National culture and the choice of exchange rate regime

Journal Pre-proofs National Culture and the Choice of Exchange Rate Regime Zhongyu Cao, Sadok El Ghoul, Omrane Guedhami, Chuck Kwok PII: DOI: Reference:

S0261-5606(18)30698-3 https://doi.org/10.1016/j.jimonfin.2019.102091 JIMF 102091

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Journal of International Money and Finance

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1 December 2018 2 October 2019 7 October 2019

Please cite this article as: Z. Cao, S. El Ghoul, O. Guedhami, C. Kwok, National Culture and the Choice of Exchange Rate Regime, Journal of International Money and Finance (2019), doi: https://doi.org/10.1016/ j.jimonfin.2019.102091

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National Culture and the Choice of Exchange Rate Regime Zhongyu Caoa, Sadok El Ghoulb, Omrane Guedhamic*, Chuck Kwokd

aUniversity

bCampus

of South Carolina, 1014 Greene St, Columbia, SC [email protected]

Saint-Jean, University of Alberta, Edmonton, AB T6C 4G9, Canada [email protected]

cUniversity

of South Carolina, 1014 Greene St, Columbia, SC [email protected]

dUniversity

of South Carolina, 1014 Greene St, Columbia, SC [email protected]

* Corresponding author. We gratefully acknowledge helpful comments from an anonymous reviewer, Najah Attig, Narjess Boubakri, Kees Koedijk (editor), Liang Shao, Helen Wang, Xiaolan Zheng, and participants at the 2018 Academy of International Business Annual Meeting. We appreciate the generous financial support from Canada’s Social Sciences and Humanities Research Council.

National Culture and the Choice of Exchange Rate Regime Abstract Based on the social analysis framework of Williamson, we argue that national culture – especially the individualism/collectivism dimension – located in the social embeddedness level can guide behaviors and decisions in a country, including the choice of exchange rate regime. We argue that individualistic societies are more likely to choose floating regimes because their economic agents are independent, overconfident, and have higher levels of risk tolerance. Individualistic societies are also associated with better financial development, fewer capital controls, and more democratic institutions, which are all tied to a higher probability of choosing a floating exchange rate regime. We use data on 78 countries over the 1976-2014 period, and we control for common determinants of exchange rate regimes. We find that individualistic countries have a significantly higher probability of implementing a floating regime than a fixed regime. Our evidence is robust to using an instrumental variables approach, an alternative estimation technique, an alternative regime classification, alternative proxies for culture, a subsample analysis, and additional controls. We also find that other cultural dimensions (uncertainty avoidance, power distance, and masculinity) can influence the choice of exchange rate regime, but their effect is weaker than that of individualism.

Keywords: National culture; individualism/collectivism; exchange rate regime JEL codes: F30; F31

1.

Introduction Since the collapse of the Bretton Woods fixed exchange rate system in the 1970s, countries

have chosen their own exchange rate regimes.1 Understanding the determinants of exchange rate regime choice is important, because it has implications for a country’s price stability, monetary policy, and international trade (Frieden, Leblang, and Valev, 2010). Prior research has shown that the choice of exchange rate regime is driven by country-level factors such as political environment, level of institutional development, and macroeconomic conditions (Chang and Lee, 2017; Rose, 2011). However, scholars “have collectively made little progress in understanding how countries choose their exchange rate regimes” (Rose, 2011, p. 671). Relying on traditional economics and political theory, we cannot fully explain why countries of similar size, openness, and formal institutions would choose different regimes (Rose, 2011). In this paper, we take a first step toward filling this gap in the literature by examining the incremental effect of national culture on the choice of exchange rate regime, both theoretically and empirically. According to Hofstede’s (2001) seminal work on cultural dimensions, culture is “the collective programming of the mind that distinguishes people of one country, region or group from people from other countries, regions or groups” (p. 9). It is a persistent national characteristic that guides decisions and behaviors in a country, as well as its institutional development. In Williamson’s (2000) four levels of social analysis framework, culture is considered an informal institution that imposes constraints on formal institutions, corporate

Although the freedom of countries to choose their exchange rate regime was recognized later in the second amendment to IMF’s articles in 1978 (Burton and Gilman, 1991), we recognize that some countries could have been forced into either a freely floating or fixed rate regime due to their economic circumstances. For example, Argentina, Brazil, India, Indonesia, South Africa, South Korea, and Turkey, which retained fixed exchange rate regimes after the collapse of the Bretton Woods system, switched to either managed floating or freely floating regime from 1978 to 1980. Our main evidence continues to hold when we restrict our sample to the 1980-2014 period. 1

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governance, and economic activities. The economic relevance of culture, as illustrated by Williamson’s (2000) framework, is supported by prior empirical research. It has been shown to explain cross-country variations in long-term economic growth rates and wealth (Gorodnichenko and Roland, 2011, 2016); the development of financial (Kwok and Tadesse, 2006), legal (Alesina and Giuliano, 2015), and democratic (Gorodnichenko and Roland, 2015; Licht, Goldschmidt, and Schwartz, 2007) institutions; capital and trade restrictions (De Jong, Smeets, and Smits, 2006); and individual (Chui and Kwok, 2008; Chui, Titman, and Wei, 2010) and corporate (Shao, Kwok, and Zhang, 2013) risk preferences. Motivated by this literature, we argue that culture affects the choice of exchange rate regime directly and indirectly through formal institutions. To capture a country’s culture, we employ Hofstede’s (1983) framework, which defines four dimensions: individualism/collectivism, uncertainty avoidance, power distance, and masculinity/femininity. Individualism/collectivism is generally the most widely used dimension, and it has been shown to have a first-order effect on several economic outcomes (Aggarwal et al., 2016; Triandis, 2001).2 Individualism emphasizes independence and the pursuit of self-interest, and is associated with the overconfidence bias (Chui, Titman, and Wei, 2010; Gelfand et al., 2002; Markus and Kitayama, 1991); in contrast, collectivism emphasizes group interdependence and harmony. In the context of exchange rate regime choice, we posit that the individualism/collectivism dimension affects a country’s regime choice both directly and indirectly. For example, individualistic countries are more likely to adopt a floating regime, for three main reasons. First, economic agents in those countries are more independent, and, thus, prefer to manage exchange rate risk themselves rather than relying on the government. Second, they tend to exhibit the

For example, Gorodnichenko and Roland (2011) find that, among the culture dimensions, individualism/collectivism can significantly affect economic growth. 2

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overconfidence bias, which suggests they believe they can handle exchange rate risk better than the government. Third, they tend to have a higher level of risk tolerance and to seek opportunistic trading. This would reduce the benefit of alleviating exchange rate risk under a fixed regime. In addition to the direct effects above, individualism may affect the choice of exchange rate regime indirectly through its impact on the institutional and economic environment, which has previously been shown to affect the choice of exchange rate regime. Individualistic countries are associated with better financial development, weaker capital controls, and more democratic institutions, which all lead to a higher probability of adopting a floating regime. Thus, we posit that individualistic countries will be more inclined to adopt floating regimes. Turning to Hofstede’s (1983) other cultural dimensions, uncertainty avoidance indicates how people in a country respond on average to uncertainty, power distance captures the degree to which people expect and accept an unequal distribution of power and status in a country, and masculinity reflects the role of different gender attributes in a country. We expect that, after controlling for other determinants, high uncertainty avoidance and power distance countries are more likely to adopt a fixed regime, while the effect of high masculinity is uncertain. To capture a country’s exchange rate regime choice, we employ the de facto classifications of Ilzetzki, Reinhart, and Rogoff (2017) and Shambaugh (2004). We focus on de facto rather than de jure classifications because countries do not always use the stated regime (Rose, 2011). Our final sample covers 78 countries over the 1976-2014 period. Following prior literature (e.g., Berdiev, Kim, and Chang, 2012; Chang and Lee, 2017), we test for the effect of culture on a country’s choice of exchange rate regime using a multinomial logit model. After controlling for factors previously shown to affect regime choice, we find that high individualism countries are more likely to employ a floating regime, consistent with culture 3

having a direct effect on the choice of exchange rate regime. The key control variables we adopt are: 1) variables based on optimal currency area (OCA) theory: trade openness, the size of the economy, trade concentration, nominal shocks, and real shocks; 2) controls for the state of the economy: inflation, international reserves, financial development, and capital controls; and 3) proxies for various political institutions: democracy and political instability. Across different specifications, the effect from individualism is consistently significant. In contrast, the effects of factors related to (1), (2), and (3) are less consistent. We also find that a 1-standard deviation increase in individualism score has a 13.7% lower probability of implementing a fixed regime, and a 2.7% higher probability of adopting a free floating regime. Our main evidence is robust to using the instrumental variables approach, an alternative estimation model, and an alternative exchange rate regime classification. Moreover, our results continue to hold when we use updated individualism scores (Tang and Koveos, 2008) and the GLOBE in-group collectivism score (House et al., 2004) as alternative proxies for individualism/collectivism. In subsample analyses, we find that the effect of individualism on exchange rate regime choice is consistently found in non-Eurozone countries, in both developed and developing countries, and across different sample periods. The documented effect also remains after controlling for religion, government budget balance, current account balance, and central bank independence. Overall, the empirical evidence supports the view that individualistic countries are more likely to adopt a floating regime. Other dimensions of national culture also play an important role in determining exchange rate regime choice. We find that high uncertainty avoidance countries are significantly less likely to adopt a floating regime, high power distance countries tend to choose a fixed regime (although this relation is weak), and high masculinity countries are associated with a greater likelihood of adopting a free floating regime. In our last set of tests, we use the World Values 4

