Faith and Flows

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Faith and Flows

An Empirical Study on the Effects of Religious Discrimination on Trade

Paal Wanvik Hole

Master of Philosophy in Economics Department of Economics

University of Oslo

November 2018

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c Paal Wanvik Hole November 2018

Faith and flows: An Empirical Study on the Effects of Religious Discrimination on Trade http://www.duo.uio.no

Print: Reprosentralen, Universitetet i Oslo

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Faith and Flows

An Empirical Study on the Effects of Religious Discrimination on Trade

“The Gods have provided commerce over the sea” - Theseus, king of Athens

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Acknowledgements

This thesis marks the end of five years of studies at the University of Oslo. First I would like to thank my supervisor, Professor Karen Helene Ulltveit-Moe for her guidance throughout the writing process. I would also like to thank my mother and father for their proofreading and feedback. Finally I would like to thank everyone who have contributed to some fantastic years at university, and especially to those who have provided welcome writing breaks throughout this semester.

I bear sole responsibility for any and all inaccuracies.

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Abstract

This thesis explores the effects of religious discrimination on bilateral trade. Religion has permeated most societies throughout time, and this is not the first research to suggest that religious factors might have an impact on trade. However, where other papers have taken more of an interest in shared religious characteristics across countries and how these affect trade flows, this paper seeks to answer if suppressing religions also matters.

I estimate this effect by employing the gravity model of international trade and augment- ing it to include a measure of religious discrimination. The empirical results are found by performing regressions using Poisson pseudo maximum likelihood estimation. The estimation is specified with different forms of fixed effects to control for unobserved heterogeneity.

Furthermore, I test if the impact differs depending on which religion is discriminated against.

I also check if the terrorist attack of the 11th of September affected the explanatory power of religious discrimination on trade.

The findings indicate that the expected trade flow between two countries is reduced when a country discriminates against a religion which has a presence in the other country. The effect depends both on how severe the degree of discrimination is, and how large the share of people who adhere to the religion is in the other country. Additionally, discrimination against Muslims and Christians in particular reduces the predicted trade flow between two countries.

For both discrimination in general, and for discrimination against Christians and Muslims specifically, the negative effects are stronger if the exporting country is the discriminating party. An interaction analysis on the impact of 9/11 however does not yield much in terms of significant effects on discrimination and trade.

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List of Tables

1 Religious discrimination variables . . . 14

2 Gravity model reference table . . . 27

3 Discrimination against religious minorities by exporters . . . 30

4 Discrimination against religious minorities by importers . . . 33

5 9/11 and discrimination against religious minorities by exporters . . . 37

6 Discrimination against separate minority religions by exporters . . . 39

A.1 All countries . . . 50

A.2 Summary statistics . . . 51

A.3 OLS gravity model reference table . . . 52

A.4 9/11 and discrimination against religious minorities by importer . . . 54

A.5 Discrimination against separate religious minorities by importer . . . 55

A.6 Discrimination against minority religions standardised . . . 56

List of Figures

1 Average discrimination of religious minorities by year. Percent . . . 13

2 The effect of discrimination on trade flows . . . 35

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1 Introduction

Religion and its effect on the economy and on the interaction between peoples and states has always been a topic of interest in the social sciences. The aspect which has driven most of the attention is the meeting of different religions and the outcomes of these meetings. Research on this topic has been extensive, also in recent decades.

In 1993 Samuel Huntington published his influential and highly controversial book “The clash of civilizations”. In it he alleged that after the fall of the Soviet Union, which marked the end of the ideological struggle between capitalism and communism for supremacy, the new struggle that would inevitably arise would be a cultural one, with religious differences as a direct cause of conflict (1993). This was seen as inevitable, due to increased interaction stemming from globalisation.

Though the idea of a clash of civilisations has been refuted by many (Mungiu-Pippidi and Mindruta 2002). (Russett, Oneal, and Cox 2000), it should be uncontroversial to say that some interactions have caused tensions recently. The immigration debate in many European nations is characterized by harsh rhetoric with a trend of increased explicit attacks on people of other religious backgrounds (ENEAR 2017). Currently, there are restrictions on specifically Muslim immigration in several European Union nations. For the ones allowed entry, there is schooling to ensure that the immigrants are informed of the values they are expected to respect in their new country (ibid.).

Of other, even more tangible examples, the Muslim Rohynigya minority was attacked and driven away from their homes in Myanmar in large numbers in 2017 (UN High Comissioner for Human Rights 2018). In China, Amnesty International reports that up to one million people of the Muslim Uyghur minority are detained in internment camps for “re-education”

(2018). At the same time, China’s economic power is so great that no Muslim countries mentioned the Uyghurs when China’s human rights record was up for review in the UN in the fall of 2018 (Rønneberg 2018).

For an economist, this raises the question of whether there are economic consequences of religious discrimination of this nature. In this paper, my aim is to explore the trade related aspects of this question, specifically how much of an effect religious discrimination has on

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trade between two countries. To do this, I identify exporter countries that discriminate against a religious minority and try to find if this affects trade with importers where the religion has a foothold. This is also done in the opposite direction to see if it makes a difference if it is the exporter or importer that discriminates against the religions of its trading partners.

This leads to the null hypothesis that religious discrimination does not affect trade between nations. The competing hypothesis is that the more a country discriminates against a minority religion, and the larger the share of people who adhere to that religion is in a prospective trading partner, the smaller the predicted trade flow between them will be.

To find if the null hypothesis holds or can be rejected, I rely on the gravity model of international trade. The gravity model is used to predict aggregate trade flows between nations, and has been shown to be an accurate and useful tool for this purpose (Baier and Bergstrand 2007). I augment the gravity regression to incorporate a term for the cross country effect of religious discrimination. The findings indicate that discrimination against religious minorities indeed has a negative impact on trade, and that this impact is largest when the exporter is the discriminating party. Furthermore there are robust findings that this holds both for discrimination against Christians and Muslims, and that the effect is largest for discrimination against Muslims.

One event that could possibly have acted as a linchpin for religious discrimination to increase its importance in bilateral relations was the terrorist attacks on the United States on the 11th of September 2001. The justification given by the perpetrators for their actions was that they had declared a jihad, a holy war, against the Christian West (Silberman, Higgins, and Dweck 2005). This was also followed by a war declared by mostly Christian countries on a Afghanistan, a Muslim one. By performing an interaction analysis, I check if this event actually had an effect on the importance of religious discrimination or of shared religion on trade. This analysis however, yields little in terms of significant results.

