Economic preferences and trade outcomes

Korff, Alex; Steffen, Nico Article — Published Version Economic preferences and trade outcomes Review of World Economics Provided in Cooperation with: Springer Nature Suggested Citation: Korff, Alex; Steffen, Nico (2021) : Economic preferences and trade outcomes, Review of World Economics, ISSN 1610-2886, Springer, Berlin, Heidelberg, Vol. 158, Iss. 1, pp. 253-304, https://doi.org/10.1007/s10290-021-00431-4 This Version is available at: https://hdl.handle.net/10419/286799 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Review of World Economics (2022) 158:253–304 https://doi.org/10.1007/s10290-021-00431-4 1 3 ORIGINAL PAPER Economic preferences andtrade outcomes AlexKorff1· NicoSteffen2 Accepted: 13 July 2021 / Published online: 4 September 2021 © The Author(s) 2021 Abstract Integrating the Global Preference Survey (GPS) and its data of unique scope on national preference structures in patience, risk attitude and reciprocity into a gravity framework, this paper is the first to explore a potential influence on international trade outcomes of economic and social preferences in a unified setting. Adding to the evidence on preferences’ importance for aggregate outcomes, the authors find marked differences in trade flows and relationships, both on the country-level and between bilateral partners. Their main results suggest that countries differing in their willingness to behave negatively reciprocal tend to trade significantly less amongst each other due to the destabilizing effect of unexpected punishments. On the other hand, countries that are patient or risk-averse tend to shift towards exporting more differentiated goods as opposed to homogeneous goods and vice versa. We propose term and risk transformation considerations as the driving mechanisms for this relationship. Keywords Trade determinants· Non-tariff barriers· Economic preferences· Sociocultural variation JEL Classification F10· F14· D01· D91· Z10 1 Introduction International trade, while recently beleaguered by protectionism and trade wars, is established in economics as an engine of growth, welfare and progress. Its potential for division of labour, specialisation and efficient use of capital is unmatched by any domestic policy, which would inevitably be faced with rigidities and restrictions in these factors. Nonetheless, the intensity of international trade has been stagnant below assumed efficient levels even before the protectionist trends of the present. * Nico Steffen [email protected] 1 Heinrich Heine University ofDüsseldorf, Düsseldorf, Germany 2 Düsseldorf Institute forCompetition Economics (DICE), Düsseldorf, Germany 254 A.Korff, N.Steffen 1 3 The causes for these so-called “dark” trade costs partly remain unknown, preventing both a solution and a more optimal outcome. In response to these anomalies, the trade literature has expanded the concept of economic gravity, based on market size, output and “hard” barriers—i.e. geographic distance and tariffs—by “soft” barriers such as cultural factors. These are based on persistent differences and similarities across countries, ranging from language or colonial history to shared values and even genetic distance. The proposed underlying mechanisms include facilitated communication, reduced informational frictions or historically established ties, but also even less tangible aspects such as shared beliefs and norms, that could foster bilateral trust, for example. By linking data and insights from behavioural economics to the trade context, this paper proposes novel additional mechanisms connecting culture with trade outcomes. To this end, term (patience) and risk preferences as well as reciprocal behaviour are incorporated into a gravity analysis using the novel GPS preference data by Falk etal. (2018). These preferences, likely shaped by culture and society within a given country, might affect negotiations between firms and agents of different nations. They inform their time horizons, influencing discounted values of a deal, their willingness to risk investment into a trade relationship and their responses to (non-)cooperative behaviour. Each of these four aspects could help explain trade outcomes and anomalies in volume between economically similar country pairs. Analytically, this paper is the first—to the best of the authors’ knowledge—to join the gravity model for trade with specific data on such behavioural preferences at the population level. That is, the observed preferences directly relate to economic decisions in the realm of contract theory and incomplete contracts. The paper expands the gravity model by a new dimension of soft barriers, which also provide a possible explanation for the effect of cultural distances on trade outcomes and for “missing trade” as well as “dark trade costs”. Preferences are integrated into the gravity framework using a two-step approach: Distances in preferences across countries are incorporated into a standard gravity model to measure the effects of bilateral differences in reciprocity as well as term and risk transformation considerations. The national preference levels, meanwhile, are analysed by decomposing the multilateral resistance terms of the gravity equation, a country’s overall propensity towards trade, thus discerning potential shifts in trade inclination associated with specific national preference leanings. The GPS is particularly well-suited to this analysis because of its broad scope and quality, covering 76 countries through nationally representative surveys on these preferences and experimental validations for them. This analysis finds that the distance in reciprocity between countries and term and risk orientation levels of a given country affect trade outcomes. Specifically, a distance in negative reciprocity between countries, i.e. the willingness to engage in costly punishment, is detrimental to export volumes by introducing unexpected costs in case of transaction issues. The national level of long-term orientation and higher levels of risk aversion, meanwhile, lower the average trade barriers for differentiated goods, while raising them for non-differentiated ones. Less risk aversion and shorter term orientation have the opposite effects. This reflects term and risk transformation concerns by 255 1 3 Economic preferences andtrade outcomes national players, wherein a product mix is selected whose trade and contract conditions reflect term and risk profiles. That is, the longer the term orientation the more complex and differentiated the product mix, and: the more risk-averse a country, the less volatile and more differentiated the produced goods. Lastly, preference effects are overall stronger for exporters, differentiated goods and OECD-countries, indicating the link between preferences and negotiation intensity as well as the financial risks being placed on the exporter. The rest of the paper is structured as follows. Section2 relates the paper to the existing literature, leading into Sect. 3 which summarizes the hypotheses of the analysis. Section4 introduces the data and the empirical strategy, whose results are reported in Sect.5. Section6 discusses a set of robustness checks and their results. Finally, Sect.7 concludes with a short discussion on the results and considerations for further research. 