Survey (WVS) to identify channels through which individualism affects the choice of exchange rate regime, namely, preference for a strong leader and preference for independence. Our research has important practical implications. First, setting the optimal currency regime could be viewed as a social welfare optimization process, in which policymakers consider both economic fundamentals as well as the cultural preferences of their people. Second, cross-border economic agents should adapt their business strategies in countries with different cultural orientations, and, hence, different currency regimes. Third, when multinational corporations make foreign investment decisions, they should consider such adaptation costs in order to avoid substantial economic losses. We develop these implications further in the conclusion. The remainder of this article is organized as follows. Section 2 reviews prior literature, and develops our hypotheses regarding the effect of national culture on the choice of exchange rate regime. Section 3 describes our data, and sections 4 and 5 report our main empirical results and the results of robustness tests, respectively. Section 6 presents additional analysis on the channels through which culture influences the choice of exchange rate regime. Section 7 concludes. 2. Background, Literature Review, and Hypotheses 2.1. Background on Fixed, Floating, and Intermediate Exchange Rate Regimes Following World War II, exchange rate regimes worldwide were governed by the Bretton Woods system, under which International Monetary Fund (IMF) member states fixed, or “pegged,” their exchange rate against the U.S. dollar—which was tied to gold—to facilitate international trade and fund post-war reconstruction. This system operated smoothly through the 1950s, during which time countries generally experienced steady growth in production and trade. During the 1960s, however, the system suffered from the gold overhang and from lax U.S. macroeconomic policies. In 1971, facing pressure from an overvalued dollar, the Nixon 5

administration suspended the dollar’s convertibility into gold, which effectively ended the Bretton Woods system. Thus, since the early 1970s, countries have had to choose whether to adopt a floating, fixed (pegged to a single currency or a basket of currencies), or intermediate rate regime. In this paper, we focus on the post-Bretton Woods era. Under a fixed regime, the monetary authority promises to buy and sell local currency in unlimited amounts at announced rates against a foreign currency or currency basket; under a free floating regime, the value of the local currency in terms of foreign currencies can fluctuate without central bank intervention. Comparing these two opposing regimes, the primary advantage of a fixed regime is low exchange rate volatility (Rose, 2011). This leads to stable prices and in turn a stable trading environment, which facilitates international trade and investment (Berdiev et al., 2012; Edwards, 1999). In contrast, a floating regime can lead to increased price uncertainty and a higher risk premium, which may dampen trade and investment (Markiewicz, 2006; Rose, 2011). However, there are two main disadvantages to a fixed regime. First, a country operating under a fixed regime cannot use monetary policy to stabilize the economy when it faces pressure from an internationally integrated capital market.3 To illustrate, consider an open economy that experiences a decrease in demand for its exports—domestic employment and output will face downward pressure because nominal prices are sticky in the short run. If this country adopts a floating regime, the central bank can stimulate short-run demand by increasing the money supply, because that would reduce the value of the local currency and bid the interest rate down. In contrast, if this country adopts a fixed regime, the central bank cannot counter negative demand shocks by increasing the money supply because, rather than hold a devaluing local currency, people will sell their excess money to the central bank in exchange for foreign

The idea that, under capital mobility, a country cannot have both a stable exchange rate and independent monetary policy is captured by the theory of the “impossible trinity” (Mundell, 1963). 3

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currency. This will result in no change to the money supply. This example highlights the primary advantage of a floating regime, namely, the ability to use monetary policy to reduce unemployment and increase output (Chang and Lee, 2017; Markiewicz, 2006), and stimulate capital outflows and realize trade surpluses (Mundell, 1963). Second, a country operating under a fixed regime is subject to speculative attacks on its currency. To defend itself from such attacks, a country needs to hold substantial foreign reserves, because insufficient reserves can lead to a currency crisis (e.g., the Asian Financial Crisis in 1997). Defending against a speculative attack and enduring a currency crisis can lead to a decrease in output (Kraay, 2003; Obstfeld and Rogoff, 1995). As we note above, a country can also choose to follow an intermediate exchange rate regime. One such option is a “crawling peg,” whereby the government announces a schedule of small, discrete devaluations or appreciations. This allows the exchange rate to gradually respond to changes in market forces, reducing the risk of a large devaluation or appreciation caused by the inflation differential between the home country and the pegged country. However, a crawling peg is subject to similar problems as a fixed regime (Obstfeld and Rogoff, 1995). Another intermediate option is a “managed float,” whereby the exchange rate fluctuates freely within a specified band, but the central bank can intervene if needed. A country that implements a managed float retains some ability to conduct monetary policy while limiting short-term exchange rate volatility. However, a managed float also faces similar problems as a fixed regime if the exchange rate reaches the upper or lower edges of its band (Obstfeld and Rogoff, 1995). Ultimately, all exchange rate regimes—fixed, floating, and intermediate—have pros and cons. The optimal choice is thus a function of a country’s particular circumstances. 2.2. Prior Literature on the Determinants of Exchange Rate Regime Choice

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A large body of extant work has explored the determinants of a country’s choice of exchange rate regime. For simplification, we categorize the factors previously considered into three groups: optimal currency area (OCA) fundamentals, other economic variables, and political variables. The first group—OCA fundamentals—includes trade openness, the size of the economy, trade concentration, and the prevalence of nominal versus real shocks. It is based on the OCA theory pioneered by Mundell (1961). This theory states that countries that depend more on international trade may prefer a fixed regime because they are more exposed to exchange rate risk that could potentially reduce trade and investment (McKinnon, 1963). Empirical findings on the degree of trade openness and the size of the economy are in line with this view (Aliyev, 2015; Berdiev et al., 2012; Markiewicz, 2006; Rose, 2011; Yeyati, Sturzenegger, and Reggio, 2010). For example, smaller economies tend to peg their currencies because they have high rates of participation in international trade; larger economies tend to employ a floating regime because they rely less on international trade, and hence place greater value on the benefits associated with monetary policy independence. The theory also suggests that countries with higher trade concentration are more likely to peg because trade concentration increases the importance of currency stability with major trading partners (Frieden et al., 2010). However, empirical findings on trade concentration are mixed. With respect to the shocks that a country is exposed to, the Mundell-Fleming framework argues that, to minimize fluctuations in output, a country should adopt a fixed (flexible) regime if nominal (real) shocks are the main source of disturbance in the economy. A country that is subject to greater real shocks, such as a decrease in international demand for the goods it produces, should adopt a floating regime. Thus, a change in the relative prices of domestic goods can be offset by a change in the exchange rate in order to maintain stable output and unemployment (Moosa, 2006). In contrast, a country that is subject to more nominal (i.e., 8

financial) shocks, such as excess domestic money supply, should adopt a fixed regime. Thus, the central bank can restore money market balance through international reserve losses, thereby keeping the nominal shock from expanding to the real economy (Moosa, 2006). Some empirical evidence supports this idea (e.g., Yeyati et al., 2010), but other evidence indicates that the explanatory power of real and nominal shocks is weak (e.g., Rose, 2011). The second group of factors includes other economic variables not originally in the OCA list, such as, e.g., inflation, international reserves, financial development, and capital controls. First, the theory predicts that countries concerned about inflation should adopt a floating regime to be able to conduct monetary policy. But prior work shows that countries lacking credible monetary institutions achieve this aim by pegging their exchange rates against a stable currency with a low inflation rate (Frieden et al., 2010; Markiewicz, 2006; Von Hagen and Zhou, 2005). We note that, in transition economies, where the central bank has achieved credibility, a floating regime is often adopted so that monetary policy can be used to support the competitiveness of the country’s tradable sectors. This may perhaps be considered a reconciliation between the theory and prior evidence. Next, previous studies (Chang and Lee, 2017; Von Hagen and Zhou, 2005) have shown that countries that hold higher levels of international reserves tend to be associated with a fixed regime. However, this relation may be subject to reverse causality problems, as countries that adopt a fixed regime need to hold sufficient foreign currency reserves to defend against speculative attacks. Previous studies have also shown that countries with underdeveloped financial markets tend to adopt a fixed regime (Aliyev, 2015; Berdiev et al., 2012; Lin and Ye, 2011). These countries do not have sufficient tools to manage open market operations, and thus may prefer exchange rate stability over monetary policy independence (Markiewicz, 2006). A fixed regime can also protect an inexperienced banking sector from currency volatility (Von Hagen and Zhou, 2005). 9

