evidence that I find compelling: (1) white, working-class voters in the General Social Survey were, on average, no more prejudiced in 2016 than during the past few presidential election years (see Morgan and Lee 2017), and (2)  It is unclear from Mutz's article which components of a prejudice-incorporating explanation are inconsistent with her status-threat explanation. She does note at the end of her article that "status threat is not the usual form of prejudice or stereotyping that involves looking down on out-groups who are perceived to be inferior" (Mutz 2018:8). Instead, for Mutz's argument, white voters resent the racial progress that has eliminated some historical disadvantage because whites perceive that progress as a zero-sum loss in their own relative status. According to this reasoning, support for Trump, following eight years of a multiracial presidency, can be interpreted as a predictable psychological response: "a classic defensive reaction among members of dominant groups" (Mutz 2018:1).
If a zero-sum threat dynamic was an emergent force during the 2016 election cycle, then it would be helpful to be able to differentiate it empirically from a baseline prejudiceincorporating explanation. Derogatory statements about Mexicans, Muslims, Baltimore gangs, and activists in the Black Lives Matter movement were probably sufficient to secure the support of whites whose votes are predetermined by their own preexisting prejudice. The matters worthy of further debate are (1) whether a zero-sum status narrative has value for explaining the electoral swing in 2016 because it can account for the votes of less prejudiced whites and (2) whether, by implying that whites simply crave reassurance of their status dominance, the acceptance of such a supplemental explanation undermines (a) the need for more balanced economic growth that will benefit all members of the working class and (b) the need for more effective racial integration to reduce the prejudice and racial animus of whites that contributes to persistent racial inequality. Looking forward, if status threat is to become worthy of being placed at the center of causal explanations for future electoral outcomessubsuming racial prejudice, economic interests, and foreign policy views-then better measures need to be developed that can explain what existing measures cannot.

The Obligation of Care in Public-facing Research
Bold interpretations of evidence have their place in social science scholarship, but we also have an obligation of care when drawing conclusions about topics that are a matter of intense public concern. After reading Mutz's article, and this reanalysis, surely some readers will reach the position that Mutz's article pushes the interpretative envelope in a way that is exciting and hence has potential to inspire new scientific breakthroughs. From this perspective, her results may not fully support her conclusions, but there is a good chance that future evidence based on new measurement techniques will validate them, demonstrating why it is courageous and vital for scholars to always be just a bit beyond what the evidence suggests.
In an age when social science is often decried as insufficiently scientific-and, in the case of political science, leftleaning and partisan enough that it should be defunded by the National Science Foundation-social scientists must be careful when conducting and popularizing our public-facing research. Mutz's article characterized many millions of U.S. residents as feckless voters driven by a reactive psychologism, a claim that, in widespread media coverage, was accepted as a myth-busting explanation of the 2016 election. It is insufficiently cautious to deny the possibility that many voters recognized their own stagnant economic fortunes, borne of an age of gross inequality not seen in decades, and welcomed by a highly educated elite no longer shy of its own conspicuous consumption. It may be puzzling to see a billionaire as a savior for the fortunes of such voters, but it is far less puzzling if, as a bombastic outsider candidate claiming to fund his own campaign, he was a beneficiary of their desire for a transgressive moment of protest. This possibility also suggests that reconciliation may be more attainable than feared, and that centrist voters, many of whom are white and working class, are not beyond the limits of reasoned persuasion, especially if working-class economic security is not enhanced by the promised upending of global trade relations and migration patterns.