2 Related literature This paper aims to link two fields of economic literature: the analysis of trade flows, especially on soft barriers to trade, and the literature on behavioural economics. Contract theory is utilised as the mechanism for this conjunction. Our analysis builds on previous analyses of trade finance and the incomplete contracts governing trade, which have touched upon the behavioural considerations at the core of this paper. Trade finance explicitly deals with the management of risk in a trade context. Therein, risk is often placed primarily on the exporter given the wide prevalence of Open Account (OA) arrangements, i.e. the importer paying only after having received the goods (Ahn 2011; Antras 2003). Given these risks and uncertainties, it is unsurprising that complex, but (still) incomplete contracts govern the actual trade interactions. In addressing these complex contracts and their dynamic nature, Defever etal. (2016) and Kukharskyy (2016) have shown that only sufficiently patient firms may establish efficient long-term supplier collaborations. Findings by Araujo etal. (2016); Aeberhardt etal. (2014) and Rauch and Watson (2003) point to relationships being developed slowly, starting with small test orders until a relationship is established, reflecting reciprocity considerations. We build upon both of these relationships by attempting to investigate their more abstract, global effects using the GPS. In regard to the broader trade literature, we contribute to the shift in discussion from conventional drivers like size and transportation costs to “missing trade” (Trefler 1995) and “dark” trade costs (Head and Mayer 2013) by proposing a novel influence in the form of national preference leanings and a simple mechanism by which its influence would occur. Closest to our analysis are Frank (2018) and Jaeggi etal. (2018) who analyse cultural attitudes as factors for overall economic development. These are future orientation and other measures from the GLOBE survey in the former and a dyadic values distance measure computed using the World Values Survey in the latter case. Genetic and values distances are consequently used as robustness checks in this analysis. However, we differ from their approaches in two ways: by also considering potential positive effects of such divergence and by 256 A.Korff, N.Steffen 1 3 proposing a channel for these effects by introducing behavioural concepts and contract theory. We follow previous analyses on cultural differences such as Melitz and Toubal (2014), who analysed the effects of a shared language and revealed a channel of shared ethnicity in the process. Another example would be Lameli etal. (2015), who discovered a trade-boosting effect between German regions sharing similar dialects. Similarly, Felbermayr and Toubal (2010) have investigated a proxy for cultural proximity as a determinant of trade flows and Fensore etal. (2017) have introduced genetic distance as a measure to this end as well.1 Lastly, bilateral trust, which could also be considered a preference, has been extensively studied in the trade context, for example by Guiso etal. (2009) or Yu etal. (2015), who find positive effects of trust on trade activity. Naturally, our analysis also connects to the behavioural economics literature leading up to the GPS study itself (Falk etal. 2016, 2018) and to research by Dohmen etal. (2016) linking patience with national economic development. Thereby, we also relate to a broader literature linking the preferences also measured in the GPS to individual outcomes. This includes Sutter etal. (2013) who link time and risk preferences to saving and smoking decisions, Kihlstrom and Laffont (1979) who investigate entrepreneurial activity with regards to the risk preferences of the players, and Fehr etal. (1997); Fehr and Gächter (2000, 2002) and Nikiforakis (2008) who all investigate the effects of reciprocity preferences on collective action and their outcomes. Here, we contribute to the literature by observing expressions of these individual decisions and outcomes at the aggregate level. 3 Hypotheses In this section, the hypothetical mechanisms by which preferences might affect trade outcomes are presented and their directions summarised. For each of the four GPS dimensions patience, risk, positive and negative reciprocity, two effects are considered: the level of a given country and bilateral differences across countries. That is, a preference could matter unilaterally and shift a country’s general attitude towards trade or it could matter only in contrast to the partner’s preference distributions. The resulting dimensions and their proposed effect directions are displayed in Table1 at the end of this section. As a simple guiding structure, a Home producer looking to export to a Foreign distributor is considered. Alternative settings, e.g. a Home firm looking for a supplier or the viewpoint of the Foreign firm, could be conceived analogously, but the prevalence of Open Account (OA) contracts usually burdens the risk on the exporter.2 Thus, it is the Home producer who has the largest incentives to carefully 1 The latter’s general (causal) influence has been challenged by Giuliano etal. (2013), however. 2 While Importers in countries with a low institutional quality, i.e. weak contract enforcement, may have to take on the risk position to initiate a relationship using CIA payments, exporters often switch back to open account terms even in these cases, once the relationship has been established and trust formed (cf. Antras and Foley 2015). Hence, this setting is not considered to be the norm here. 257 1 3 Economic preferences andtrade outcomes consider his trade relationships, implying a potentially larger role of exporter’s preferences, as well. In this setting, producers and distributors have to choose between domestic and international relationships, wherein the latter are associated with greater uncertainties due to a - typically - lower knowledge of these partners and their markets. These uncertainties include the timing of payments as well as default and recoupment risks, for which the players would formulate expectations and, in a next step, expected values for a given trade relationship. These calculations and thus the outcomes might be shaped or influenced by the preference leanings of the players involved. In the following, the rationale for each of the four dimensions investigated in this analysis is provided. Patience Patience measures the willingness to forego short-term profits for higher gains in the long-run and therefore factors into the evaluation of the timing of payments in the setting outlined above. Higher levels of patience should imply a lower discount (higher interest) rate, i.e. a higher tolerance for delayed payments, and consequently benefit trade volumes and intensity by raising the expected value of trade. Another rationale for this hypothesis is the understanding of trade as a means to achieve efficiency gains by constructing international supply and distribution networks, allowing greater specialization. Since the construction of these networks, from contract negotiations to physical construction and transport times, requires time and effort, more patient agents would be more likely to engage in these activities than impatient agents. Conversely, impatient agents would be more likely to engage with local partners despite a potential long-term disadvantage. This mechanism could also be interpreted as a form of comparative advantage, in which the opportunity costs for production are lowest for those goods which best fit a country’s level of patience by avoiding costly term transformations, inviting specialization into these goods. The gains from that specialization would manifest best between differently patient players exploiting their respective comparative advantages. Hence, these gains would define the effect of distances in patience on trade and should be positive. However, the more pronounced effect of a comparative advantage is the change in the composition and structure of trade, which might overshadow a potential bilateral increase in volumes. Risk-taking The GPS risk preference can be understood as an inverse measure of the average risk premium a given population is willing to pay. For highly risk-tolerant societies, this measure can take a negative value, while it is positive for the average population. With regards to trade outcomes, less risk-aversion should facilitate the formation of trade relations as it would heighten the tolerance for trade-associated risks and uncertainties such as defaults on payments. For exporters in particular, this tolerance would be required so as to compensate for the risks placed on them by the structure of trade finance. However, considering the greater picture of trade, it might be risk-minimising to diversify into a multitude of international relationships, lowering the exposure to local shocks and individual contracts. Note that this diversification argument applies to both securing access to inputs and to maintaining steady sales and cashflows. 