Turning to capital controls, according to Mundell’s (1963) “impossible trinity” theory, governments can maintain both a fixed regime and domestic monetary policy autonomy by adopting capital controls (Berdiev et al., 2012; Bernhard and Leblang, 1999). Some empirical studies confirm this view (Aliyev, 2015; Bernhard and Leblang, 1999), but the effect may be weak (Von Hagen and Zhou, 2007). The third group of factors captures whether a country is a democracy, as well as its degree of political instability. Broz (2002) argues that democracies implement a floating regime to signal a commitment to transparency and political accountability, while Aliyev (2015), Berdiev et al. (2012), and Chang and Lee (2017) argue that democracies float to maintain monetary policy independence. Policymakers may also employ a floating regime to boost output growth, and, in turn, their chances of reelection. Authoritarian countries, in contrast, typically control their exchange rate (Frieden, Ghezzi, and Stein, 2000), and transition economies tend to peg (Frieden et al., 2010). Frieden et al. (2010) argue that, in transition economies, calls for expansionary monetary policy lead policymakers who favor low inflation to peg (which makes monetary policy ineffective) in order to credibly demonstrate that “their hands are tied.” Finally, the political instability determinant has been widely studied, but its effect is unclear. On the one hand, a government with little institutional credibility may combat inflationary pressures using a fixed regime as a second-best solution to a commitment problem (Yeyati et al., 2010). Similarly, unstable governments that are unable to agree on or conduct effective monetary policy are more likely to adopt a fixed regime (Bernhard and Leblang, 1999). On the other hand, a weak or unstable government may have limited ability to sustain a fixed regime, in which case, political instability may increase the likelihood of a floating regime (Edwards, 1996; Von Hagen and Zhou, 2007).

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2.3. The Effect of National Culture on Exchange Rate Regime Choice4 The Economic Foundations of the Role of Culture. Culture, “the collective programming of the mind” (Hofstede and Bond, 1988), “provides a language-based conceptual framework for encoding and interpreting the information that the senses are presenting to the brain” (North, 1990, p. 37). It thus shapes individual perceptions of the external world, and influences every aspect of a society, from individual decisions and behaviors to a society’s formal and informal institutions (Zheng et al., 2012). To illustrate the fundamental role of culture in shaping economic activities and outcomes, we rely on Williamson’s (2000) framework of social analysis. This framework consists of four levels, in which each level imposes constraints on the next. Level 1 (the top) is the social embeddedness level, where informal institutions such as norms, customs, mores, and traditions are located. Culture is embedded in this level. Level 2, constrained by Level 1, is referred to as the institutional environment level. Here, formal rules, such as the “executive, legislative, judicial, and bureaucratic functions of the government” prevail. A country’s formal rules are systematically related to its primary cultural orientation, so policies should partially reflect the culture in a society (Licht, Goldschmidt, and Schwartz, 2005). The exchange rate regime belongs to this level. Level 3 includes governance institutions (e.g., the “play of the game”), especially contracts. Lastly, Level 4 represents the allocation and employment of resources, where economic agents optimize and adjust prices and quantities, incentive alignments, etc. Culture (Level 1) imposes constraints on formal institutions (Level 2), contract enforcement (Level 3), and individuals’ and firms’ economic decisions (Level 4). In line with this framework, prior research supports the fundamental role of culture in influencing economic outcomes at different levels. For Level 2, culture is shown to affect a country’s economic

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We thank an anonymous referee for suggesting to develop this section.

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growth (Gorodnichenko and Roland, 2011), financial systems (Kwok and Tadesse, 2006), and legal and democratic institutions (Alesina and Giuliano, 2015; Gorodnichenko and Roland, 2015; Licht et al., 2007). For Level 3, culture is related to firm-level governance practices (Griffin et al., 2017), such as board structure (Li and Harrison, 2008) and disclosure policies (Hope, 2003). For Level 4, culture affects individual (Chui and Kwok, 2008; Chui et al., 2010) and corporate (Li et al., 2013) risk-taking, corporate hedging decisions (Lievenbrück and Schmid, 2014), corporate investments (Shao et al., 2013), corporate financing decisions (Zheng et al., 2012), corporate cash holdings (Chen et al., 2015), and individual saving decisions (Guiso, Sapienza, and Zingales, 2006). Using Hofstede’s (1983) four dimensions of national culture, we extend these studies by examining the role of national culture (Level 1) in influencing the choice of exchange rate regime (Level 2). Individualism/collectivism. Of Hofstede’s (1983) four dimensions of national culture, individualism/collectivism has the greatest power to explain various economic outcomes (Aggarwal et al., 2016). We argue that individualistic societies are more likely to choose floating regimes because their economic agents are independent, overconfident, and risk takers (direct effects). Individualistic societies are also associated with better financial development, fewer capital controls, and more democratic institutions, which all make a floating regime preferable (indirect effects). Direct Effects. There are three primary reasons economic agents in individualistic societies tend to prefer floating regimes: 1) they value independence, and prefer to manage exchange rate risk themselves rather than relying on the government; 2) they believe they can handle exchange rate risk better than the government; and 3) they typically exhibit higher levels of risk tolerance, which would tend to reduce the benefits of a fixed regime. First, according to Hofstede (2001), an individualistic society emphasizes independence and the pursuit of individual achievements, while a collectivistic society emphasizes group 12

embeddedness and harmony. In a collectivistic country, people display loyalty to their group, and, in exchange, the group provides protection to its members (Hofstede, 2001). Moreover, people often defer decision-making to their group’s leaders (Hofstede, 2001; Shao et al., 2013). Therefore, we predict that, rather than managing exchange rate risk themselves, people in a collectivistic society will prefer the government to reduce exchange rate risk on their behalf by setting a fixed exchange rate (as a form of protection). In contrast, economic agents in an individualistic country are more likely to have a free-market mentality and a preference for making their own decisions in the marketplace, including managing foreign exchange risk. Second, people in an individualistic society tend to exhibit the overconfidence bias (Markus and Kitayama, 1991).5 Consistent with this attribute, they tend to believe their abilities are above average, and their predictions tend to be overly precise (Chui et al., 2010), and, hence, they can deal with the exchange rate risk better than the government. Consequently, they prefer a floating regime. Third, according to OCA theory, countries face a trade-off between the microeconomic benefits and the macroeconomic costs of a fixed regime (Edwards, 1999; Sorensen and WhittaJacobsen, 2004). One of the main microeconomic benefits of a fixed regime is a reduction in foreign exchange risk, which would promote international trade. We argue that the higher level of risk tolerance of economic agents in individualistic societies (Chui and Kwok, 2008; Li et al., 2013; Shao et al., 2013) attenuates the role of exchange rate risk in their international trade decisions. Hence, this reduces the benefits from adopting a fixed regime. In fact, economic agents with higher levels of risk tolerance may even want to take advantage of the currency speculation opportunities offered by a floating regime (Chui et al., 2010).