The rest of the paper is structured as follows. First I present earlier work on religion and trade, with a distinction between the works of historians and economists. This section is concluded with a brief summary of how my paper fits in the context of the earlier works. The next section is devoted to the data on both trade and religion used in this paper, and contains

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a description of how the data were collected and merged, as well as a description of how the variables on discrimination against religious minorities were constructed. Section 4 details the gravity model of trade and the work that has been done on it throughout the years. This leads to how I specify the model used in this paper, performing Poisson Pseudo Maximum Likelihood regressions, and using different types of fixed effects to account for multilateral resistance unaccounted for in the model. The penultimate section contains the empirical analysis and the discussion of the findings. This is followed by my concluding remarks and suggestions for further research.

2 Background and the contribution of this paper

The purpose of this section is to provide an overview of the previous research done in the field of religion and trade to have some theoretical basis on which to anchor the later findings in the paper. Traditionally, the interaction between religion and trade has not fallen under the purview of economics, while historians and sociologists have taken more interest in the subject. Section 2.1 is therefore devoted to the literature on religion and trade based on historical sources. These sources focus more on the interaction between people of different religions than economists, who have tended to explore the effects of sharing a religion across borders.

In section 2.2 I will give a brief summary of related literature by economists based on gravity analyses. Section 2.3 will detail how my paper contributes to the field.

2.1 Historical sources on religion and trade

The epigraph attributed to the semi mythical Athenian king Theseus (Sacks 2014 p.76), as recorded by ancient Greek historian Euripedes, is telling for how trade in historic times was not something separate from religious influence. Indeed, the quotation also highlights how trade across cultural divides (in this case across the sea) needed justification from the implicit blessings of the gods. It took an even more explicit by early Christian merchants who sought blessings from priests for their endeavours (ibid.).

Whether religion has had a trade promoting or trade impeding effect has been the source

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of some debate among historians. The prevailing view for a long time was that groups of people with shared religion tended to trade amongst themselves first and foremost, to the exclusion of trade across cultural divides (Antunes 2014). This view however has been challenged by modern historians.

One of them is Philip D. Curtin, who argues that religion has in fact played a positive role in the trade relations between peoples of different cultures from time immemorial (1984).

As he states, “Trade communities of merchants living among aliens in associated networks are to be found on every continent and back through time to the very beginning of human life” (ibid., p. 3).

Diasporas of people from one civilization would be formed in countries outside of their homeland, as civilizations came into contact with one another. The diasporas then served as facilitors of trade with their origin country and the country they or their ancestors had settled in. Examples range from the Phoenicians and the ancient Greeks establishing small communities throughout the ancient world, to Hindu trading communities in Tanzania, Uganda and Kenya in the nineteenth century (ibid.). Other researchers have found that Jewish communities across the Atlantic ocean helped the fledgling overseas trade as the American colonies started to form (Kagan and Morgan 2008).

There are other examples of religion as not only an aid to trade, but a reason in and of itself. The hajj, the pilgrimage to Mecca prescribed by the Quran, drew notice from traders not only of the Muslim faith (Das Gupta 1982). The Frenchman Jean-Baptiste Tavernier described how in the seventeenth century, merchants from all over the Indian subcontinent joined pilgrims on ships sailing to Mecca and sold their goods for great profit. The hajj was thus a cause of a substantial bulk of Indian trade at the time (Coates 1911). As with all eyewitness accounts not backed up by data, it is however important to note that these effects might be overblown, as suggested by Eric Tagliacozzo (2015).

The interplay between religion and trade was also evident when European powers expanded to the east and west to form colonies. The motivation behind Vasco da Gama’s expedition to India was “... to seek Christians and spices” (Marocci 2014 p.94). The early Europeans in America were also encouraged to foster dependency between Christians and non-Christians, and thus pave the way for the religious conversion of the latter group (Sacks

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2014).

At times there were even religious imperatives behind trade between people of differing faiths, specifically between Christians and Muslims. In the sixteenth and seventeenth century, raids by Christians on Muslim villages and vice versa near the Mediterranean ocean were common. The goals of these raids were to capture hostages. The Catholic church decreed that merchants trading with Muslims were obligated to barter for the release of the captives.

This in turn led merchants from the different faiths to trade in goods as well (Kaiser and Calafat 2014).

The religious imperative for the trade of captives however touches on another aspect of the interaction between religions. The idea that religion at all times has only been a positive influence on trade is obviously untrue. Culture clashes has occurred numerous times throughout history, and two nations or cultures at war with one another necessarily trade less with one another as well. There have been many instances of holy war between Christianity and Islam throughout history, and even when the faiths were not at war, restrictions on trade with the historic enemy was often in place.

One such instance was when there was a papal bull banning trade with Muslims in the middle ages. This was in addition to the permanent ban from both Islam and Christianity on the sale to the other of any material that could be used for armaments, including any metals and woodwork (Trivellato 2014). However, exceptions to the rules, such as the ordered trade with captives enabled the formation of black markets where Christians and Muslims traded forbidden goods with one another (Miller 2014).

Often, while there was religious conflict, and in name prohibitions against trading with people of other faiths, the reliance on trade meant that the state would turn a blind eye to merchants flouting the rules. There was for instance still vibrant trade between Portuguese merchants and Muslim merchants even as the Portuguese crown explicitly called for holy war (Marocci 2014). Laws banning the trade between Christians and Muslims were at other times set aside on technicalities, such as when a distinction was made between the Muslim enemy and Muslims living in peace, which allowed merchants to keep trading (ibid.). The same duality was present in Islam. Islam bans trade in impure goods, and some religious scholars took this to mean any trade with goods made by non-Muslims. In the fifteenth century, the

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rise of paper made in Europe, led to a Fatwa, a non-binding yet strong legal opinion, on the trade and use of Christian-made paper by Muslims. The economic necessities more than anything led the Muslim scholar to decide that the trade with, and use of Christian-made paper should still be allowed (Halevi 2008).

Another example of economic necessities trumping the call for religious unity was in France during its early colonial era. Non-Catholics were banned from any colonial holding of France, yet Huguenots were allowed to continue their daily lives, as their fleet of ships and ties with partners in Holland and England meant that their economic importance outweighed the calls for a homogenous society (Bosher 1993).