258 A.Korff, N.Steffen 1 3 Hence, the effect of unilateral national risk-taking levels on trade outcomes is unclear ex ante. On the other hand, the effect of bilateral distances in risk attitudes on trade should be positive. A divergence in risk perceptions should allow for forms of arbitrage and for risk transformation, similar to the hypothesis for patience, with regards to the product mix of the players of a given country pair. This, too, can be viewed as a form of comparative advantage, wherein less risk-averse players have lower opportunity costs in producing goods with a higher perceived commercial risk and viceversa. Trade then generates efficiency gains by reducing the amount of risk premia needed to facilitate overall production. Positive reciprocity Positive reciprocity is the willingness to reward cooperative behaviour and positive actions, i.e. a propensity to “return a favour”. In general, the presence of a more positively reciprocal player should stabilize commercial agreements by inducing cooperation. This can be achieved through positive feedback loops caused by reliable and timely deliveries and payments, which would build up goodwill on both sides of the relationship.3 In contract terms, positively reciprocal actions could lower the perceived risk of defaults or assume the shape of more accommodating terms of payment within an active and ongoing relationship, thus lowering costs and building trust. Table 1 Summary of the hypotheses for preferences This table summarizes the hypothesized effect of the four preference dimensions patience, risk attitude, positive and negative reciprocity on trade outcomes as well as for the average bilateral distance over all dimensions. It provides hypotheses for both the unilateral level of a preference dimensions and the bilateral distance between two countries in that dimension + implies a positive relationship, − implies a negative one, +/− an unclear relationship and +/0 one that could be positive or non-signif- icant. An empty entry signals that no effect can exist Dimension Effect on Trade Values Preference Level Distance in Preference Patience + +/0 Risktaking +/− + Pos. Recip. + + Neg. Recip +/− − Overall − 3 Corresponding results or interpretation are common in the literature. Fehr etal. (1997) have stressed the importance of reciprocity in non-enforceable contracts especially, while Gächter and Herrmann (2009) showed that positive reciprocity may induce selfish types to cooperate. Cable and Shane (1997) propose positively reciprocal cooperation as a key aspect in an entrepreneur’s efforts to acquire capital and develop alliances with larger companies. 259 1 3 Economic preferences andtrade outcomes Consequently, the distance in positive reciprocity should have a positive effect due to the implied presence of one highly positively reciprocal partner. In such a relationship, the reciprocal behaviour of that partner would be viewed akin to a standard gift exchange (cf. Akerlof 1982) and thus strengthen the relationship between the players. Moreover, the effect of positive reciprocity can only manifest within existing relationships. Hence, it would neither shift the overall approach to trade nor the extensive margin of trade, defined here as the number of bilateral non-zero trade flows on the three-digit SITC industry level. Negative reciprocity Negative reciprocity describes a willingness to conduct costly punishment of non-cooperative behaviour and negative actions. A hypothesis on its effects is complicated. On one hand, higher levels imply a willingness to punish deviation from contracts and agreements—even beyond a monetarily rational level—,thus raising the cost of a breach of contract once it has been established. While this might partially deter some initial agreements in the first place, the prospect of a more credible punishment could help to prevent deviation by raising the costs to the partner deviating from the contract. In this way, it may foster the establishment of persistent trade relationships. Dohmen etal. (2008), for example, highlight this ability to make credible threats as a potential bargaining advantage. However, this seems to hold true for milder forms of negative reciprocity only. In its strongest forms—decisively taking revenge and anti-social punishment—, negative reciprocity may actually hinder coordination and cooperation (Gächter and Herrmann 2009; Herrmann etal. 2008). In a contract framework, the risks associated with the threat of punishment could become higher than the prospective gains from trade, causing a rational player to abstain from the deal. As an example for this perspective, Caliendo etal. (2012) observe that a propensity to take revenge negatively affects the probability of staying in entrepreneurship, suggesting that high levels of negative reciprocity reflect non-cooperation and reduce one’s own profits. More importantly, if partners differ in negative reciprocity, the actions of the more negatively reciprocal partner might antagonize or alarm the less reciprocal partner. Thus, larger distances in negative reciprocity are expected to reduce bilateral trade. For the level effect, no clear prognosis is possible. Overall Bilateral Distance Following the literature on shared characteristics in trade such as language, ethnicity and culture, the effect of overall preference distances between two countries is also analysed. This serves two purposes. First, it allows a comparison to studies regarding such shared characteristics and to control for a potential correlation with them. Second, it allows for testing the hypothesis that partners with more similar preference sets would be more likely to trade with one another solely on account of that greater similarity causing affinity. Given the diverging proposed directions for the preference dimensions specified above, such an effect is unlikely to emerge at the overall level. 260 A.Korff, N.Steffen 1 3 4 Data andempirical strategy Mapping and isolating the potential impact of preferences on trade requires a comprehensive, three-part data set consisting of the GPS’ preference data, the corresponding trade data and a set of cultural and institutional controls. The following subsections will be dedicated to describing the data used and the empirical strategy. 4.1 Data Preference Data The main variables of interest are the GPS’ results detailing a fourdimensional preference structure for 76 countries: patience, risktaking, positive and negative reciprocity. Patience is therein understood as a broader measure of term orientation or time discount considerations, whereas risk assesses the average risk premium of a given population. Positive reciprocity is the willingness to reward cooperative behaviour and, consequently, negative reciprocity the willingness to conduct costly punishment of non-cooperative or deviant behaviour.4 All preferences are considered to be persistent, underlying convictions or notions, related to upbringing, education, norms and other societal trends. The GPS was conducted alongside the 2012 Gallup World Poll, utilising the infrastructure and scope of that survey to gain both coverage and size. The Gallup World Poll interviewed representative samples of at least 1000 persons per covered country and uses tried weighting techniques for these samples to match a nation’s population. The GPS’ data covers all important global economies with the possible exception of Africa. Around ninety percent of world population and GDP lie within the sample borders. Africa’s coverage is less dense than for the other continents, but both Sub-Saharan and North African countries are included, which permits their use without disregarding the structural differences imposed by the Sahara desert (see Falk etal. 2018). This scope permits conclusions beyond the traditionally available data from more developed countries only. This size and the World Poll’s methodology elevate the GPS above previously available measures. Additionally, the survey items—except for negative reciprocity—are experimentally validated (see Falk et al. 2018), in that incentivized experiments were conducted to evaluate the fit between survey answers and revealed preferences in the experiment. This factor differentiates the GPS from other typically questionnaireonly surveys of similar intent by contextualizing the preferences as economic. The focus is shifted from abstract cultural measures and perceptions to their role in decision-making. Via that channel, these preferences inform negotiations, defining term and risk profiles and behaviour in interaction. As for the preferences themselves, they are provided in a normalized distribution, calculated in a three-step procedure. First, individual-level data on the experimental and survey data is combined using weights obtained by OLS regression on behavior 4 The GPS also includes assessments of the preferences altruism and trust, which were not used in this analysis. For further information, see the Appendix Table7. 