In fact, in the questionnaire used to construct the individualism index, we found that respondents in individualistic countries tended to agree more with the statement: “Decisions made by individuals are usually of higher quality than decisions made by groups” (Hofstede, 2001). 5

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Indirect Effects. Individualism/collectivism may also affect the choice of exchange rate regime indirectly through formal institutions (Level 2), as documented previously. First, prior studies show that collectivistic societies tend to have higher levels of banking corruption (Zheng et al., 2013) and weaker investor protection and enforcement (Licht et al., 2005, 2007). These attributes can lead to underdeveloped financial markets (La Porta et al., 1997, 2006). Countries with underdeveloped financial markets have a limited ability to conduct monetary policy to stabilize output. They will thus benefit less from adopting a floating exchange rate (Berdiev et al., 2012; Lin and Ye, 2011). Therefore, collectivistic societies may prefer a fixed regime. Second, according to the “impossible trinity,” governments can maintain both a fixed exchange rate and domestic monetary policy autonomy by adopting capital controls (Aliyev, 2015; Bernhard and Leblang, 1999), while simultaneously enjoying lower exchange rate risk and functional monetary policy. Given prior evidence that collectivistic societies are associated with stronger capital controls (De Jong et al., 2006), we predict they are more likely to choose a fixed regime. Third, as argued above, democratic governments tend to adopt a floating regime to signal transparency and political accountability (Aliyev, 2015; Broz, 2002). Similarly, Berdiev et al. (2012) and Chang and Lee (2017) argue that policymakers in democratic governments may prefer to employ a floating regime. This is because they need a functional monetary policy in order to boost output growth, and, in turn, their reelection chances. Authoritarian countries, in contrast, typically control their exchange rate (Frieden et al., 2000). Given that collectivistic countries are associated with less democratic institutions (Alesina and Giuliano, 2015; Gorodnichenko and Roland, 2015), we predict they are more likely to adopt a fixed regime. Uncertainty avoidance. The uncertainty avoidance dimension captures the extent to which members of a country feel threatened by uncertainty and ambiguity (Hofstede, 2001). A 14

primary reason to adopt a fixed regime is to reduce variations in the exchange rate. Because high uncertainty avoidance countries should be averse to daily fluctuations and the associated risk, we expect such countries to mitigate this uncertainty by adopting a fixed regime. Besides its direct effect, uncertainty avoidance may also affect the choice of exchange rate regime indirectly through other factors. Uncertainty avoidance leads to stronger capital controls (De Jong et al., 2006), and potentially to a preference for a fixed regime. However, a high uncertainty avoidance country should dislike the volatility in inflation and output. Thus, its economic agents may prefer a floating regime to maintain monetary policy independence and ensure stabilization of the economy. We conclude that the indirect effect of the uncertainty avoidance dimension is theoretically unclear. Power distance. Power distance refers to the degree to which people expect and accept that power and status are distributed unequally among a group’s members (Hofstede, 1983). This dimension captures differences in how countries deal with hierarchies and the corresponding inequality in their social lives. In high power distance countries, people with certain powers and privileges are expected to use this advantage to increase their wealth (Zheng et al., 2013); people without such advantages are expected to submit to their leaders, with the expectation that they will be taken care of. Accordingly, we expect that rather than determining the exchange rate themselves in the market, they will be more likely to let the government set the exchange rate on their behalf. On the other hand, a government with less concentrated power may implement a flexible regime to signal its transparency and political accountability (Aliyev, 2015; Broz, 2002). A high power distance country that does not emphasize such attributes is less likely to adopt a floating regime. Power distance may also affect exchange regime indirectly. High power distance countries tend to have stronger capital controls (De Jong et al., 2006) and weaker democratic accountability (Licht et al., 2007). Hence, they are more likely to adopt a fixed 15

regime. Masculinity. The masculinity/femininity dimension captures the degree to which “male assertiveness” is a more dominant value than “female nurturance” (Hofstede, 2001). In masculine societies, ambition, success, and personal ability are emphasized. A floating regime gives investors more opportunities to exercise their skill through, for example, speculative trading in the foreign exchange market. Therefore, we expect masculinity to be associated with a higher probability of adopting a floating regime. On the other hand, it is possible that leaders in masculine societies will prefer directly controlling the exchange rate in order to show the assertiveness of the government, therefore preferring a fixed regime. 3. Data To capture a country’s culture, we use Hofstede’s (1983) culture dimensions as our main measurement. “To develop a commonly acceptable, well-defined, and empirically based terminology to describe cultures, and to use systematically collected data about a large number of cultures, rather than just impressions,” (Hofstede, 1983), Hofstede (1983) originally derived his culture dimensions from a survey of IBM employees in the 1970s. He later expanded the surveys to approximately 80 countries. In each country, the survey is translated into the local language by a team of translators and local speakers in order to avoid misleading respondents. The survey was not originally aimed at studying national cultures, but rather to investigate goals in life and the workplace. Hofstede (1983) argues that, by using various survey data collected by others, along with descriptive statistics, he can construct culture indices while avoiding interference from researchers. The Hofstede (1983) cultural measures range from 0 to 100 along four dimensions: individualism, uncertainty avoidance, power distance, and masculinity. A higher score indicates that people in that country demonstrate characteristics in line with that culture 16

dimension. For example, the U.S. is a typical individualistic country, with an individualism score of 91; on the other hand, China is a typical collectivistic country, and has an individualism score of 20. Our classification of exchange rate regimes follows Ilzetzki et al. (2017) (hereafter, IRR). IRR (2017) categorize exchange rate regimes into four groups: 1) fixed, 2) crawling peg, 3) managed floating, and 4) free floating,6 based on market-determined exchange rates rather than officially announced rates. The IRR (2017) classification is thus de facto. We choose this classification, in line with prior studies on the determinants of exchange rate regimes (e.g., Chang and Lee, 2017; Lin and Ye, 2011; Markiewicz, 2006; Rodriguez, 2016), because a country’s actual regime may differ from its stated one.7 Figure 1 shows the frequency of countries with different exchange rate regimes over the sample period. We note that fewer countries adopt de facto free floating regimes, while similar numbers of countries adopt fixed, crawling peg, and managed floating regimes.8 We also note regime transitions over time. Collectivistic countries, such as Panama, Burkina Faso, Trinidad and Tobago, and El Salvador, are more likely to adopt a fixed regime. On the other hand, individualistic countries, such as Australia and the U.S., prefer a free floating regime. Figure 1 goes about here As discussed above, we use three sets of control variables that are also based on prior literature. The first set of controls includes OCA fundamentals: Trade Openness, the sum of the net exports of goods and services normalized by GDP; Trade Concentration, the share of trade with the three largest trading partners; Log GDP, the natural log of GDP (current U.S.$) Following prior literature (e.g., Chang and Lee, 2017; Markiewicz, 2006), we exclude observations classified as ‘“freely falling’ falling,” and dual markets with missing parallel market data. 7 For example, Markiewicz (2006) shows that, while some transition economies in Central and Eastern Europe formally announced the adoption of a floating exchange rate regime, their currencies actually rely on a de facto nominal exchange rate anchor. 8 Our main evidence continues to hold when we combine the classifications of managed floating and free floating regimes into one category. 6

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as a proxy for the size of the economy; Log GDP p.c., the natural log of GDP per capita (current U.S.$);9 Real Shocks, the standard deviation of the terms of trade over the previous five years adjusted by the average openness over the previous five years; and Nominal Shocks, the standard deviation of domestic credit to the private sector over the previous five years. The second set of controls includes other economic variables: Inflation, the consumer price inflation rate (transformed); Reserves, the ratio of international reserves (excluding gold) to GDP; Financial Development, the ratio of domestic credit to the private sector (% of GDP); and the Capital Controls index from the Fraser Institute. The third set of controls includes variables that capture a country’s political institutions: the Freedom index from Freedom House; and Political Instability, the sum of dummies for years in which the executive changed and a legislative election took place. Table 1 provides detailed variable definitions and data sources. Table 1 goes about here The final sample for our main specification, including all the controls above, contains 2,113 country-year observations for 78 countries over 1976-2014. Tables 2 and 3 summarize the sample distribution, and present summary statistics for the variables. Tables 2 and 3 go about here Table 4 presents Pearson correlations between the IRR classification and Hofstede’s (1983) four dimension of national culture. As can be seen, individualism and masculinity are positively correlated with a floating regime, while power distance and uncertainty avoidance are negatively correlated with a more flexible regime. This correlation suggests that individualistic countries tend to prefer a market-determined exchange rate over the government setting the rate for them. In the next section, we conduct an empirical analysis to shed more light on the relationship between national culture and the choice of exchange rate regime.