2.2 The gravity model and its impact on studies on religion and trade

The work on religion’s impact on trade is to a large degree connected to the gravity model of international trade. The model finds that the economic size of a country and its proximity to another act as determinants of trade, and is shown to predict between 60 and 80 percent of the cross-section variation in world trade volumes (Baier and Bergstrand 2007). In section 4.1, the theoretical foundations behind the model, and the model specifications done in this paper are presented.

The popularity of the gravity model in more recent years has led trade economists to research factors that might constitute unobserved trade costs, with some of these factors thought to be of a religious nature. The model is well suited to this purpose, and the studies done on religion and trade mostly relies on a gravity framework for their analysis. The papers presented in this section all employ some variations of gravity equations in their analyses.

The standard way to incorporate religion in gravity equations is as a variable on the shared religion between two countries (Head and Mayer 2014). When two trading partners share majority religion, predicted trade flows have been shown to rise (Lewer and Van den Berg 2007a). Lewer and Van den Berg delves further into this and looks at separate religions’

influence on bilateral trade. They find that the larger the share of people following the same religion across two countries, including minority religions, the higher is the predicted trade

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(2007b). The exceptions are shared Catholicism, shared Islamic faith, and shared Judaism.

The results, especially on the effect of minority religions, give credence to the findings of Curtin, and indicate that when people of a religion spread beyond their origin country, this has a positive effect on trade between the origin and destination country. Zelekha, Sharabi and Bar-Efrat find that Jewish diasporas in the rest of the world act as multipliers to trade to and from Israel (2012), strengthening this argument.

Matthias Helble sought to shed a light on some of the same aspects of religion and its effects on trade as I do in this paper. In his paper “Is God Good for Trade?”, he tries to answer whether religious openness has a positive effect on trade (2007). He creates dummy variables for all the major religions that equal to unity if the religion is present in the exporter country. The product of the sum of these dummy variables for a country pair is what he dubs a religious openness variable. Including this variable in the gravity equation, he finds that religious openness is conductive to trade.

These results also apply for flows of foreign direct investments, with Hergueux finding that the positive effect of religious diversity between trading partners is much larger than the effect of religious similarities (2011).

As mentioned in section 2.1, colonialism had an effect both on trade and on the sharing of religions. It is common to check for colonial ties in gravity papers, as these are thought to be sources of lasting linkages between countries. It seems however that these ties, though important in explaining religious similarities, are decreasingly important for explaining trade flows (Head, Mayer, and Ries 2010). Colonial ties are found by Head et al to have less predictive power in terms of trade flows as time passes from the independence date of the former colony, yet it does still have a positive, if dampened effect (ibid.).

Other research closely linked to this paper is on the institutional effects of religion on trade. One finding is that trust is what drives trade between countries which share religions (Guiso, Sapiensa, and Zingales 2009) (Lee 2013). The idea underpinning this finding is that people who share the same religion are closely linked, and that the cost of cheating the other is high, whcih leads to the fear of the other party cheating you being reduced. Guiso et al. find that sharing a religion leads to increased trade in differentiated goods in particular (2009). They postulate that differentiated goods vary greatly in quality, and when there is

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trust that the other party is not cheating you, you are more likely to purchase these types of goods from them. Lee finds similar results, though with the caveat that the institutional effects of religion varies depending on which religions the country pair share (Lee 2013).

There are also other aspects closely related to religion that might affect trade. Govern- ments which place large emphasis on religion as an underpinning of society often seek to increase their legitimacy and hold on power on the basis of the will of god. Strict religious states, such as Saudi Arabia and Iran, are also usually more autocratic than religiously freer countries. As a consequence, studies on the effect of autocracies on trade should be seen as related to the study of religious strictness on trade. Findings in this field indicate that autocracies trade less than freer countries (Aidt and Gassebner 2010), (Galiani and Torrens 2014). A prediction is then that religious strictness, in the form of discrimination, also will have a negative impact on trade.

A criticism of findings on religion’s impact on trade is that when people answer surveys on religion, they tend to answer according to what they think is expected of them (Barro and McCleary 2003). Barro and McCleary postulate that this in particular leads to results where the percentage of people said to adhere to the majority religion in a country is much higher than what the case is in reality (ibid.).

2.3 The contribution of this paper

This paper departs from earlier research in that what I aim to explore is the effects of religious discrimination on trade. As the preceding sections show, this diverges from research done by historians, which focuses mainly on trade between different religions when faced with other restrictions. It also diverges from research done by economists, which mostly explores the positive trade effects of sharing a religion across borders. Here, in a sense, what is investigated is the suppression of some shared religious characteristics.

It is natural to think that the network effects of a shared religion would be difficult to exploit if one country discriminates against people of that faith. Most of the papers and books focus on how shared majority religion is a source of trade. It is therefore perhaps unsurprising that religious discrimination has received little attention, as one would assume that the majority religion is not discriminated against other than in very select circumstances.

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When Curtin describes how one nation’s trade with another is helped by a diaspora of people from the same cultural background as their trading partner (1984) being present in the latter, there is an implied presupposition that the people of the diaspora are allowed to conduct their daily business without being persecuted. The findings of Lewer and Van den Berg that any sort of shared religion across borders, not only shared majority, has positive effects on trade(2007b), show that Curtin’s research may also hold on an aggregate level.

Accordingly, it is interesting to see if religious discrimination might be a dampener on this effect.

Helble (2007) and Hergueux (2011) who found that religious diversity in both members of a dyad had a positive economic effect on their interaction, touched on the discrimination aspect by concluding that religious openness is good for trade. However, the presence of many religions in one country does not necessarily imply that the minority religions are free from persecution. It still might be unobserved discrimination there which affects trade. The investigation of this is what my thesis will contribute to the field of religion and trade.

3 Data and sample construction

There are two main types of data employed in this study, collected from four datasets in total. The first type is the classical data used in studies that employ the gravity framework of economic factors specific to the importer and exporter as well as trade flows between them and some shared characteristics. The second type is religious data, specifically on the discrimination against minority religions.

3.1 Trade data

The variables commonly used in gravity equations as explanatory variables on trade flows are courtesy of the french research centre Le Centre d’études prospectives et d’informations internationales (CEPII) (Head, Mayer, and Ries 2010). The gravity dataset spans from 1948 until 2015, though only observations in the period 1990 to 2008 are used in this paper. The variables used in the analysis that are sourced from this dataset are, among others, variables on GDP for both exporter and importer, population weighted distance between the two, and

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other geographical variables. Also included are several variables related to shared history, ethnicity, language, and so forth, as well as data on shared regional trading agreements (RTAs) between the country pair. See table A.2 in the appendix for a summary of the variables.