267 1 3 Economic preferences andtrade outcomes 5 Results 5.1 Standard gravity The results from estimating the intensive margin of trade via PPML are reported in Table3. Specification (1) is a conventional gravity equation regressing bilateral exports on distance14, contiguity, colonial relationships, existing regional trade agreements, a shared language and country fixed effects. With the exception of common language (lng) the coefficients have the expected directions and are significant at least at the one percent level. The non-significane of common language does not change when using native and spoken language dummies. This result is in line with Melitz and Toubal (2014), who likewise find insignificant language effects when using PPML estimators15 and whose language data is used in this analysis. Specifications (2) and (3) incorporate a bilateral distance in preferences measure similar to Jaeggi etal. (2018) or Spolaore and Wacziarg (2018) regarding values and genetics. This variable is defined as the unweighted average of the l single preference distances: dpref = 1 l∑ l k( � � � zki −zkj) � � � . It measures whether preferences affect outcomes simply by being different between partners, which would speak for the overall preferences reflecting or proxying for a simple cultural (dis-)similarity. Such an outcome is not observed as dpref is non-significant in both models. Its inclusion does not alter conventional gravity parameters, implying little correlation between these variables and the preferences, given fixed effects. In specifications (4) and (5), single preference distances across countries are included. Specification (5) also incorporates measures for distance in legal system quality leg.qlt16 and comleg, a dummy indicating whether a pair shares the same legal tradition. These are added to insure that the effect of reciprocity is not related to non-performing legal systems which might be conducive to punishing behaviour as a means to compensate for the lack of legal recourse (cf. Herrmann etal. 2008).17 This separation reveals a highly significant effect of distances in negative reciprocity (5) T ij =exp (||| zi−zj ||| 𝜻+Si+Mj+𝝓′ ij𝜽 ) +𝜈 ij 14 The measure is constructed by taking the natural logarithm of the average distance in kilometres between the most important population centre’s of the two countries as calculated in Mayer and Zignago (2011). 15 Overlap with the colonial relationship dummy may partially explain these results, as both are relatively broad measures for many-faceted conditions and durations of national exposure. 16 That measure is drawn from the Worldwide Governance Indicator rule of law (in levels) using absolute differences, equivalent to the preference distance calculation. 17 Note that these parameters are significant and conducive to trade, which likely stems from the facts that navigating a system of similar design is easier and that large distances in legal quality imply the presence of one strong legal system within the pair. (The cases when both countries—or none—have a strong legal system are captured by the fixed effects.) Directions and significance also match the analysis by Yu etal. (2015), who also use WGI data as a bilateral variable. 268 A.Korff, N.Steffen 1 3 on the volume of goods exports. A one standard deviation 0.236)18 increase—e.g. the distance between Czechia and Lithuania—, would decrease the respective trade volume by 12.5% when accounting for legal systems and 14.87% when not. This result reflects the hypothesis that a distance in negative reciprocity might deter the less negatively reciprocal partner from engaging with a highly negatively reciprocal partner due to the latter’s insistence on credible and strong punishment. These punishments—especially if unexpected—would raise the risks of a contract from the perspective of the less negatively reciprocal partner and drive him to limit his exposure to that partner. If punishment has occured already, it might motivate him to end that contract. In the same vein, the negatively reciprocal partner might execute a “grim trigger”-like strategy and thus end the relationship permanently. Regarding our hypotheses, these adverse effects appear to outmatch the potential commitment effect (or: reduced incentive to deviate) caused by a higher willingness to commit costly punishment.19 The distance in risk is also significant, albeit only at the 10% level, with an effect similar in size to that of dnegrec when accounting for legal systems.20 This corresponds to the diversification or risk transformation hypothesis, in that more riskaverse countries would outsource riskier enterprises, preferring to import their produce—and vice versa. This particular match of a more risk-averse and a risk-tolerant partner may facilitate agreement on the form of trade finance contracts because both partners could agree on allocating risk to the less risk-averse side. Given the significance and robustness issues with this result, it needs to be treated with caution. 5.2 Differentiated andnon‑differentiated goods Expanding on the aggregated results, specification (5) of Table 3 is used for an analysis on differentiated and non-differentiated goods, according to the Rauch (1999) specifications at the 3-digit level. That separation yields two sets of comparable trade volumes and produces reasonable results for conventional variables. Most notably, distance matters significantly more for non-differentiated goods. The other conventional variables are comparable across the subsets and specifications. Legal quality continues to matter, though a common legal system appears insignificant for non-differentiated goods. The latter is likely a result of the more formalized exchanges governing non-differentiated goods trade, which reduce the importance of legal recourse. Also, as in the aggregated specification, the overall preference dimensions remain non-significant. 18 Summary statistics for the preference distances are listed in Table7 of the Appendix. 19 In line with behavioral and managerial literature, it would have been sensible to distinguish between costly, but rational punishment and acts of revenge. Those are the forms of negative reciprocity which have been queried by sub-questions for the GPS. Unfortunately, that data has not been provided in the publicly available data set. 20 Specifically, trade increases by 14% in volume when drisk changes by one standard deviation. That deviation is 0.338, equal the distance between Great Britain to Rwanda. 269 1 3 Economic preferences andtrade outcomes In general, preferences appear to matter less for non-differentiated goods, which can likely also be attributed to the more formalized exchanges in play. Of the GPS dimensions, only bilateral distances in negative reciprocity matter for non-differen- tiated goods and their significance is also diminished compared to the aggregate or the differentiated cases. For differentiated goods, the results for bilateral distances in negative reciprocity remain similar to the aggregated results.21 Distance in risk is still significant at the 10%-level only, but solely for differentiated goods. This fits the risk transformation hypothesis as transformation can only occur with different risk profiles. If risk-averse players self-select into the less volatile differentiated goods, their export markets must be less risk-averse, or else they would have the same production profile. On the other hand, more risk-tolerant players benefit from trading with more risk-averse partners willing to pay a premium for the risk avoidance. Hence, the effect is positive. Secondly, non-differentiated goods can be traded on exchanges, thus reducing the options for less risk-averse players to strike direct bilateral agreements for risk transformation purposes22. Distances between countries in positive reciprocity have a weakly significant, positive effect on trade volumes for differentiated goods (specification (2) of Table4in the Appendix), whereas there is no significant effect for non-differentiated goods. The coefficient corresponds to a 9.2% increase in trade per standard deviation (0.31; equal the distance between Austria and the Netherlands). This positive effect, if robust, likely reflects the stabilising effect of rewards by the more positively reciprocal player towards his partner, for whom this behaviour would be unexpected given his different reciprocity profile, but beneficial. In this dimension, cultural distance appears to have a positive effect on the intensity of trade. This further highlights why the overall distance in preferences is not significant and presents a case wherein contrasting values or preferences might be beneficial to economic exchange. 