Theoretically, the income level of a country is not directly linked to its choice of exchange rate regime. Empirical results have been mixed (Rose, 2011). However, because most studies still control for it, we also include income level in this study. 9

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Table 4 goes about here 4. Empirical Analysis In our primary analysis, we test the relationship between national culture and the choice of exchange rate regime using the de facto IRR classification. Following previous literature (Berdiev et al., 2012; Chang and Lee, 2017; Von Hagen and Zhou, 2007; Yeyati et al., 2010), we use a multinomial logit (MNL) model for IRR classification. We note that some macroeconomic factors may suffer from simultaneity. For example, a country with low foreign reserves may prefer a floating regime, but a country with a floating regime does not necessarily require high foreign reserves. Similar concerns arise for economic performance and inflation. We therefore follow the literature (e.g., Chang and Lee, 2017; Frieden et al., 2010; Lin and Ye, 2011; Rodriguez, 2016), and use one-year lagged explanatory variables (except for culture, which is time-invariant) in all specifications.10 All regressions are estimated using robust standard errors clustered at the country level, and the fixed regime is the baseline outcome. Table 5 presents the estimation results of the MNL model. We run four regressions that include different combinations of the OCA fundamentals and the economic and political environment variables. For comparison purposes, and to assess the subsequent model fit, columns (1)-(3) report the results using the OCA fundamentals as controls (i.e., excluding the individualism/collectivism dimension culture). In columns (4)-(6), we further control for the individualism/collectivism dimension, and find that individualism significantly increases the probability of adopting a free floating, managed floating, or crawling peg against the fixed regime. Using a likelihood ratio test,11 we find that introducing the individualism index into a

Yeyati et al. (2010) point out that lagged independent variables may not be enough to address endogeneity. We acknowledge that we can only partially reduce this problem. We replicate our main specification by using twoyear lagged variables. The results, reported in Appendix Table 3, are consistent. 11 The results of a likelihood ratio test, tabulated in Table 5, show that introducing the individualism index can significantly improve the model fit. To properly conduct this test, we estimated the models both with and without the individualism index using regular standard errors (we do not report the results here; we only report test 10

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model can significantly improve the fit, which indicates that the individualism/collectivism dimension contributes significantly to explaining the choice of exchange rate regime. When we add controls for other economic variables in columns (7)-(9), we find that high individualism countries continue to have a significantly higher probability of implementing a more flexible than fixed regime. And when we further add the political institution variables in columns (10)-(12), we find that the coefficients on individualism remain positive and significant. The results in Table 5 show that, in line with our expectation, individualism has a strong association with the choice of exchange rate regime after controlling for OCA fundamentals and for variables related to the economic and political environments. In particular, the positive coefficients suggest that individualism has a direct effect on the preference of a floating regime, because economic agents in high individualism countries value independence, exhibit an overconfidence bias, and have a higher level of risk tolerance. Furthermore, in our main specification, reported in columns (10)-(12), the marginal effects show that a 1-standard deviation increase in individualism score leads to a 13.7% lower probability of implementing a fixed regime, and a 2.7% higher probability of implementing a free floating regime. Turning to the other explanatory variables, consistent with our expectations and with prior literature, we note that a country that relies more on international trade (Berdiev et al., 2012; Frieden et al., 2010; Yeyati et al., 2010), has a higher trade concentration (Von Hagen and Zhou, 2005), and a less developed financial system (Frieden et al., 2010; Markiewicz, 2006; Von Hagen and Zhou, 2005), is less likely to adopt a free floating regime. Results for the other variables, however, are weak. Overall, after controlling for various factors that have been previously shown to affect the

statistics). The estimated coefficients and standard errors in Table 5 are the MNL model using robust standard errors clustered at the country level.

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choice of exchange rate regime, we find that individualistic societies have a significantly higher probability of choosing a floating regime. This suggests that culture has an incremental role in explaining countries’ choices of exchange rate regimes. Table 5 goes about here Table 6, panels A-C, report the results of our main specification with the full set of controls using Hofstede’s (1983) three other dimensions of culture; for the sake of brevity, however, we do not report the results for the controls. In panel A, we find that, after controlling for other determinants, a high uncertainty avoidance country is less likely to choose a floating regime. This suggests that people in high uncertainty avoidance countries prefer less uncertainty in the day-to-day exchange rate. In panel B, we find that the coefficients on the managed floating and free floating regimes are significantly negative. Thus, a high power distance country is also less likely to choose a floating regime. This result suggests that people in high power distance countries are accustomed to ceding decision-making power to their leaders, who mitigate exchange rate volatility by adopting a less flexible rate regime. In panel C, we find that a high masculinity country is more likely to choose a free floating than a fixed regime. This suggests that people in a society with more masculine characteristics prefer a floating regime and to manage exchange rate risk themselves, rather than let the government directly control the exchange rate. Table 6, panel D, runs a horserace for the four cultural dimensions. Due to the high correlation among cultural dimensions, in this test we use orthogonalized cultural indices are the residuals of each cultural index regressed on the remaining three indices. Consistent with prior literature (e.g., Aggarwal et al., 2016; Triandis, 2001), the results show that, among the four cultural dimensions, individualism plays the most significant role in the choice of exchange rate regime. Table 6 goes about here 21

Overall, the results from our MNL model show that, even after we control for a host of variables previously shown to affect regime choice, national culture—especially the individualism/collectivism dimension—contributes significantly to the choice of exchange rate regime. 5. Robustness Checks 5.1. Endogeneity Culture and the choice of exchange rate regime are potentially endogenous, because they can be affected by geography, technology, regime shifts, and other shocks. As a result, it is possible that unobserved factors correlated with both may be biasing our estimation. To mitigate any endogeneity concerns, we use instrumental variables (IV) analysis, with the historical Pathogen Prevalence Index developed by Fincher et al. (2008) as an instrument. This index has been widely used in prior literature as an instrument for national culture (e.g. Gorodnichenko and Roland, 2016; Zheng et al., 2013). It uses data from atlases of infectious diseases that were compiled before the epidemiological revolution in treatment. Because historical diseases are unlikely to affect the choice of exchange rate regime through culture during our sample period, the Pathogen Prevalence Index is expected to satisfy the exclusion restriction for an instrument. Our main specification is an MNL model, so we cannot compute standard errors using standard econometric packages. We therefore adopt the bootstrapping technique used by Yeyati et al. (2010) to obtain the IV estimator for the IRR classification. Table 7 reports the results of the main specification for IRR classification using the historical disease index as an instrument. Column (1) reports the first-stage result and the weak IV test result. The F-statistic rejects the null hypothesis that the instrument is weakly correlated with the endogenous regressor. The standard errors of the second stage, reported in columns (2)-(4), are consistent estimators of the two-stage standard errors across 1,000 replications. We find 22

that our main result continues to hold after addressing potential endogeneity using the IV approach. Thus, high individualism countries are more likely to adopt a floating regime. Table 7 goes about here 5.2. Alternative Model Ordered logit models are often used in studying the determinants of the choice of exchange rate regime (Berdiev et al., 2012; Chang and Lee, 2017; Markiewicz, 2006; Rizzo, 1998; Rodriguez, 2016). We opt for an MNL model in our primary analysis, because an ordered logit assumes that the “degree of regime flexibility is monotonic in the regime determinants” (Von Hagen and Zhou, 2007, p. 1072). Nonetheless, in this section, we use an ordered logit model as a robustness check. The results are in Table 8, column (1). We find that the coefficient on individualism is significantly positive, which supports the view that individualistic countries are more likely to implement a floating regime. The signs of the coefficients on uncertainty avoidance, power distance, and masculinity also continue to hold (see Appendix Table 1), but the coefficients are no longer statistically significant. They thus weakly support our findings above. Table 8 goes about here 5.3. Alternative Classification of Exchange Rate Regime We next test the robustness of our results to the exchange rate regime classification of Shambaugh (2004). Shambaugh (2004) classifies a country’s exchange rate as pegged if its official exchange rate remains within a small band for a sufficiently long period of time, and as non-pegged otherwise. This classification, which is regarded as de facto, is also widely used in the literature (e.g., Chang and Lee, 2017; Rose, 2011). Because the Shambaugh (2004) classification is binary, we employ a panel logit model here. Table 8, column (2), reports the results. We find that the effect of individualism remains positive and significant. Specifically, a 23