In the gravity data there is also a measure of shared religion of the country-pair. This is a variable on the share of Catholics, Protestants and Muslims in a country pair, taking the highest values when a large share of the importer’s population follow the same religion as a large share of the exporter’s population. The values are held constant for all years.

The data on trade flow used in the paper are sourced from CEPII’s trade history data set (Foquin and Hugot 2016). In addition to reported trade flows from importers (exporters when there is no report from the importer), the data set contains a variable that is equal to zero if the trade between the two countries in a country pair is likely to have been zero for the year in question, and missing otherwise. Trade flow is coded as likely to zero if information is missing for a reporting country; that country reports trade with more than 30 percent of the countries in its trade reports for the given year, and that country reports trade with at least 10 partners. After all the datasets are consolidated, 67 000 likely zero trade flows remain. In the data used for the estimation of the gravity equation, missing values on trade flows are replaced by zeros where Foquin and Hugot found that there likely was no trade. This should be uncontroversial, as most of the observations of likely zero trade flow is to or from small economies that one would expect not to trade with all other nations.

3.2 Religion data

The data used on religious factors with possible explanatory power on trade flows are collected from two rounds of the religion and state project by the Bar Ilan University, hosted by the Association of Religion Data Archives (ARDA) at the University of Pennsylvania.

In the minority religions dataset (Akbaba and Fox 2011), various forms of government restrictions on the practice of minority religions are estimated on a country basis. If at least 0.25 percent of the inhabitants, or 500 000 individuals of a country follow a minority religion, the minority is included in the data.

The data measures 24 different aspects of religious discrimination, from restrictions on

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building places of worship, to mandatory education in the majority religion, to forced conver- sions to the majority religion. The degree of discrimination is measured from zero to two, with zero as no discrimination, one as some discrimination and two as significant discrimination.

The discrimination variables are also combined to form an index of overall discrimination of religious minorities by year. The overall index is calculated as the sum of the values of the discrimination variables, and can take values from zero to 48.

The discrimination data contains yearly observations from the year 1990 to the year 2008.

The variables are coded based on information from human rights reports, academic resources and news media sources.

The data set of round three of the Religion and State project (Fox 2015) contain time invariant variables which measure how large the shares of people who follow separate religions are by country, which are used in this thesis.

If there is one criticism to be levied at the minority religion discrimination index, it is that it is only the sum of the values of all the underlying variables. One could argue that this leaves the index with slightly less explanatory power than it would have with a firmer, theoretically consistent underpinning. To illustrate the point, it is weighted the same if religion heads must be citizens of the state as if people are imprisoned for practising their religion. One of these is obviously a more egregious form of discrimination than the other, and there might be some legitimate questions regarding bias in the index because of this.

Finding a way to weight the variables properly, anchored in theory would be a way to further improve the predictive power.

Fox addresses this issue, and invites researchers to create a consistent system of weights for the variables included in the index. He finds that the researchers cannot agree on how the different aspects of discrimination should be weighted (ibid.). Furthermore, when evaluating the expert weighted indices, the correlation between the non-weighed index and the expert weighted indices are 99.9 percent. The additive index is thereby functionally the same as if it was weighted by experts.

The index is therefore kept as is in this paper to avoid giving arbitrary weights to the different variables based on my own discretion. Though it might contain slight overestimations of discrimination, in defence of the generality of the index as it is employed in this thesis,

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the worst case scenario of the lack of specific weighting is that the countries will be clustered artificially close together in terms of discrimination. The most likely consequence is that the effect of religious discrimination on trade might be underestimated; there is no real reason to suspect that the signs of the coefficients will be wrong.

Another criticism is that the religion shares are time invariant. It is, as mentioned in section 2.2, difficult to measure religion accurately, so relying on fixed religion shares has been the way other papers have dealt with the same issue as well (Lewer and Van den Berg 2007a), (Helble 2007). However, this will necessarily affect the accuracy of the findings somewhat.

3.3 Sample and construction of discrimination variables

The four datasets are combined to form one, which is used in the analysis in this paper.

There is some discrepancy between the countries included in the trade data and the religion data. To harmonise the data, some non-autonomous regions were excluded, as well as some countries that have either seized to exist, or gained independence since 1990.

In total, 170 distinct economies were included in the final data both on the exporter and the importer side, resulting in a total of 558 885 total observations. See table A.1 in the appendix for a list of the included economies.

I constructed a new set of variables to research the effect of religious discrimination on trade. First the indices on discrimination against separate minority religions were converted to ratios where 1 corresponds to the previous theoretical maximum of 48. The discrimination ratio was then weighted by the percent of people who adhere to this religion in the importer country. Finally the mean was taken of all the non-missing values to create a variable Mean weighted discrimination which is the mean weighted discrimination against the importer country’s religions by the exporter. The same exact process was followed to construct a corresponding variable for the discrimination against the exporter’s religions done by the importer.

The intuition behind the explanatory power of the variables is that they will go some way in helping to see whether the theory of trade promoting networks of minorities (Curtin 1984) holds. The link is that if a country actively suppresses a minority religion that has

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a presence in the other country, the possible trade network between the two groups with the same religious beliefs might be unrealised. If the effect of discrimination is negative, it might mean that people are unwilling to trade with a country where their fellow believers are discriminated against.

3.4 Descriptive statistics

Only 21 of the countries included in the dataset have never displayed discrimination against any of their minority religions between 1990 and 2008. As figure 1 shows, the rate of discrimination against minorities has increased in the time frame of reference, which in turn makes it even more interesting to see the effects this has had on factors such as trade.

Figure 1: Average discrimination of religious minorities by year. Percent

The values for the constructed discrimination variables are presented in table 1. The table reveals that the average discrimination against Christians in general is higher than any of the other major religions included, and also higher than the discrimination against any specific

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subset of Christians. All of the major religions have at least some countries where they are discriminated against, though discrimination against specifically Sunni or Shiite Muslims is not very common. The discrimination against Muslims in general has a lot of observations, meaning that Sunni and Shiite Muslims are likely also discriminated against more than it seems, the countries that discriminate against them just do not discriminate between them.