21 Interestingly, this relationship becomes more nuanced when considering OLS and alternative PPML estimations of the gravity specification. A summary of these is displayed in Tables 10, 11 and 12 in the Appendix. In an OLS setting, the coefficient for dnegrec loses significance and even becomes positive for differentiated goods. It becomes significant and negative again when weighting the observations with their level trade flows and remains negative for all alternative PPML estimations. The divergence between methods notably subsides for the OECD subset (see Table21 in the Appendix), which has both less zeroes (1% to 6.5% in the full set) and a higher concentration of larger flows. The OLS results are also sensitive to the treatment of zero flows, providing different (non-)significances depending on the implementation. These results hint at a non-linearity of the relationship in line with the hypothesis that high levels of negative reciprocity might enable a trading partner to overcome the obstacles of weak legal institutions. Hence the positive effect in OLS. For larger trade flows, this effect would decline, yielding instead a negative relationship due to the punishment risk. Additionally, countries with larger trade flows are typically economically more powerful and tend to have stronger institutions (or be involved with third-party mediators). In our sample, GDP and the legal quality indicator (leg.qlt) share a correlation of about 30%, while trade volumes are correlated by around 10% with the legal quality indicators for each country in the pair. Further investigation of this link would be interesting, but is beyond the explorative scope of this paper. 22 For which they would not have the same valuation as risk-averse players anyway, given their higher risk tolerance. 270 A.Korff, N.Steffen 1 3 Table 4 Estimation of goods category-specific exports Differentiated goods Non-differentiated goods Agg. Pref. Dist. Single Pref. Dist. Agg. Pref. Dist. Single Pref. Dist. (1) (2) (3) (4) ldist −0.54∗∗∗ −0.54∗∗∗ −0.80∗∗∗ −0.80∗∗∗ (0.07) (0.07) (0.07) (0.06) contig 0.45∗∗∗ 0.45∗∗∗ 0.42∗ 0.41∗ (0.11) (0.11) (0.18) (0.17) colony 0.33∗ 0.38∗∗ 0.43∗∗∗ 0.41∗∗∗ (0.15) (0.14) (0.10) (0.09) rta 0.47∗∗∗ 0.50∗∗∗ 0.26∗ 0.28∗ (0.10) (0.10) (0.12) (0.12) lng 0.10 0.09 −0.21 −0.19 (0.15) (0.14) (0.18) (0.20) comleg 0.24∗∗∗ 0.21∗∗ 0.12 0.11 (0.07) (0.07) (0.08) (0.09) leg.qlt 0.17∗∗∗ 0.18∗∗∗ 0.16∗∗ 0.14∗∗ (0.04) (0.05) (0.05) (0.05) dpref −0.14 −0.30 (0.37) (0.31) dpati −0.19 0.05 (0.14) (0.14) drisk 0.44† 0.34 (0.23) (0.32) dposrec 0.31† −0.14 (0.18) (0.21) 271 1 3 Economic preferences andtrade outcomes For this estimation, aggregated bilateral exports are split into differentiated and non-differentiated goods according to Rauch (1999) three-digit SITC classifications. The variables of interest are the distances in preferences, included as an unweighted average dpref in (1,2) and as single variables dpati, drisk, dposrec, dnegrec (3,4). Standard errors are clustered to Importer and Exporter fixed effects ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 Table 4 (continued) Differentiated goods Non-differentiated goods Agg. Pref. Dist. Single Pref. Dist. Agg. Pref. Dist. Single Pref. Dist. (1) (2) (3) (4) dnegrec −0.46∗∗∗ −0.55∗ (0.11) (0.22) Observations 5112.00 5112.00 5112.00 5112.00 Deviance 2192 ×109 2140 ×109 2784 ×109 2747 ×109 Null Deviance 37598 ×109 37598 ×109 19520 ×109 19520 ×109 Exp./Imp. FE YES YES YES YES 272 A.Korff, N.Steffen 1 3 5.3 Impact onaverage barriers The fixed effects, i.e. the average trade barriers, are extracted from the single preference specifications (2) and (4) of Sect.5.2, Table4, to decompose the effects of GPS preferences on trade outcomes. The effects from the separate sets are used due to the substantial observed differences in coefficients between the goods classes.23 Exporter and importer fixed effects of the two goods specifications are each regressed on average bilateral characteristics relating to the country in question, population and per-capita GDP, a landlocked dummy and the single preferences in their levels. Population pop and per-capita GDP gdpcap are significant and have the expected positive signs for importers and exporters alike. Being landlocked has an expected negative effect, signalling the higher transport costs arising from lacking ocean access. Average bilateral characteristics are included for consistency in accordance with Head and Mayer (2014) only and cannot be interpreted on their own. The results are shown in Table5. As mentioned in Sect.3, it is usually the exporter who faces a delay in payment and the risk of default due to the prevalence of open account (OA) payment forms.24 Indeed, our results suggest that preferences only seem to matter for exporters—displayed in specifications (1) & (3).25 Reasonably then, risk-taking is also the dominant preference. The more risk-averse a population is on average26, the more differentiated goods it exports but also the less non-differentiated ones. However, the effect on differentiated goods is stronger. For differentiated goods, a one standard deviation change in risk-taking (0.302) would lower the average fixed effect (21.7) by 3.35%. This corresponds to a decrease in exports of approximately equal size and a jump from Brazil’s risk attitude to Sweden’s. The same change implies an increase of 2.28% for non-differentiated goods.27 According to these results, higher risk-aversion implies a comparative advantage for and correspondingly a product mix heavy in differentiated goods, whereas a higher risk tolerance yields a product mix intensive in non-differentiated goods. This corroborates the risk transformation argument for distance in risk as alternative suppliers for differentiated goods are scarcer, increasing the bargaining power of the exporter even in an Open Account setting. Given this incentive, risk-averse suppliers would self-select into these goods. 23 Second stage estimations for the aggregate bilateral volumes have also been computed, but found to be non-significant, which is understandable given the effect directions observed in Table5. 24 As nicely summarised by Niepmann and Schmidt-Eisenlohr (2017), all common contract forms except for cash-in-advance (CIA) place a risk and financing burden on the exporter. 25 It must be noted, however, that PPML tends to overstate origin country fixed effects, which might also contribute to the non-significance of the parameters of interest in the second-stage regression for the importer. When estimating the first stage with OLS instead of PPML, the second stage results remain similar for differentiated goods, but patience and risk become non-significant for non-differentiated goods. 26 The variables are normalized to the global average in the GPS data. That mean is risk-averse, not riskneutral. 27 Given these opposing effects for the two commodity class subsets, it is unsurprising that the preferences would have no significant impact on the fixed effects of total bilateral flows. 273 1 3 Economic preferences andtrade outcomes Meanwhile, more risk-tolerant players gain a comparative advantage for non-dif- ferentiated goods as they put less weight on the associated opportunity costs - i.e. the risk of being substituted with a competitor and the general OA risks. As they are effectively offered risk premia for providing non-differentiated goods, risk-toler- ant players will conversely self-select into these. However, the formal organization Table 5 Estimation of fixed effects composition The Fixed Effects represent Average Trade Barriers and are estimated via a two-step approach. Exporter and importer fixed effects are extracted from Table4 specifications (2) and (4) and estimated via OLS using unilateral size and location variables, the average bilateral characteristics relating to the country in question and the single preference variables. Columns (1) and (2) show country characteristics for differentiated goods and (3) and (4) for non-differen- tiated goods. Exporter results are displayed first in each case. Classical standard errors are provided, since robust standard errors are functionally identical ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 Second Stage Differentiated Goods Non-Differentiated Goods Exporter Importer Exporter Importer (1) (2) (3) (4) (Intercept) 20.11∗∗∗ −0.33 21.76∗∗∗ −2.82 (4.62) (2.86) (3.95) (3.19) avg.char −0.18 0.16 −0.23 −0.25 (1.03) (0.64) (0.57) (0.46) pop 0.04∗∗ 0.03∗∗∗ 0.03∗∗∗ 0.04∗∗∗ (0.01) (0.01) (0.01) (0.01) gdpcap 0.44∗ 0.57∗∗∗ 0.78∗∗∗ 0.55∗∗∗ (0.20) (0.13) (0.15) (0.12) landlocked −1.35∗ −0.98∗ −1.30∗∗ −0.92∗ (0.64) (0.40) (0.48) (0.39) patience 1.93† −0.31 −1.62∗ −0.38 (1.05) (0.65) (0.79) (0.64) risktaking −2.41∗∗ 0.33 1.87∗∗ −0.00 (0.86) (0.53) (0.65) (0.52) posrecip 0.46 0.28 0.11 0.03 (0.66) (0.41) (0.49) (0.40) negrecip 0.77 0.27 −0.07 0.76 (0.89) (0.55) (0.67) (0.54) R2 0.56 0.57 0.50 0.60 Adj. R2 0.51 0.51 0.44 