country with a 1-standard deviation higher individualism score has a 13.97% higher probability of choosing a non-pegged (i.e., floating) regime. When we examine the other three dimensions of culture (see Appendix Table 2), we find that power distance significantly increases the likelihood that a country pegs its exchange rate, while masculinity (uncertainty avoidance) may increase the likelihood that a country floats (pegs) its exchange rate, as the latter effects are statistically insignificant. Because individualism continues to have the strongest effect on the choice of exchange rate regime, in the tests below, we limit discussion to those results. 5.4. Alternative Proxies for Culture Our next set of robustness tests uses alternative proxies for culture. Since Hofstede’s (1983) cultural dimensions are constructed from survey data collected in the late 1960s and early 1970s, there may be concerns about the stability of the scores over our sample period, and, in turn, about our inferences. According to Hofstede (2001), national cultures are stable over time. Hofstede (2001, p. 12) characterizes culture as “a crystallization of history in the minds, hearts, and hands of the present generation.” Since history cannot be changed, culture should be persistent over time. Nevertheless, national culture may change due to natural forces (climate change and spread of diseases, etc.) or to human forces (trade, conquest, political and economic domination, scientific discoveries, technological breakthroughs, etc.) (Kwok and Tadesse, 2006). Therefore, modernization and globalization may lead to changes in norms, values, and beliefs (Beugelsdijk, Maseland, and van Hoorn, 2015; Eun, Wang, and Xiao, 2015; Tang and Koveos, 2008) that could affect the relevance of Hofstede’s framework and our results. Beugelsdijk et al. (2015) test the validity of the assumption of temporal stability underlying the use of Hofstede’s framework. They find that although within-country Hofstede’s cultural dimensions tend to change over time, these changes are absolute rather than relative, as cultural differences between countries are relatively stable. They conclude (p. 238), “If there is an issue with the relevance or validity of Hofstede’s measures of national culture, itis not due to the 24

assumption of temporal stability.” To address this concern, we first use the updated Hofstede dimensions constructed by Tang and Koveos (2008). These updated indices use data from the 1990s, which is close to the median of our sample period. The updated scores indeed imply that, due to changes in economic conditions, Hofstede’s cultural dimensions change over time across countries. However, as the results in Table 9, panel A, indicate, we continue to find that individualistic countries tend to choose a floating regime after accounting for changes in culture over time. Since the sample size using the updated cultural dimensions is smaller than in our main specification, we also replicate the test using Hofstede’s original individualism scores on the reduced sample. The effect of individualism (untabulated) remains unchanged. Furthermore, we also construct a combined individualism index, using Hofstede dimensions before 1990, and Tang and Koveos (2008) in and after 1990. As shown in Table 9, panel B, the effect again holds. Table 9 goes about here As a further test, we use the nine proxies for national culture proposed by the GLOBE study of House et al. (2004). The GLOBE measurements of culture are constructed using more recent data. Among the nine GLOBE culture dimensions, in-group collectivism captures the degree to which individuals take pride in membership in small groups, such as their family and circles of close friends (House et al., 2004). People in high in-group collectivism countries are interdependent. This dimension of culture is conceptually more in line with Hofstede’s notion of collectivism/individualism (House et al., 2004).12 We expect that economic agents in a high in-group collectivism country will prefer that the government set a stable exchange rate on their behalf. GLOBE’s cultural dimensions have a practice, or “as is,” component, and a value, or

12

In-group collectivism is negatively correlated with the Individualism index.

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“should be,” component. In this study, we focus on the practice score, because it describes people’s actual experiences. The evidence, reported in Table 9, panel C, supports our conjecture. Specifically, high in-group collectivism countries tend to employ a fixed regime. This result supports our main finding using Hofstede’s individualism/collectivism dimension. 5.5. Subsample Analyses It is possible that the choice of exchange rate regime differs fundamentally between Eurozone and non-Eurozone countries (Chang and Lee, 2017). To reduce exchange rate variability and increase monetary stability, most European Union member states adopted the Euro beginning in 1999. The European Central Bank manages the monetary policy of all Eurozone countries. Thus, member countries cannot set their own rates. Rather, Eurozone countries are classified as having a fixed regime because they adopted the Euro. However, if adoption of the Euro is not motivated by culture (e.g., if it is more politically driven), the effect of individualism should hold. To test this prediction, we add a Eurozone dummy to our main specification in Table 10, panel A. We find that the coefficients on individualism are significantly positive, in line with our earlier findings. In Table 10, panel B, we show that the effect of individualism on the choice of exchange rate also holds for non-Eurozone countries. Table 10 goes about here The choice of exchange rate regime may also differ between developed and developing countries. For example, Berdiev et al. (2012) find that the effect of globalization on the choice of exchange rate regime differs across developing and developed countries. Because individualistic countries tend to be more developed, national culture may capture unobserved factors related to the level of development that can affect the choice of exchange rate regime, which would bias our estimation above. To test for this possibility, we rerun our main

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specification separately for developed and developing countries.13 The results are in Table 10, panels C and D. We find that individualism continues to have a significant effect on the choice of exchange rate regime. In particular, individualistic societies among both developed and developing countries tend to choose a free floating, rather than a fixed, regime, although the magnitudes of the coefficients are larger for developed countries. Thus, while the strength of the effect may differ, the results continue to show that individualistic countries are more likely to adopt a free floating regime. Due to data limitations, several countries have their first year of observation in the 1980s and 1990s. Therefore, we replicate our main specification with all controls with the more balanced sample, from 1980-2014 and from 1990-2014. As Table 10, panels E and F, show, the effects of individualism are consistent. Moreover, to capture the sample period immediately after the breakdown of Bretton Woods, from 1971-1975, we conduct the estimation using a similar method but with fewer control variables, i.e., only OCA fundamentals. The effect of individualism remains significant for the longer sample period. 5.6. Additional Control Variables Note that individualism/collectivism may affect the choice of exchange rate regime through other omitted variables. For example, culture and religion influence each other, and both can help explain economic growth (Gorodnichenko and Roland, 2011; McCleary and Barro, 2003), as well as a variety of other economic variables. Therefore, to avoid an omitted variable bias, and following Chui and Kwok (2008), we add religion to our main specification. The results are in Table 11, panel A. We find that religion has no significant effect on the choice of exchange rate regime, while the effect of individualism remains.

13

We classify a country as developed if it is a “high income” country according to the World Bank.

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Additionally, other economic and political variables correlated with culture may affect the choice of exchange rate regime. First, a country with weak fiscal discipline may have a limited ability to maintain a fixed regime. Prior evidence of this effect, however, is either mixed (e.g., Von Hagen and Zhou, 2005) or insignificant (Carmignani, Colombo, and Tirelli, 2008; Lin and Ye, 2011). Second, while floating regimes tend to rebalance their current account, governments operating under a fixed regime may run a chronic current account deficit, which can attract a speculative attack. Prior empirical results on this effect are again either mixed (e.g., Von Hagen and Zhou, 2005) or ambiguous (Lin and Ye, 2011). Third, previous literature has examined the effect of central bank independence (Aliyev, 2015; Berdiev et al., 2012; Chang and Lee, 2017; Frieden et al., 2010). More independent central banks are more likely to implement a floating regime to maintain their ability to set monetary policy. Table 11, panels B-D, report the results of our main specification after adding controls for the budget balance, the current account balance, and the degree of central bank independence, respectively.14 The results show that high individualism countries are significantly more likely to adopt a floating regime. Table 11 goes about here 6. Channels In this section, we supplement our analysis by using the WVS to identify channels through which culture can affect the choice of exchange rate regime. The WVS has been widely used to identify the effect of beliefs, culture, and values on economic outcomes. The theory suggests that economic agents in collectivistic countries prefer a strong leader who will look after their needs. One question in the WVS relates to this characteristic, namely, whether respondents

Due to data limitations for these variables, the number of observations drops from 2,113 to 1,398, and the data cover only 73 countries when we include all these variables in our specification. We therefore choose not to control for these three variables in our main specification, but rather include them in robustness checks here. 14

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believe their countries’ political systems should be characterized as having a strong leader. In our main sample of 2,113 observations from 78 countries, individualism is significantly negatively correlated with “having a strong leader.” We predict that countries with more agreement on this characteristic are less likely to adopt a floating regime, because people in these countries are more likely to want the government to manage exchange rate risk on their behalf via a fixed regime. The results in Table 12, panel A, are in line with this view. Turning to individualism, the theory suggests that people in individualistic countries are more independent. One question in the WVS relates to this characteristic, namely, whether respondents believe that independence is an important child quality. In our main sample, individualism is significantly positively correlated with this belief. We therefore predict that countries with more agreement on this question are more likely to adopt a floating regime, because people in those countries are more likely to prefer managing exchange rate risk themselves. The results in Table 12, panel B, again confirm this view. Overall, the results from our WVS analysis point to two channels through which culture can affect the choice of exchange rate regime, namely, preference for a strong leader, and preference for independence. Table 12 goes about here 7. Conclusion This paper presents a theoretical framework and provides the first evidence on the effect of national culture on a country’s exchange rate regime choice. Specifically, using a de facto exchange rate regime classification, we find that culture, as proxied for by Hofstede’s (1983) individualism dimension, is significantly positively related to the adoption of a floating regime. When we control for other factors previously shown to affect regime choice, we find that a country with a 1-standard deviation higher individualism score has a 13.7% lower probability