The variables for the mean weighted discrimination take values from zero to 0.74, where 0.74 is the value obtained for country pairs that include either Saudi Arabia or Laos as one party and trading partners where nearly the entire country identifies as Christian. There are observations of these variables for nearly two thirds of country pairs, and there are more than 230 000 non-zero values.

Table 1: Religious discrimination variables

Obs Mean Min Max

Discrimination against Christians 226 641 0.0904256 0 0.7447417 Discrimination against Catholics 182 411 0.0587875 0 0.7425 Discrimination against Protestants 100 521 0.0144132 0 0.5775 Discrimination against Orthodox Christians 14 246 0.0093882 0 0.27125 Discrimination against Muslims 181 911 0.0599746 0 0.7484916 Discrimination against Sunni Muslims 4 100 0.0437391 0 0.248 Discrimination against Shiite Muslims 6 091 0.030251 0 0.7045833 Discrimination against Jews 17 623 0.0010068 0 0.3003749 Discrimination against Hindus 24 729 0.0132632 0 0.61875 Discrimination against Buddhists 31 400 0.0217979 0 0.65875 Mean weighted discrimination 361 706 0.0532542 0 0.7425

N 558 885

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4 Model framework and specification

4.1 The gravity model of trade

The gravity model is a tool to explain international trade flows, developed by Jan Tinbergen (1962) and expanded on by James E. Anderson (1979). It initially drew on Isaac Newton’s law of gravity in name and idea. In much the same manner as the gravitational pull of one object over another depends on its size and proximity to the other object, so to was the size of an economy and its proximity to another supposed to exert a sort of gravitational pull on trade flows.

Perhaps because of its unclear theoretical underpinning in traditional economic theory, the gravity model was not a popular tool of mainstream economics in its early years. In the last decades however, extensive work has been done in trade economics, and gravity equations are derived from standard economic modelling. As a result, gravity equations

“should no longer be thought of as deriving from a murky analogy with Newtonian physics”

(Head and Mayer 2014 p. 132).

In its simplest shape, the gravity equation takes the form

X_ij =GS_iM_jφ_ij (1)

The left hand side variableX denotes bilateral trade from exporter countryito importer country j. The first determinant of trade, G denotes a gravitational constant, measuring the overall conductivity to trade displayed by the cross section of countries. S_i represents countryi’s capabilities as an exporter in general to all markets, whileM_j mirrors this variable as it denotes country j’s capabilities as an importer from the rest of the world. The variable 0< φ_ji <1 captures two important determinants of trade. It factors in trade costs between the country pairs, and combines this with the elasticities of trade of the two countries (ibid.).

The estimation of both S_i and M_j is a difficult task, as what determines exporter and importer capabilities is not immediately obvious. As such, the common way of expressing the gravity equation as a “naive” gravity equation (ibid.) is:

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X_ij =GY_j^aY_i^bφ_ij (2) HereS_iandM_j are replaced by the respective countries’ gross domestic products (GDPs).

The loss of the overall explanatory power of the equation is offset by the gain in terms of quantifiability. Most countries have quite accurate reports of GDP, and it is natural to assume that the value of production of a country is closely linked to its import and export capabilities. It is often assumed that a = b = 1, as there appears to be near unit GDP elasticities (Head and Mayer 2014).

4.2 Model specification

Due to the multiplicative nature of the terms in the gravity equation, it used to be common to take the log of the terms and run OLS-regressions to predict trade flows. John McCallum recognised that the border between Canada and the US probably caused less trade between Canadian provinces and American states than what would be the case sans border given their proximity and economic sizes. The so called McCallum Gravity equation thus took the form

ln Trade flow_ij =β₀+β₁lnGDP_i+β₂lnGDP_j +β₃ln Distanceij+δij +εij

(3) The log of the trade flow between two countries is regressed on the log of the GDP of the two countries, the distance between the two countries, a δ_ij dummy term that equaled one for interprovincial trade and zero for trade between province and state, and an error term.

(McCallum 1995).

Earlier papers on the gravity of trade included no assumptions about multilateral resistance terms that might affect two countries’ propensities to trade with one another (Anderson 1979), (Rose 2000). The dummies McCallum included for intra province and cross border trade was a small step in this direction.

Researchers realised that there were unobserved effects creating biases in their estimations, and tried to internalise these by adding possible explanatory variables. For a while, the

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prevalent solution was adding a remoteness variable which represented the average distance from one country in a dyad to all trading partners except the other country in the dyad (Frankel 1997).

In 2003, Anderson and van Wincoop published their formative paper on the gravity model where they held that current efforts in gravity literature were theory inconsistent since they did not include terms which captured both the resistance of the importer and the exporter to trade with all countries (2003). Considerable efforts in their paper and in subsequent trade literature are devoted to the correct specification of these multilateral resistance terms.

Their specification yields the following gravity equation:

X_ij =GY_i^a¹Y_j^a²d^(1−σ)ρ_ij R_ij (4)

whered_ij is the distance between the exporter and importer. The term of interest isR_ij, which comprises all trade resistance terms other than distance, and is of the form:

R_ij =b^σ−1_ij (P_j

P_i)^1−σ (5)

bij can comprise different factors that may increase or decrease trade between two countries.

In Anderson and Van Wincoop’s regressions, it contains trade costs, while in this paper, it will also include terms of shared language, contiguity, shared trade agreements, and most importantly variables identifying shared religious characteristics. σ is the elasticity of substitution between all goods. The multilateral resistance lies in the price indices ratio of (^P_P^j

i)^1−σ. P_i depends on all bilateral resistances of the exporter, and P_j depends on all the bilateral resistances of the importer. Borrowing notations from Baier and Bergstrand (2009), the formal composition of the price index P_i is

P_i^1−θ =

N

X

k=1

P_k^θ−1 GDP_k

GDP^Worlde^(1−θ)ρ^lnd^ki^+α²^{RT A}^ki^+α³sharedlanguage_ki+...+αnotherf actors_ki (6) where k 6= j. The P_i price index then depends on the GDP constant of the world, functionally equal to the gravitational constant G, and the sum of all factors connected to

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the ease of trade between country i and all trading partners other than countryj. The same non-linear market-equilibrium conditions apply to the P_j term.