0.55 Num. obs. 72 72 72 72 274 A.Korff, N.Steffen 1 3 of the markets for non-differentiated goods and nation-specific resource allotments should reduce the effect on these goods categories.28 Patience yields opposite results to risk: a one standard deviation increase (0.370: from Brazil to Vietnam) in the preference increases exports of differentiated goods by 3.28%, but decreases those of non-differentiated ones by 2.42%. That is, more patient countries export more in differentiated goods and less in non-differentiated ones. These coefficients align with their underlying long-term considerations or discount factor arguments. Differentiated goods require more up-front investment to produce or trade and involve more complex searches and negotiations with potential partners. Both requires a longer time horizon for the players in question, while non-differentiated goods remove the necessity for search and negotiations by accessing organized exchanges. Additionally, different national patience levels allow for term transformation, i.e. firms specializing on products maximizing profits corresponding to their country’s particular time horizons. These foci would differ between nations, constituting competitive advantages and efficiency gains from trade, subsequently reinforcing these specializations. Notably, due to these specializations becoming more niche, gains could be achieved even between partners of similarly high time preferences, thus explaining the non-significance of dpati. Capital allotment—based on discount and interest rates—and contract enforcement would appear as reasonable channels for these specialization procedures.29 As illustrated by Nunn (2007), better enforcement implies more trade in goods which are intensive in relationship-specific investments. Patience, as long-term orientation, would be conducive to considering gains from repeated interactions and more elaborate trade networks. The costs for contract enforcement and its design would then become bearable given the expected future gains from engaging in the effort. Lastly, the change in effect directions for differentiated and non-differentiated goods further underlines that term and risk preferences address more than cultural heterogeneity when it comes to trade. These results also explain the lack of a clear relationship between aggregated, normalized exports and national preferences in Fig.1. 5.4 Breadth oftrade: theextensive margin Lastly, the extensive goods margin of trade and thus the negotiations establishing economic exchange are observed using the 3-digit SITC-industry specifications to transform trade volumes into 240 binary choices per country pair. That is: Does country i export good c to country j? Specification (1) of Table6 presents a conventional PPML gravity estimation for the aggregation of these choices. Specification (2) displays the extensive margin equivalent to Sect.5.1, while specifications (3) and (4) are equivalent to Sect. 5.2. The coefficients and significances for the 28 It is beyond the scope of this paper to analyse potentially biasing influences of nation-specific resource allotments on trade outcomes. 29 The latter is especially notable as inclusion of a legal quality variable causes patience to become insignificant. The corresponding results are displayed in Table13 in the Appendix. 275 1 3 Economic preferences andtrade outcomes Table 6 Estimation of the breadth of trade Basic Grav. Single. Pref. Dist Differentiated Goods Non-Differentiated Goods (1) (2) (3) (4) ldist −0.25∗∗∗ −0.26∗∗∗ −0.23∗∗∗ −0.34∗∗∗ (0.04) (0.03) (0.03) (0.04) contig −0.07 −0.04 −0.04 −0.08 (0.08) (0.07) (0.07) (0.08) colony 0.11∗ 0.08 0.05 0.15∗∗ (0.05) (0.05) (0.05) (0.05) rta 0.01 0.06† 0.04 0.13∗∗ (0.03) (0.03) (0.03) (0.04) lng 0.32∗∗∗ 0.29∗∗∗ 0.29∗∗∗ 0.31∗∗∗ (0.06) (0.06) (0.06) (0.07) comleg 0.10∗∗∗ 0.08∗∗ 0.13∗∗∗ (0.03) (0.03) (0.03) leg.qlt 0.07∗ 0.08∗ 0.04 (0.03) (0.03) (0.03) dpati 0.28∗∗∗ 0.28∗∗∗ 0.31∗∗∗ (0.07) (0.07) (0.07) drisk −0.12 −0.13 −0.11 (0.08) (0.09) (0.09) dposrec −0.10∗∗∗ −0.10∗∗∗ −0.09∗∗∗ (0.02) (0.03) (0.02) dnegrec 0.07 0.07 0.05 (0.06) (0.06) (0.08) Observations 5112 5112 5112 5112 Deviance 78802.90 73272.30 54961.47 27058.91 276 A.Korff, N.Steffen 1 3 Table 6 (continued) Basic Grav. Single. Pref. Dist Differentiated Goods Non-Differentiated Goods (1) (2) (3) (4) Null Deviance 344350.25 344350.25 220156.99 140931.44 Exp./Imp. FE YES YES YES YES Breadth of trade is defined as the number of three-digit SITC goods categories with non-zero export values, i.e. Tij =∑c t cij . The variables of interest are the bilateral distances in preferences, included as single variables dpati, drisk, dposrec, dnegrec (2). Model (1) is a standard PPML gravity equation for comparison, specifications (3) and (4) estimate differentiated and non-differentiated goods, respectively. Standard errors are clustered to importer and exporter fixed effects ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 283 1 3 Economic preferences andtrade outcomes Table 7 Summary statistics for distances in preferences Statistic Mean St. Dev. Min Max Top 2 Bottom 2 dpati 0.415 0.331 0.0001 1.684 NIC-SWE, RWA-SWE ITA-JPN, IND-PER drisk 0.338 0.273 0.0001 1.763 PRT-ZAF, NIC-ZAF GTM-UKR, ISR-KEN dposrec 0.382 0.298 0.0005 1.608 EGY-MEX, GEO-MEX CRI-IDN, POL-ZWE dnegrec 0.309 0.236 0.00002 1.228 GTM-HRV, HRV-MAR BRA-KAZ, ARG-VNM dpref 0.358 0.124 0.061 0.812 GEO-SAU, EGY,ZAF AUS-CAN, AUT-CHE Table 8 Summary statistics for trade on goods category level Statistic N Mean St. Dev. Min Max Trading 1,261,440 0.360 0.480 0 1 Volume 1,261,440 8,935,006.000 228,039,017.000 0 74,214,173,234 Trading is a dummy variable which takes value 1 when a specific goods category is traded between a given country pair and 0 otherwise. Volume is the volume exported from one country to a specific partner country. For each variable, key distributional statistics are provided Table 9 Summary statistics for bilateral trade outcomes Statistic N Mean St. Dev. Min Max Volume (in mio.$) 5256 2144.40 11,944,18 0 425,430.22 Trade Links 5256 86.317 71.344 0 224 Avg. Exp. Partner 5256 67.342 5.756 47 72 Avg. Imp. Partner 5256 67.342 5.990 48 72 Volume is the average value of goods exported from country i to country j for all countries in the set. Trade Links is the average number of goods exported from i to j, again for all country pairs. Avg. Exp. Partner and Avg. Imp. Partner denote the average number of partners for a given exporter and importer, respectively 284 A.Korff, N.Steffen 1 3 Table 10 Alternative estimators for aggregated bilateral exports Estimator OLS PPML PPML OLS PPML PPML (weighted) (share) (share) Sample flow > 0 flow > 0 flow > 0 flow > 0 (1) (2) (3) (4) (5) (6) ldist −1.26∗∗∗ −0.59∗∗∗ −0.62∗∗∗ −0.61∗∗∗ −0.86∗∗∗ −0.88∗∗∗ (0.05) (0.06) (0.06) (0.03) (0.08) (0.08) contig 0.42∗∗ 0.48∗∗∗ 0.41∗∗ 0.65∗∗∗ 0.27† 0.25† (0.16) (0.14) (0.14) (0.06) (0.14) (0.14) colony 0.21 0.33∗∗ 0.28∗∗ 0.50∗∗∗ 0.44∗∗∗ 0.40∗∗ (0.17) (0.10) (0.09) (0.09) (0.13) (0.13) rta 0.22∗∗ 0.35∗∗∗ 0.35∗∗∗ 0.66∗∗∗ 0.36∗∗∗ 0.36∗∗∗ (0.07) (0.09) (0.09) (0.06) (0.09) (0.09) lng 0.85∗∗∗ −0.06 −0.11 0.08 0.25 0.20 (0.11) (0.13) (0.12) (0.09) (0.15) (0.15) comleg 0.41∗∗∗ 0.15∗ 0.16∗ 0.19∗∗ 0.29∗∗∗ 0.29∗∗∗ (0.07) (0.07) (0.08) (0.06) (0.07) (0.07) leg.qlt 0.30∗∗∗ 0.15∗∗∗ 0.14∗∗∗ 0.18∗∗∗ 0.05 0.04 (0.04) (0.03) (0.03) (0.02) (0.04) (0.04) dpati 0.16 −0.16 −0.16 0.72∗∗∗ 0.07 0.05 (0.12) (0.10) (0.10) (0.06) (0.13) (0.13) drisk 0.61∗∗∗ 0.52† 0.50† 1.38∗∗∗ 0.01 −0.02 (0.15) (0.28) (0.28) (0.18) (0.33) (0.32) dposrec −0.15 −0.04 −0.05 0.10 −0.11 −0.10 (0.12) (0.18) (0.17) (0.10) (0.11) (0.11) dnegrec −0.12 −0.53∗∗ −0.55∗∗ −1.19∗∗∗ −0.13 −0.14 (0.15) (0.16) (0.17) (0.12) (0.13) (0.13) Observations 4821 5112 4821 4821 5112 4821 Deviance 4562 ×109 4253 ×109 44.64 42.01 Null Deviance 52347 ×109 51028 ×109 224.63 216.19 R2 0.27 0.20 Adj. R2 0.25 0.17 Exp./Imp. FE YES YES YES YES YES YES All estimations are based on model (5) of Table3, i.e. the PPML estimation of single preference distances with legal quality as control variable. These results are also displayed in column (2) of this table. Standard errors are clustered to Importer and Exporter fixed effects, identical to that model. The alternative estimators follow Mayer etal. (2019), specifically their Table3, in controlling for potential confounding effects of both large and zero trade flows. Column (1) provides the OLS equivalent of the PPML main equation, with zero flows excluded. Column (3) provides the same estimation, but via PPML. In column (4), the model is estimated with weighted least squares; level trade flows are used as weights. Columns (5) and (6) estimate the main PPML model with trade shares, i.e. bilateral imports divided by total imports of that importer, as dependent variable ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 285 1 3 Economic preferences andtrade outcomes Table 11 Alternative estimators for bilateral exports in differentiated goods Estimator OLS PPML PPML OLS PPML PPML (weighted) (share) (share) Sample flow > 0 flow > 0 flow > 0 flow > 0 (1) (2) (3) (4) (5) (6) ldist −1.36∗∗∗ −0.54∗∗∗ −0.59∗∗∗ −0.70∗∗∗ −0.83∗∗∗ −0.85∗∗∗ (0.04) (0.07) (0.06) (0.02) (0.08) (0.08) contig 0.48∗∗ 0.45∗∗∗ 0.35∗∗ 0.43∗∗∗ 0.23⋅ 0.19 (0.15) (0.11) (0.11) (0.05) (0.14) (0.14) colony 0.18 0.38∗∗ 0.32∗∗ 0.64∗∗∗ 0.43∗∗ 0.38∗∗ (0.16) (0.14) (0.12) (0.07) (0.14) (0.13) rta 0.26∗∗∗ 0.50∗∗∗ 0.51∗∗∗ 0.76∗∗∗ 0.43∗∗∗ 0.42∗∗∗ (0.07) (0.10) (0.11) (0.05) (0.10) (0.10) lng 0.92∗∗∗ 0.09 0.02 0.08 0.37∗∗ 0.34∗ (0.10) (0.14) (0.13) (0.07) (0.14) (0.14) comleg 0.44∗∗∗ 0.21∗∗ 0.22∗∗ 0.38∗∗∗ 0.25∗∗∗ 0.25∗∗∗ (0.06) (0.07) (0.07) (0.05) (0.07) (0.07) leg.qlt 0.20∗∗∗ 0.18∗∗∗ 0.16∗∗∗ 0.23∗∗∗ 0.00 0.00 (0.04) (0.05) (0.05) (0.02) (0.04) (0.04) dpati 0.27∗ −0.19 −0.18 0.59∗∗∗ 0.09 0.07 (0.11) (0.14) (0.13) (0.05) (0.16) (0.16) drisk −0.17 0.44⋅ 0.42⋅ 0.43∗∗ −0.47 −0.48 (0.14) (0.23) (0.23) (0.14) (0.38) (0.38) dposrec −0.23∗ 0.31⋅ 0.28 0.35∗∗∗ 0.01 0.02 (0.11) (0.18) (0.17) (0.08) (0.10) (0.10) dnegrec 0.28∗ −0.46∗∗∗ −0.47∗∗∗ −0.20∗ −0.18∗ −0.18∗ (0.14) (0.11) (0.11) (0.09) (0.08) (0.08) Observations 4749 5112 4749 4749 5112 4749 Deviance 2139 × 10 9 1904 × 10 9 35.28 32.72 Null Deviance 37598 ×109 36564 ×109 252.74 242.14 R2 0.35 0.29 Adj. R2 0.32 0.26 Exp./Imp. FE YES YES YES YES YES YES All estimations are based on model (2) of Table4, i.e. the PPML estimation of single preference distances with legal quality as control variable and differentiated goods flow as dependent variable. These results are also displayed in column (2) of this table. Standard errors are clustered to Importer and Exporter fixed effects, identical to that model. The alternative estimators follow Mayer etal. (2019), specifically their Table3, in controlling for potential confounding effects of both large and zero trade flows. Column (1) provides the OLS equivalent of the PPML main equation, with zero flows excluded. Column (3) provides the same estimation, but via PPML. In column (4), the model is estimated with weighted least squares; level trade flows are used as weights. Columns (5) and (6) estimate the main PPML model with trade shares, i.e. bilateral imports divided by total imports of that importer, as dependent variable ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 286 A.Korff, N.Steffen 1 3 Table 12 Alternative estimators for bilateral exports in non-differentiated goods Estimator OLS PPML PPML OLS PPML PPML (weighted) (share) (share) Sample flow > 0 flow > 0 flow > 0 flow > 0 (1) (2) (3) (4) (5) (6) ldist −1.38∗∗∗ −0.80∗∗∗ −0.82∗∗∗ −0.85∗∗∗ −0.97∗∗∗ −0.95∗∗∗ (0.06) (0.06) (0.06) (0.03) (0.08) (0.08) contig 0.55∗∗ 0.41∗ 0.38∗ 0.77∗∗∗ 0.35∗ 0.35∗ (0.19) (0.17) (0.17) (0.07) (0.18) (0.17) colony 0.41∗ 0.41∗∗∗ 0.37∗∗∗ 0.66∗∗∗ 0.43∗∗ 0.42∗∗ (0.20) (0.09) (0.09) (0.10) (0.16) (0.16) rta 0.28∗∗ 0.28∗ 0.28∗ 0.64∗∗∗ 0.37∗∗ 0.38∗∗ (0.09) (0.12) (0.12) (0.07) (0.14) (0.14) lng 0.50∗∗∗ −0.19 −0.24 −0.11 0.16 0.07 (0.13) (0.20) (0.20) (0.10) (0.21) (0.21) comleg 0.31∗∗∗ 0.11 0.12 0.12† 0.30∗∗ 0.30∗∗ (0.08) (0.09) (0.09) (0.06) (0.11) (0.10) leg.qlt 0.17∗∗∗ 0.14∗∗ 0.12∗ 0.04 0.09 0.06 (0.05) (0.05) (0.05) (0.03) (0.08) (0.08) dpati 0.31∗ 0.05 0.05 1.10∗∗∗ 0.20 0.17 (0.14) (0.14) (0.14) (0.07) (0.19) (0.18) drisk 0.60∗∗∗ 0.34 0.29 1.22∗∗∗ 0.29 0.22 (0.18) (0.32) (0.31) (0.20) (0.32) (0.31) dposrec −0.02 −0.14 −0.13 0.38∗∗∗ −0.27† −0.24 (0.14) (0.21) (0.22) (0.11) (0.15) (0.15) dnegrec −0.28 −0.55∗ −0.57∗ −1.38∗∗∗ −0.07 −0.09 (0.17) (0.22) (0.23) (0.13) (0.22) (0.21) Observations 4550 5112 4550 4550 5112 4550 Deviance 2747 ×109 2645 ×109 74.27 70.15 Null Deviance 19520 ×109 18532 ×109 241.47 224.72 R2 0.24 0.19 Adj. R2 0.21 0.16 Exp./Imp. FE YES YES YES YES YES YES All estimations are based on model (4) of Table4, i.e. the PPML estimation of single preference distances with legal quality as control variable and non-differentiated goods flow as dependent variable. These results are also displayed in column (2) of this table. Standard errors are clustered to Importer and Exporter fixed effects, identical to that model The alternative estimators follow Mayer etal. (2019), specifically their Table3, in controlling for potential confounding effects of both large and zero trade flows. Column (1) provides the OLS equivalent of the PPML main equation, with zero flows excluded. Column (3) provides the same estimation, but via PPML. In column (4), the model is estimated with weighted least squares; level trade flows are used as weights. Columns (5) and (6) estimate the main PPML model with trade shares, i.e. bilateral imports divided by total imports of that importer, as dependent variable ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 287 1 3 Economic preferences andtrade outcomes Fig. 2 Preference distance and trade volumes. Notes: This figure displays the relationship between the preference distances and the intensity of trade between a given country pair. The latter is measured as the bilateral trade between that pair, divided by their joint GDP 288 A.Korff, N.Steffen 1 3 Table 13 Robustness Estimations of Exporter Fixed Effects - Differentiated Goods Baseline Single Pref. Single Pref. Rights Single Pref. Legal (Intercept) 22.97∗∗∗ 19.30∗∗∗ 19.17∗∗∗ 18.24∗∗∗ (4.49) (4.66) (5.07) (4.74) avg.char 0.64 −0.35 −0.58 −0.63 (1.02) (1.07) (1.05) (1.10) pop 0.05∗∗∗ 0.04∗∗ 0.04∗∗∗ 0.04∗∗ (0.01) (0.01) (0.01) (0.01) gdpcap 0.78∗∗∗ 0.43∗ 0.43† 0.28 (0.12) (0.21) (0.22) (0.25) landlocked −1.63∗ −1.41∗ −1.44∗ −1.35∗ (0.65) (0.67) (0.67) (0.67) patience 1.86† 0.63 1.51 (1.06) (1.15) (1.10) risktaking −2.25∗ −1.49 −2.06∗ (0.90) (0.94) (0.91) posrecip 0.97 0.91 0.91 (1.08) (1.06) (1.08) negrecip 0.64 1.41 0.73 (0.89) (0.91) (0.89) altruism −0.84 −0.90 −0.70 (1.02) (1.01) (1.02) trust 0.46 0.89 0.47 (0.91) (0.94) (0.91) ‘PR Rating‘ 0.88† (0.46) ‘CL Rating‘ −1.24∗ (0.48) Free 0.13 (1.90) PartFree 0.43 (1.14) leg.qlt (level) 0.48 (0.42) R2 0.50 0.57 0.62 0.58 Adj. R2 0.47 0.50 0.53 0.50 Num. obs. 72 72 72 72 ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 Notes: The Fixed Effects represent Average Trade Barriers and are estimated via a two-step approach for differentiated-goods only. Exporter fixed effects are extracted from Table4 specification (2) and estimated via OLS using unilateral size and location variables, the average bilateral characteristics relating to the country in question and the single preference variables including altruism and trust. Column shows a regression on conventional country characteristics. (2) adds the single preferences in level, (3) and (4) add different institutional and legal quality controls. The results imply a relationship between risktaking and patience on one side and legal regimes on the other. However, these regressions must be treated with caution due to the high number of coefficients 289 1 3 Economic preferences andtrade outcomes Table 14 Robustness Estimations of Exporter Fixed Effects - Non-Differentiated Goods Baseline Single Pref. Single Pref. Rights Single Pref. Legal (Intercept) 19.34∗∗∗ 20.55∗∗∗ 21.65∗∗∗ 21.30∗∗∗ (3.86) (4.05) (4.41) (4.13) avg.char −0.63 −0.39 −0.51 −0.27 (0.56) (0.59) (0.61) (0.61) pop 0.03∗∗ 0.03∗∗∗ 0.03∗∗∗ 0.03∗∗∗ (0.01) (0.01) (0.01) (0.01) gdpcap 0.53∗∗∗ 0.75∗∗∗ 0.71∗∗∗ 0.85∗∗∗ (0.09) (0.16) (0.18) (0.19) landlocked −1.18∗ −1.31∗ −1.36∗ −1.35∗ (0.49) (0.51) (0.53) (0.51) patience −1.60∗ −1.41 −1.38 (0.80) (0.90) (0.83) risktaking 1.96∗∗ 1.95∗ 1.83∗ (0.68) (0.74) (0.69) posrecip 0.26 0.21 0.28 (0.82) (0.83) (0.82) negrecip −0.20 −0.36 −0.26 (0.68) (0.72) (0.68) altruism −0.50 −0.43 −0.59 (0.77) (0.80) (0.77) trust 0.84 0.58 0.84 (0.69) (0.74) (0.69) ‘PR Rating‘ −0.36 (0.36) ‘CL Rating‘ 0.13 (0.38) Free −1.50 (1.49) PartFree −1.30 (0.89) leg.qlt (level) −0.30 (0.32) R2 0.43 0.52 0.54 0.53 Adj. R2 0.39 0.44 0.43 0.44 Num. obs. 72 72 72 72 ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 Notes: The Fixed Effects represent Average Trade Barriers and are estimated via a two-step approach for non-differentiated-goods only. Exporter fixed effects are extracted from Table4 specification (2) and estimated via OLS using unilateral size and location variables, the average bilateral characteristics relating to the country in question and the single preference variables including altruism and trust. Column (1) shows a