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of implementing a fixed regime, and a 2.7% higher probability of adopting a free floating regime. These results are robust to the instrumental variables approach, an alternative estimation technique, an alternative regime classification, alternative proxies for culture, a subsample analysis, and additional controls. Furthermore, we use the WVS to identify channels through which individualism affects the choice of exchange rate regime, namely, preference for a strong leader and preference for independence. When we consider Hofstede’s three other cultural dimensions, we find that they also help explain the choice of exchange rate regime. Uncertainty avoidance and power distance are associated with a higher probability of a fixed regime, while masculinity is associated with a floating regime. However, their effects are weaker. To summarize, we find that national culture is an important determinant of exchange rate regime choice, and it appears to be more important than the other factors that affect regime choice. Our results thus provide support for Rose’s (2011) assertion that country-specific preferences may play an important role when otherwise similar countries make different choices. Although we provide new evidence on the incremental role of national culture in explaining a country’s choice of exchange rate regime, our study has certain limitations. First, our instrumental variables approach may not fully eliminate endogeneity. It would be ideal, but is not possible, to randomly assign different cultures to countries, and then observe their choice of exchange rate regime. Second, to better understand the role of culture, one could conduct a case study of the choices of some countries or regions that have experienced political upheaval (e.g., the reunification of West and East Germany, the handover of Hong Kong to mainland China, and the dissolution of the Soviet Union). However, our measure of national culture is time-invariant, which prevents us from linking national culture to exchange rate regime choice around political upheaval. Third, the history of exchange rate regime choices does not begin solely from the post-Bretton Woods era. In fact, countries adopted different exchange rate 30

regimes during earlier times, such as during the gold and silver standards. Future research may wish to explore how national culture plays a role beyond our sample period. In spite of these potential limitations, our research has important practical implications. First, our findings suggest that the choice of foreign exchange regime is not a one-size-fits-all decision. In setting optimal currency regimes, policymakers should consider not only economic fundamentals but also non-economic factors, such as national culture. If the exchange rate regime of a country is aligned with its national culture, economic agents can better accept and adapt to the regime, leading to higher social welfare. Second, in conducting international business, individuals and corporate economic agents should consider that the host country may have an entirely different currency regime than their home country. The new environment may require cross-border economic agents to adapt their business strategies in order to succeed.15 Third, when multinational corporations make foreign investment decisions, they should take such adaptation costs into consideration. As documented in Siegel, Licht, and Schwartz (2011), cultural distance increases the costs of doing business overseas, and reduces cross-border investment flows (e.g., from syndicated loans, bonds, equities, and mergers and acquisitions). If multinational corporations underestimate such costs, seemingly positive-NPV projects may ultimately return substantial economic losses.

For example, economic agents from a country with a fixed regime may not be familiar with hedging techniques that can reduce exchange rate risks in a country with a floating regime. Conversely, economic agents from a country with a floating regime may need to adapt to the absence of speculative opportunities when doing business in a country with a fixed regime. 15

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37

TABLES AND FIGURES

38

Table 1 Variable Definition and Source Variable Definition Dependent Variables IRR 1: fixed, including no separate legal tender, preannounced peg, or currency board arrangement, preannounced horizontal band that is narrower than or equal to ±2%, de facto peg. 2: crawling peg, including preannounced crawling peg, preannounced crawling band that is narrower than or equal to ±2%, de facto crawling peg, de facto crawling band that is narrower than or equal to ±2%. 3: managed floating, including preannounced crawling band that is wider than or equal to ±2%, de facto crawling band that is narrower than or equal to ±5%, moving band that is narrower than or equal to ±2% (i.e., allows for both appreciation and depreciation over time). 4: free floating Shambaugh

0: pegged; 1: not pegged

Source Ilzetzki et al. (2017), course classification

Shambaugh (2004)

Hofstede's Culture Dimensions Individualism Ranges from 0 to 100

Hofstede (2001)

Uncertainty Avoidance Power Distance

Ranges from 0 to 100

Hofstede (2001)

Ranges from 0 to 100

Hofstede (2001)

Masculinity

Ranges from 0 to 100

Hofstede (2001)

Control Variables Trade Openness The sum of exports and imports of goods and services, normalized by GDP Trade Concentration

Share of total trade with the three largest trading partners

Log GDP

Natural log of GDP (current U.S.$)

Log GDP p.c.

Natural log of GDP per capita (current U.S.$)

Real Shocks

Standard deviation of the terms of trade over the previous five years adjusted by average openness in the previous five years Nominal Shocks Standard deviation of the domestic credit to private sector over the previous five years Reserves

Ratio of international reserves (excluding gold) to GDP

39

World Development Indicators IMF, Direction of Trade Statistics World Development Indicators World Development Indicators World Development Indicators World Development Indicators World Development Indicators

Inflation Financial Development Capital Control Freedom

Political Instability Religion

Transformed consumer price inflation rates π*. The transformation uses the formula π*=π/(1+π), where π denotes the raw data series Domestic credit to private sector (% of GDP)

World Development Indicators World Development Indicators Intensity of capital controls, ranges from 0 to 10. Fraser Institute, Higher score represents less capital control Economic Freedom Index of political freedom and civil liberty. The index Freedom House is constructed by first averaging the scores of political rights and civil liberties (on a scale from 1 to 7), and then subtracting the average scores from 8. The index is again on a scale from 1 to 7, but with higher values representing higher degrees of freedom A measure of political instability, defined as the sum of 1) a dummy for the year in which the effective executives changed, and 2) a dummy for the year in which a legislative election takes place Ratio of the population adhering to Christianity and Islam, ranging from 0 to 1 Central government budget balance, normalized by GDP

World Bank, Political Institutions 2015

World Religion Dataset Government IMF, World Budget Balance Economic Outlook Current Account Current account balance, normalized by GDP IMF, World Balance Economic Outlook Central Bank Ranges from 0 to 1, with a higher value meaning Garriga (2016) Independence greater independence Notes: Shambaugh is reverse-coded from the original Shambaugh (2004) exchange rate regime data for ease of interpretation. Capital Control is available every five years prior to 2000. Missing values for Capital Control are replaced with values from the previous year.

40

Table 2 Sample Distribution

　

Country Name

N

　

　

　

First Country Name N Year Albania 16 1999 Guatemala 35 Argentina 26 1979 Hungary 19 Australia 39 1976 Iceland 32 Austria 32 1976 India 39 Bangladesh 34 1979 Indonesia 29 Belgium 10 1998 Iran, Islamic Rep. 3 Brazil 19 1995 Ireland 33 Bulgaria 18 1997 Israel 29 Burkina Faso 9 2006 Italy 33 Canada 34 1976 Jamaica 28 Chile 36 1978 Japan 39 China 33 1982 Jordan 34 Colombia 31 1976 Korea, Rep. 38 Costa Rica 36 1976 Latvia 10 Croatia 15 2000 Lithuania 10 Czech Republic 17 1998 Luxembourg 9 Denmark 39 1976 Malaysia 39 Dominican Republic 33 1976 Malta 39 Ecuador 24 1976 Mexico 31 Egypt, Arab Rep. 39 1976 Morocco 31 El Salvador 39 1976 Netherlands 32 Estonia 15 2000 New Zealand 33 Finland 32 1977 Nigeria 31 France 32 1976 Norway 39 Germany 17 1976 Pakistan 39 Ghana 24 1984 Panama 39 Greece 39 1976 Peru 21 Notes: This table reports the full sample distribution by country.