The multilateral resistance term picks up how both the exporter and importer have different kinds of non-monetary costs that are related to trade in general, and that either hinders or furthers their trade with all countries. By including the term, the predicted impact of the bilateral terms between the importer and exporter are not skewed by effects that are not specific to the interaction of the country pair.

The model of Anderson and Van Wincoop (2003) is based on Armington assumptions, where each country produce a different good, and consumers prefer consumption of at least some of each country’s production (Armingtion 1969), (Allen and Arkolakis 2014). The Armington assumption allows there to be a source of trade, as consumers in a country are assumed to have preferences for consumption of not only domestic goods. In addition, it is assumed that consumers’ utility functions are homothetic, i.e. there is constant elasticity of substitution (CES) (Anderson and van Wincoop 2003).

4.3 Poisson pseudo maximum likelihood estimation

As demonstrated by the McCallum gravity equation, the common way of applying the gravity equation empirically was to take the log of both sides of the equality and being left with the log of trade flow as the dependent variable.

This method had several attractive features. For one, the math involved is in most cases trivial, after the model one wishes to explore has produced a gravity equation. As mentioned regarding the McCallum equation, when all terms are included multiplicatively, taking the log of both sides of the equation leads to an uncomplicated additive equation.

The norm in trade literature was running least squares regressions on the log transformed equation (Santos Silva and Tenreyro 2006). By its nature, this method led to another attractive feature in the economically very tangible results of the regression, namely, that the estimated coefficients corresponds to the elasticities of the dependent variable to the respective independent variables (Stock and Watson 2014); in this case elasticities of trade. The coefficients for the dummy variables could be considered average treatment effects, i.e. for instance the expected percentage change in trade value of going from not having a regional

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trading agreement with a trading partner to having one (Angrist 2008). Thereby, researchers were able to present their findings in an easily accessible way even to non-economists. As a way of analysing existing or proposed trade policies for instance, least squares regression on log terms intuitively had its merits (Santos Silva and Tenreyro 2006).

A substantial shift in trade literature took place after Santos Silva and Tenreyro published a paper on the issues of taking the log of gravity equations (ibid.). A crucial assumption, sometimes made implicit, for the results of gravity equation regressions to be valid, was that the error term had to be statistically independent of the regressors. However, when checking if this was actually the case, Santos Silva and Tenreyro found “overwhelming evidence” that this condition did not hold. On the contrary, the error terms were heteroskedastic, i.e.

the log linear error term dependeded on the covariates, calling into question the validity of estimated elasticities of log linearised gravity equations. They concluded that previously estimated elasticities were inconsistent at the very least (ibid.).

Additionally, not all countries trade with every other nation in the world every year.

Two small economies situated far from each other might have little reason to trade with one another. For this reason, trade data will usually include several null observations of pairwise trade flows. Even moderately sized economies do not trade with every other economy every year.

As an example Norway, the 29th largest economy in the world as of 2017 (IMF 2018), has a trade flow of zero with 67 of the countries in the data used in this paper in at least one year between 1990 and 2008, for a total of 521 either zero import or zero export observations.

This is corroborated by official Norwegian sources who identify 86 distinct countries that there has been zero trade observations of in the same time frame for 612 total zero flows (Statistics Norway 2018). This is not accounting for missing trade values, of which there are several in both the dataset used in this paper and in the data of Statistics Norway. Overall, there are 196 000 zero trade flow observations in the data, representing 36 percent of all non-missing observations.

Santos Silva and Tenreyro identifies problems related to the way in which zero trade flow

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had been treated in earlier studies.

x→0limln(x) =−∞

When x tends to zero, the log of x tends to minus infinity, i.e. the log of zero is undefined.

As the dependent variable in gravity regressions was commonly specified as the log of trade flows, and trade flows are often zero, this led to some issues for researchers. An early solution to this problem was to drop any zero observations in the data (Bikker 1987). However, excluding observations is likely to bias results (Frankel 1997). Other ways of dealing with the issue consisted of assigning arbitrarily small values to recorded zeros, which resulted in heteroskedasticity problems (ibid.), tobit estimation, or expressing the dependent variable as ln (1 + Trade flow_ij). Santos Silva and Tenreyro found that all these methods resulted in the parameter estimators being inconsistent (2006).

Santos Silva and Tenreyro hold that maximum likelihood estimators are preferable to other estimation methods due to how they place the same weight on all observations. This is especially the case when we do not know the pattern of heteroskedasticity. Furthermore, they present data which show the Poisson pseudo maximum likelihood estimator (PPML) outperforming other estimators in use, as well as outperforming other pseudo maximum likelihood estimators with different assumed probability distributions (ibid.).

The data do not need to be Poisson distributed for the PPML-estimator to be consistent.

Consistency can be achieved as long as the conditional mean is correctly specified (Cameron and Trivedi 2005) (Santos Silva and Tenreyro 2006), i.e. E[y_i|x] =e^xⁱ^β. This weaker condition separates the pseudo maximum likelihood estimator from the maximum likelihood estimator, and the only functional difference in the estimation results is in the variances (Cameron and Trivedi 2005).

Even though the dependent variable is measured in levels rather than in logs, the PPML- estimator yields results that can be interpreted the same as the results of the standard log- linearised OLS-estimator (ibid.). In other words, the coefficients of the log-linearised variables can still be seen as the elasticities of trade, while the interpretation of the coefficients of the level variables, are still the semi-elasticities of trade.

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Another draw of the PPML-estimator is that in contrast to most other non-linear maximum likelihood estimators it is consistent in the presence of fixed effects (Shepherd 2013).

This feature is important for this paper as the regressions will feature fixed effects estimations, as will be discussed in the following subsections.

I follow the same approach as Santos Silva and Tenreyro, and the trade flow term in this paper thus figures in levels instead of in logs to deal with the log of zero issue. The only mathematical operation performed on the trade flow term is to scale it down to ease the computational strain of the regressions.

4.3.1 Multilateral resistance and country fixed effects

In lieu of specifying the multilateral resitance terms, the gravity equation may be estimated using fixed effects as proposed by Anderson and van Wincoop (2003) and Feenstra(2004).

Doing this has been shown in literature to be consistent with the equilibrium constraints placed on the equation by structural approaches (Fally 2015), (Head and Mayer 2014).

As Baldwin and Taglioni explains (2006), including time invariant country fixed effects removes some of the gold medal error of gravity equations; i.e. omitting to account for the multilateral resistance terms in the regression. However, the multilateral resistance terms are assumed to be time variant (Anderson and van Wincoop 2003), (Baldwin and Taglioni 2006).