regression on conventional country characteristics. (2) adds the single preferences in level, (3) and (4) add different institutional and legal quality controls. The results imply a relationship between risktaking and patience on one side and legal regimes on the other. However, these regressions must be treated with caution due to the high number of coefficients 290 A.Korff, N.Steffen 1 3 Table 15 Estimation fixed effects composition for breadth of trade Differentiated goods Non-differentiated goods Exporter Importer Exporter Importer (1) (2) (3) (4) (Intercept) 4.76∗ −1.11 4.18† −1.07 (1.91) (0.84) (2.15) (1.09) avg.char −0.25 −0.49 −0.30 −0.29 (0.99) (0.44) (0.74) (0.37) spop 0.01∗∗ 0.00∗∗ 0.02∗∗∗ 0.01∗∗∗ (0.00) (0.00) (0.00) (0.00) sgdpcap 0.18∗ 0.08∗∗ 0.24∗∗ 0.12∗∗ (0.07) (0.03) (0.08) (0.04) landlocked −0.56∗ −0.09 −0.48† −0.20 (0.21) (0.09) (0.25) (0.13) patience 0.28 0.04 0.25 0.04 (0.35) (0.15) (0.41) (0.21) risktaking −0.51† 0.05 −0.26 0.04 (0.28) (0.12) (0.33) (0.17) posrecip 0.14 −0.09 0.08 −0.01 (0.22) (0.10) (0.26) (0.13) negrecip 0.36 0.05 0.23 0.19 (0.29) (0.13) (0.34) (0.17) R2 0.52 0.37 0.52 0.46 Adj. R2 0.45 0.27 0.44 0.37 Num. obs. 72 72 72 72 The Fixed Effects represent Average Trade Barriers and are estimated via a two-step approach. Exporter and importer fixed effects are extracted from Table6 specifications (2) and (4)—for differentiated and non-differentiated goods—and estimated via OLS using unilateral size and location variables, the average bilateral characteristics relating to the country in question and the single preference variables. Columns (1) and (2) show country characteristics for differentiated goods and (3) and (4) for non-differentiated goods. Exporter results are displayed first in each case ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 291 1 3 Economic preferences andtrade outcomes Table 16 OECD subset: preference distribution Statistic N Mean St. Dev. Min Max patience 25 0.317 0.416 −0.431 1.071 risktaking 25 −0.078 0.232 −0.792 0.244 posrecip 25 −0.073 0.284 −1.038 0.316 negrecip 25 0.101 0.277 −0.375 0.665 altruism 25 −0.148 0.341 −0.940 0.406 trust 25 0.021 0.260 −0.519 0.532 The single preferences are normalized to the individual level for the whole GPS sample, while the averages are calculated using only those GPS countries which are also in the OECD. For this reason, the means deviate from zero despite the normalization Table 17 OECD subset: sumary statistics for distances in preferences Statistic N Mean St. Dev. Min Max dpati 600 0.485 0.332 0.0001 1.502 drisk 600 0.249 0.214 0.001 1.036 dposrec 600 0.296 0.272 0.004 1.354 dnegrec 600 0.321 0.224 0.001 1.040 daltr 600 0.386 0.290 0.002 1.346 dtrus 600 0.297 0.217 0.001 1.051 dpref 600 0.339 0.130 0.061 0.712 292 A.Korff, N.Steffen 1 3 Table 18 OECD subset: standard gravity Basic Grav. Agg. Pref. Dist. Agg. Pref. Dist Single Pref. Dist. Single Pref. Dist. (1) (2) (3) (4) (5) ldist −0.52∗∗∗ −0.55∗∗∗ −0.56∗∗∗ −0.60∗∗∗ −0.60∗∗∗ (0.08) (0.08) (0.08) (0.06) (0.06) contig 0.69∗∗∗ 0.64∗∗∗ 0.63∗∗∗ 0.66∗∗∗ 0.66∗∗∗ (0.15) (0.14) (0.14) (0.12) (0.13) colony 0.27∗ 0.23† 0.19 0.28∗∗ 0.24∗ (0.13) (0.12) (0.12) (0.11) (0.11) rta 0.56∗∗∗ 0.52∗∗∗ 0.50∗∗∗ 0.49∗∗∗ 0.47∗∗∗ (0.13) (0.12) (0.12) (0.11) (0.11) lng 0.07 0.23 −0.03 0.09 −0.08 (0.19) (0.17) (0.18) (0.17) (0.18) dpref 0.94 1.37∗ (0.58) (0.56) comleg 0.31∗∗∗ 0.21∗ (0.08) (0.09) leg.qlt −0.05 −0.11 (0.13) (0.12) dpati 0.39∗ 0.57∗∗∗ (0.17) (0.15) drisk −0.26 −0.32 (0.59) (0.62) dposrec 1.24∗∗∗ 1.25∗∗∗ (0.30) (0.32) dnegrec −0.66∗∗∗ −0.52∗∗∗ (0.10) (0.11) Observations 600 600 600 600 600 Deviance 1067 ×109 1043 ×109 1006 ×109 9215 ×109 9031 ×109 Null Deviance 137327 ×109 13732 ×109 13732 ×109 13732 ×109 13732 ×109 Exp./Imp. FE YES YES YES YES YES The estimation of aggregated bilateral exports Xij of all members of the OECD included in the GPS dataset is conducted via PPML. The variables of interest are the distances in preferences, included as an unweighted average dpref in (2,3) and as single variables dpati, drisk, dposrec, dnegrec (4,5). Commonalities in legal systems are included in models (3) and (5) due to their potential impact on negotiations, the channel of interest. Model (1) is a standard gravity equation for comparison. Standard errors are clustered to Importer and Exporter fixed effects. It can be seen that the negative coefficient and significance for dnegrec remains. However, the weak effect of distances in risk does not carry over to the OECD set, while distance in positive reciprocity has a significant positive impact on volumes here, supporting the hypothesis of a beneficial effect from corresponding gestures—e.g. gifts, perceptions of fairness ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 299 1 3 Economic preferences andtrade outcomes Table 25 Hofstede & GPS Second Stage: Exporter Differentiated Goods Non-Differentiated Goods (1) (2) (3) (4) (Intercept) 20.80∗∗∗ 16.46∗∗ 21.09∗∗∗ 12.40∗∗ (4.53) (4.74) (3.75) (4.08) avg.char −0.03 −1.03 −0.31 −1.59∗∗ (1.00) (0.99) (0.54) (0.56) spop 0.04∗∗ 0.03∗ 0.03∗∗∗ 0.02∗∗ (0.01) (0.01) (0.01) (0.01) sgdpcap 0.46∗ 0.32∗∗ 0.79∗∗∗ 0.41∗∗∗ (0.20) (0.12) (0.15) (0.09) landlocked −1.46∗ −0.11 −1.26∗∗ −0.65 (0.62) (0.78) (0.47) (0.62) patience 1.93† −1.68∗ (1.04) (0.78) risktaking −2.21∗∗ 1.90∗∗ (0.81) (0.61) uai 0.00 0.02 (0.01) (0.01) ltowvs 0.02∗ −0.00 (0.01) (0.01) R2 0.55 0.44 0.50 0.50 Adj. R2 0.51 0.35 0.46 0.42 Num. obs. 72 44 72 44 Second Stage: Importer Differentiated Goods Non-Differentiated Goods (5) (6) (7) (8) (Intercept) 21.09∗∗∗ −7.32∗ −1.97 −9.68∗∗ (3.75) (3.43) (3.05) (3.29) avg.char −0.31 −1.41† −0.12 −1.20∗ (0.54) (0.72) (0.44) (0.45) spop 0.03∗∗∗ 0.02∗∗ 0.04∗∗∗ 0.03∗∗∗ (0.01) (0.01) (0.01) (0.01) sgdpcap 0.79∗∗∗ 0.35∗∗∗ 0.57∗∗∗ 0.33∗∗∗ (0.15) (0.08) (0.12) (0.07) landlocked −1.26∗∗ −0.37 −1.00∗ −0.50 (0.47) (0.56) (0.38) (0.50) patience −1.68∗ −0.35 (0.78) (0.64) risktaking 1.90∗∗ 0.19 (0.61) (0.50) 300 A.Korff, N.Steffen 1 3 Second Stage: Importer Differentiated Goods Non-Differentiated Goods (5) (6) (7) (8) uai 0.01 0.01 (0.01) (0.01) ltowvs 0.00 0.01 (0.01) (0.01) Adj. R2 0.46 0.38 0.56 0.55 Num. obs. 72 44 72 44 Exporter (1–4) and importer (5–8) fixed effects are extracted from a basic gravity regression on differentiated and non-differentiated goods, respectively; the basic equation is equivalent to specification (1) of Sect.5.1 Standard Gravity. The fixed effects are estimated via OLS following the design of Sect.5.3 Impact on Average Barriers, but restricted to preference measures for patience and risk. Uneven specifications show the results for GPS data, while even ones use Hofstede dimensions instead ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 Table 25 (continuted) 301 1 3 Economic preferences andtrade outcomes Table 26 Genetics, religion & GPS Single Pref. Dist. Gen. Dist. Rel. Dist. (1) (2) (3) (4) (5) ldist −0.59∗∗∗ −0.69∗∗∗ −0.68∗∗∗ −0.59∗∗∗ −0.60∗∗∗ (0.06) (0.09) (0.08) (0.06) (0.06) contig 0.48∗∗∗ 0.38∗∗ 0.37∗∗ 0.48∗∗∗ 0.49∗∗∗ (0.14) (0.13) (0.13) (0.13) (0.14) colony 0.33∗∗ 0.40∗∗∗ 0.39∗∗∗ 0.33∗∗ 0.34∗∗∗ (0.10) (0.10) (0.10) (0.10) (0.10) rta 0.35∗∗∗ 0.34∗∗∗ 0.33∗∗∗ 0.35∗∗∗ 0.35∗∗∗ (0.09) (0.09) (0.09) (0.09) (0.09) lng −0.06 −0.06 −0.04 −0.06 −0.06 (0.13) (0.13) (0.13) (0.13) (0.12) comleg 0.15∗ 0.15∗ 0.15∗ 0.16∗ 0.16∗ (0.07) (0.07) (0.07) (0.07) (0.07) leg.qlt 0.15∗∗∗ 0.14∗∗∗ 0.14∗∗∗ 0.15∗∗∗ 0.15∗∗∗ (0.03) (0.03) (0.03) (0.03) (0.03) dpati −0.16 −0.13 −0.14 −0.16 −0.16 (0.10) (0.10) (0.10) (0.10) (0.11) drisk 0.52† 0.48† 0.47† 0.52† 0.51† (0.28) (0.27) (0.27) (0.28) (0.27) dposrec −0.04 0.03 0.01 −0.03 −0.02 (0.18) (0.17) (0.17) (0.17) (0.17) dnegrec −0.53∗∗ −0.53∗∗ −0.52∗∗∗ −0.54∗∗ −0.55∗∗ (0.16) (0.16) (0.16) (0.17) (0.18) new_gendist_weighted 7.92 (5.08) new_gendist_plurality 6.95† (4.04) reldist_dominant_formula 0.04 (0.10) reldist_weighted_formula 0.26 (0.29) Observations 5112.00 4970.00 4970.00 4970.00 4970.00 Deviance 4562 ×109 4495 ×109 4489 ×109 4548 ×109 4542 ×109 Null Deviance 52347 ×109 51781 ×109 51781 ×109 51731 ×109 51731 ×109 Exp./Imp. FE YES YES YES YES YES The aggregated bilateral exports are estimated via PPML. Models (2) and (3) include genetical distances between populations in two different calculations, whereas specifications (4) and (5) include two version of religious distance. Both distances are taken from Spolaore and Wacziarg (2018) and compared to the GPS’ preference distances ∗∗∗ p < 0.001 , ∗∗ p < 0.01 , ∗p < 0.05 , †p < 0.1 302 A.Korff, N.Steffen 1 3 Funding Open Access funding enabled and organized by Projekt DEAL. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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