As there are certainly some changes over time in the religious variables, only controlling for country fixed effects in the regressions will possibly lead to the time caused omitted variable bias not only being correlated with the dependent variable, but with the religious variables as well.

To check whether the country fixed effects model still may yield results with explanatory power on religious factors on trade, the following regression is employed:

Trade flow_ij =β₁ln GDP_ij +β₂ln GDP_ij

+β₃ln Distance weighted_ij +η_ij +α_i+α_j +ε_ij

(7)

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with

η_ij =β₄Contiguity_ij +β₅Shared language_ijβ₆Colonial ties_ij

+β₇Time zone difference_ij +β₈Common legal origins_ij +β₉RTA_ij +β₁₀Shared religion_ij +β₁₁Religious discrimination_ij

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and where α_i and α_j are dummy variables for all importers and exporters respectively and ε_ij is the error term.

4.3.2 Country-pair fixed effects

Including time invariant country-pair fixed effects is assumed to eliminate slightly more of the gold medal error than including country fixed effects only (Baldwin and Taglioni 2006).

This method controls for any unobserved effects that works through each country pair. This has the bonus effect that variables related to distance are eliminated. These variables have long been a source of consternation for researchers when they tried to properly specify them (Cheng and Wall 2005). The normal way of measuring distance is between the capitals of countries, but this implies that transport over land and across oceans cost the same (ibid.).

Using country-pair fixed effects estimation is however impractical in this paper. The reason is that all the variables that serve as controls for the actual impact of religious discrimination on trade are also eliminated, as they are fixed between the country pair and time invariant.

Shared religion between the importer and exporter serves as an important reference point in the analysis, and is eliminated when using time invariant country-pair fixed effects.

4.3.3 Country-year fixed effects

Applying country-year fixed effects means it is possible to account for unobserved heterogeneity stemming from both the exporter and the importer even when they are time variant.

Furthermore, using fixed effects for exporter year and importer year does not require mak- ing strong structural assumptions concerning the underlying model of the gravity equation (Head and Mayer 2014), as market clearing is not a necessary condition for the estimation procedure to be valid (Mayer 2018).

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Following the specifications of the gravity equation from Anderson and Van Wincoop, adjusted to fit the Poisson pseudo maximum likelihood estimation form with country-year fixed effects, the gravity equation takes the form

Trade flow_ijt=β0+β1lnGDPit+β2lnGDPjt

+β3ln Distance weighted_ijt+ηijt+αit+αjt+εijt

(9) whereη_ijt is the same as in equation 8.

Obviously, the GDP-terms for the two countries vary only within each respective country, not across the country pair. As a result, both of these terms will drop out of the estimates.

This is a drawback of country time fixed effects estimation when used in gravity regressions, as the economic sizes of the two countries are some of the key components of the equation, and what is thought to exert the gravitational pull. It is however the same feature of the multilateral resistance term that allows it to be explained away by fixed effects estimation.

As detailed in equation 6, the price indices P_j and P_i do not vary across the country pair, only within country, and the unobserved effects stemming from both these terms are thereby eliminated in this specification. The multilateral resistance term is assumed to be ^P_P^jt

it, i.e.

time variant (Baldwin and Taglioni 2006), and controlling for this should eliminate most of the issues of unobserved trade costs.

4.4 Econometric concerns

A significant part of this section has been devoted to the discussion of the best fit between the gravity equation estimated using country fixed effects or country-year fixed effects. The specifications are compared in table 2, 3 and 4. The advantages of Poisson pseudo maximum likelihood estimation over ordinary least squares estimation also received a thorough treatment in section 4.3. Especially due to the possible biases of removing zero flow obser- vation from the regressions, the tables below employ PPML-estimations. Not to disregard OLS-estimations completely, the traditional OLS-regression is applied as a cross-check to see if there still is some overlap in the estimations. A table corresponding to table 2, using OLS- estimation is included in the appendix. The dependent variable in the OLS-regression differs from the one in the Poisson-regressions in that it is the log of trade flow. As pointed out

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when discussing the advantages of PPML-estimations, the interpretation remains the same.

There is however some difference in how the regressions in the tables below are interpreted.

As discussed, regressions with only country-fixed effects treat the data as cross sectional.

What this means is that the fixed effects control for the heterogeneity that is specific to each country, it does not take into account any heterogeneity that varies only in the time dimension within the country. In a model with country fixed effects, the interpretation of the coefficients is what is the impact of a change in the variable in question that cannot be explained only by factors that are specific to one of the countries and that stay constant over time. In a roundabout way, this means that if there are effects that do not vary with time within a country, these are excluded. To correct for any heterogeneity that is specific only with respect to time, one estimation using only year fixed effects is included, as well as one regression where both country fixed effects and year fixed effects are employed. To control for year fixed effects, dummy variables for all but one of the years from 1990 to 2008 are included. The reason one year dummy is dropped is because if not there would be perfect multicollinearity. This also applies for the country dummies included in the country fixed effects estimation.

The inclusion of country-year fixed effects controls for all country specific factors that vary over time. The difference between this and applying both country fixed effects and year fixed effects is that those control for effects that vary only by country, and effects that vary only by year, respectively. The country-pair fixed effects control for effects that vary only within country by year, allowing for changes in the country pair and time dimension.

When running regressions with country year fixed effects, the standard way of doing this is to create dummies for each importer year and each exporter year. However, doing this often lead to regression matrices becoming large and uncooperative. The reason being that datasets on trade are usually quite extensive, with numerous observations per country and per year, and creating country year dummy variables lead to an infeasibly large number of variables to work with.

The problem of an excessive number of dummy variables is not a new one, and although unproblematic if only looking at country fixed effects, estimations using importer-year and exporter-year fixed effects require more computing power than what is readily available.

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Even though the data used for this paper only spans from the year 1990 to the year 2008, it contains more than 170 distinct importers and the same number of exporters as well.

Creating dummy variables for every importer-year and exporter-year yield nearly 6 500 new variables to be included in the regressions, resulting in matrices that exceeded the memory capacity of standard computers by a fair margin.

To combat this issue, I use the Stata command poi2hdfe (Guimarães 2014). The command allows for two high-dimensional fixed effects to be used in the estimation of a Poisson regression. It builds on thereghdfe command created by Sergio Correia (2014), which allowed for the same procedure with linear regression: The difference is that it is modified to allow for Poisson regressions. It avoids the problem of creating an extremely large matrix by using iterated reweighted least squares to implement the estimation, leading to the same results as if using the normal maximum likelihood estimators (Guimarães 2014).

The theoretical foundation is found in a paper by Paulo Guimarães and Pedro Portugal (2010) on the estimation of models with high dimensional fixed effects. They find that by creating an algorithm that solves the equations resulting from a fixed effects model and iterating between the sets of equations, they find the exact least squares solution. They also show that the same method is applicable to non-linear regressions, and proves that employing this method yields the same results for Poisson regressions as the Stata command xtpoisson.

The estimates of all the regressions in this section are reported with robust standard errors, which already is the default for the poi2hdfe estimator. Estimation with robust standard errors will remove more of the unobserved bias causing heteroskedasticity in the error terms. It could be discussed whether it is actually necessary to place any restrictions on the distribution of the standard errors in the non-country-year fixed effects regressions;

however, estimating using robust standard errors is consistent with litterature (Shepherd 2013) and since the country-year fixed effects estimator is the workhorse of this paper, the other regressions will feature robust standard errors as well to ensure comparable results.

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5 Empirical analysis

5.1 The effects of shared religion on trade

The central purpose of the paper is to see whether some of the religious impact on trade stems from other sources than simply the number of followers of the same religion across country pairs. To have something to compare the new explanatory variable to, it is key to see how the variable on shared religion performs. The results are presented in table 2. The prediction is that the more people share the same religion across two countries, the larger is the trade flow between the two.

It is somewhat surprising that the significant results of the unrestricted Poisson regression and the one with only year fixed effects imply that the more similar the religious make-up of two countries is, the less they will trade with one another. This is the opposite of the expected effects, as well as the effects found in earlier papers (Lewer and Van den Berg 2007a), (Helble 2007). If the earlier findings are not wrong, this might indicate that some unobserved factors work through this variable and skews the results and thus actually require more restrictive fixed effects to control for these.

When estimating the model using more restrictive fixed effects, the picture changes slightly. Applying country fixed effects and country and year fixed effects yield no significant results on the predicted effect of sharing a religion on bilateral trade. It should however be mentioned that none of the 95 percent confidence intervals of the two estimations with country fixed effects overlap with the confidence intervals of the first two estimations. A logic conclusion is then that some of the discrepancy is due to some unobserved heterogeneity that varies only in the importer or exporter dimension.

For the estimation with country-year specific effects, the results are significant at the ten percent level, and yield the expected positive results of shared religion on trade. The effect appears to be quite weak. This indicates that shared religion between two countries might not necessarily result in trade between them if they would not trade with each other otherwise.

The OLS-reference table A3 in the appendix, shows that all of the model estimations, bar the one with no fixed effects (which has non-significant results), find that shared religion has

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Table 2: Gravity model reference table

PPML PPML FE PPML FE PPML FE PPML FE

ln GDP_it 0.799^∗∗∗ 0.812^∗∗∗ 0.596^∗∗∗ 0.705^∗∗∗

(0.00420) (0.00379) (0.0913) (0.117)

ln GDPjt 0.827^∗∗∗ 0.840^∗∗∗ 0.589^∗∗∗ 0.698^∗∗∗

(0.00559) (0.00533) (0.0638) (0.0494)

ln Population weighted distance -0.517^∗∗∗ -0.505^∗∗∗ -0.618^∗∗∗ -0.620^∗∗∗ -0.622^∗∗∗

(0.0140) (0.0130) (0.0612) (0.0615) (0.0124) Contiguity 0.653^∗∗∗ 0.628^∗∗∗ 0.454^∗∗∗ 0.453^∗∗∗ 0.430^∗∗∗

(0.0244) (0.0216) (0.0747) (0.0734) (0.0164)

Shared language 0.332^∗∗∗ 0.317^∗∗∗ 0.115 0.115 0.122^∗∗∗

(0.0206) (0.0194) (0.0717) (0.0719) (0.0176)

Colonial ties 0.176^∗∗∗ 0.151^∗∗∗ 0.145 0.139 0.138^∗∗∗

(0.0456) (0.0465) (0.247) (0.248) (0.0447) Time zone difference -0.0375^∗∗∗ -0.0433^∗∗∗ -0.0213 -0.0217 -0.0203^∗∗∗

(0.00359) (0.00351) (0.0177) (0.0177) (0.00255) Common legal origins 0.227^∗∗∗ 0.242^∗∗∗ 0.181^∗∗∗ 0.182^∗∗∗ 0.187^∗∗∗

(0.0161) (0.0148) (0.0438) (0.0432) (0.00983)

RTA 0.110^∗∗∗ 0.125^∗∗∗ 0.496^∗∗∗ 0.484^∗∗∗ 0.521^∗∗∗

(0.0220) (0.0216) (0.0582) (0.0587) (0.0207) Shared religion -0.628^∗∗∗ -0.639^∗∗∗ 0.0432 0.0408 0.0415^∗

(0.0296) (0.0277) (0.101) (0.101) (0.0225)

Observations 514 336 514 336 514 336 514 336 543 833

PseudoR² 0.860 0.864

Year fixed effects NO YES NO YES NO

Country fixed effects NO NO YES YES NO

Country-year fixed effects NO NO NO NO YES

Standard errors in parentheses

∗p <0.10,^∗∗p <0.05,^∗∗∗ p <0.01

Faith and Flows

Faith and Flows

An Empirical Study on the Effects of Religious Discrimination on Trade

Paal Wanvik Hole

Master of Philosophy in Economics Department of Economics

University of Oslo

November 2018

Faith and Flows

An Empirical Study on the Effects of Religious Discrimination on Trade

Acknowledgements

Abstract

Contents

List of Tables

List of Figures

1 Introduction

2 Background and the contribution of this paper

2.1 Historical sources on religion and trade

2.2 The gravity model and its impact on studies on religion and trade

2.3 The contribution of this paper

3 Data and sample construction

3.1 Trade data

3.2 Religion data

3.3 Sample and construction of discrimination variables

3.4 Descriptive statistics

4 Model framework and specification

4.1 The gravity model of trade

4.2 Model specification

4.3 Poisson pseudo maximum likelihood estimation

4.4 Econometric concerns

5 Empirical analysis

5.1 The effects of shared religion on trade