41

　 First Year 1976 1996 1976 1976 1985 1976 1976 1986 1976 1976 1976 1981 1976 2000 2000 2006 1976 1976 1976 1976 1976 1979 1976 1976 1976 1976 1994

　 Country Name Philippines Poland Portugal Romania Russian Federation Saudi Arabia Singapore Slovak Republic Slovenia South Africa Spain Suriname Sweden Switzerland Tanzania Thailand Trinidad and Tobago Turkey United Kingdom United States Uruguay Venezuela, RB Vietnam Zambia Total Observations Number of Countries 　

　 N 38 20 33 10 15 4 35 17 15 16 33 4 39 30 20 38 39 19 39 39 24 30 11 13 2113 78 　

　 First Year 1976 1995 1976 2001 2000 2011 1980 1998 2000 1999 1976 2011 1976 1985 1995 1976 1976 1976 1976 1976 1979 1976 2004 2002

　

Table 3 Summary Statistics Standard 25th Deviation Percentile IRR 2,113 2.170 0.913 1.000 Shambaugh 2,242 0.666 0.472 0.000 Individualism 2,113 42.699 24.586 20.000 Uncertainty Avoidance 2,113 64.873 22.805 49.000 Power Distance 2,113 58.406 21.804 39.000 Masculinity 2,113 48.994 19.486 40.000 Trade Openness 2,113 75.528 56.827 43.822 Trade Concentration 2,113 0.506 0.107 0.431 Log GDP 2,113 25.202 1.841 23.871 Log GDP p.c. 2,113 8.564 1.461 7.509 Real Shocks 2,113 0.001 0.002 0.000 Nominal Shocks 2,113 5.065 5.891 1.732 Reserves 2,113 0.129 0.153 0.038 Inflation 2,113 0.763 2.380 0.725 Financial Development 2,113 59.791 43.797 26.376 Capital Control 2,113 4.275 3.070 2.000 Freedom 2,113 5.520 1.603 4.500 Political Stability 2,113 0.460 0.556 0.000 Government Budget Balance 1,627 -0.709 0.143 -0.802 Current Account Balance 1,865 -1.130 6.038 -4.302 Central Bank Independence 2,006 0.498 0.222 0.310 Notes: This table reports the summary statistics for the variables in the main analysis. Variable

N

Mean

42

75th Percentile 3.000 1.000 65.000 86.000 73.000 63.000 86.896 0.573 26.431 9.734 0.001 6.168 0.158 0.920 82.771 6.923 7.000 1.000 -0.625 1.519 0.693

Table 4 Pearson Correlation Matrix IRR Individualism Uncertainty Avoidance Power Distance Masculinity Variable IRR 1 Individualism 0.248* 1 Uncertainty Avoidance -0.127* -0.284* 1 Power Distance -0.135* -0.671* 0.212* 1 Masculinity 0.162* 0.010 0.007 0.191* 1 Notes: This table reports Pearson correlations between the exchange rate regimes and Hofstede's culture dimensions. * denotes that correlation coefficients are significant at the 5% level or better.

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Table 5 IRR in Multinomial Logit Model with Different Controls (1) (2) OCA Fundamentals Variables Crawling Peg Managed Floating Individualism

(3)

(5) (6) OCA Fundamentals Free Floating Crawling Peg Managed Floating Free Floating 　 0.030*** 0.029** 0.068*** 　 (0.011) (0.013) (0.020) Trade Openness -0.006* 0.000 -0.091*** -0.005 0.002 -0.084*** (0.004) (0.005) (0.031) (0.004) (0.005) (0.029) Trade Concentration -0.738 -2.292 -11.277** -1.350 -3.046 -13.342** (1.967) (1.988) (5.436) (1.819) (2.139) (6.401) Log GDP 0.158 0.191 0.393 0.130 0.175 0.456 (0.164) (0.181) (0.356) (0.155) (0.177) (0.315) Log GDP p.c. -0.396** -0.222 1.254** -0.714*** -0.532** 0.529 (0.200) (0.217) (0.600) (0.228) (0.239) (0.539) Real Shocks -63.556 -44.916 125.320 -54.765 -32.690 146.363 (67.775) (75.357) (77.421) (67.435) (84.500) (104.728) Nominal Shocks -0.038 0.011 -0.038 -0.039 0.009 -0.023 (0.031) (0.023) (0.055) (0.031) (0.024) (0.048) Constant 0.678 -1.571 -13.749* 3.105 0.583 -12.089* (3.854) (4.221) (8.042) (3.561) (4.325) (7.140) Observations 2,113 　 2,113 Number of Countries 78 　 78 Likelihood Ratio Test 120.61 　 Prob > chi2 　 0 　 　 　 　 Notes: This table reports the results of the multinomial logit model. Each set of three columns represents a separate regression result. The fixed exchange rate is the base outcome. All variables are the one-year lagged value, except for culture. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

44

(4)

Table 5 IRR in Multinomial Logit Model with Different Controls (7) (8)

(9)

OCA Fundamentals and Financial Variables　 Variables Individualism Trade Openness Trade Concentration Log GDP Log GDP p.c. Real Shocks Nominal Shocks Reserves Inflation Financial Development Capital Control Freedom Political Stability

Crawling Peg 0.034*** (0.011) -0.003 (0.004) -1.319 (2.042) 0.227 (0.155) -0.517** (0.258) -73.246 (67.278) 0.005 (0.032) 1.696 (1.905) 0.052 (0.051) -0.020*** (0.006) -0.066 (0.069)

Managed Floating 0.037*** (0.013) -0.006 (0.006) -1.929 (2.020) 0.187 (0.192) -0.513* (0.272) -42.977 (81.652) 0.019 (0.028) 5.204** (2.133) 0.041 (0.058) -0.004 (0.007) -0.018 (0.067)

Free Floating 0.073*** (0.027) -0.113*** (0.043) -8.940 (5.623) 0.043 (0.406) -0.021 (0.539) 80.987 (96.105) -0.078 (0.069) 2.845 (4.269) -0.009 (0.025) 0.022** (0.011) 0.142 (0.160) 　 　 　 　 45

(10) (11) (12) OCA Fundamentals, Financial Variables, and Political Variables Crawling Peg Managed Floating Free Floating 0.031*** 0.034** 0.077*** (0.011) (0.013) (0.029) -0.002 -0.005 -0.114*** (0.005) (0.006) (0.043) -1.546 -2.210 -9.549* (2.014) (2.023) (5.339) 0.293* 0.258 0.085 (0.173) (0.206) (0.401) -0.723** -0.734** -0.039 (0.293) (0.320) (0.577) -56.866 -27.332 85.103 (70.457) (89.029) (87.645) 0.006 0.021 -0.086 (0.032) (0.028) (0.077) 2.029 5.591*** 1.651 (1.913) (2.010) (4.230) 0.046 0.035 -0.006 (0.047) (0.051) (0.024) -0.019*** -0.004 0.023** (0.006) (0.007) (0.011) -0.069 -0.021 0.129 (0.069) (0.068) (0.168) 0.231 0.246 -0.130 (0.147) (0.171) (0.387) 0.058 0.011 -0.201 (0.092) (0.085) (0.203)

Constant

-0.384 (3.645)

-0.568 0.607 -1.497 -1.740 0.717 (4.718) (8.629) (3.850) (4.852) (8.586) Observations 2,113 2,113 　 Number of Countries 78 78 　 Likelihood Ratio Test 146.52 120.61 　 Prob > chi2 0 0 　 　 　 　 Notes: This table reports the results of the multinomial logit model. Each set of three columns represents a separate regression result. The fixed exchange rate is the base outcome. All variables are the one-year lagged value except for culture. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

46

Table 6 IRR with Four Culture Dimensions Panel A: Uncertainty Avoidance Variables Uncertainty Avoidance

(1) Crawling Peg -0.027*** (0.009)

(2) Managed Floating -0.018* (0.011) Yes 2,113 78

(3) Free Floating -0.041* (0.023)

(1) Crawling Peg -0.019 (0.012)

(2) Managed Floating -0.026** (0.013) Yes 2,113 78

(3) Free Floating -0.074* (0.042)

(1) Crawling Peg -0.010 (0.011)

(2) Managed Floating -0.003 (0.015) Yes 2,113 78

(3) Free Floating 0.063** (0.031)

Controls Observations Number of Countries Panel B: Power Distance Variables Power Distance Controls Observations Number of Countries Panel C: Masculinity Variables Masculinity

Controls Observations Number of Countries Panel D: All Four Culture Dimensions

(2) (3) Managed Floating Free Floating 0.064** 0.161** (0.026) (0.064) Orthogonalized Uncertainty -0.019* -0.030 Avoidance (0.011) (0.029) Orthogonalized Power Distance -0.061** -0.116 (0.025) (0.132) -0.007 0.063 Orthogonalized Masculinity (0.016) (0.047) Controls Yes Observations 2,113 Number of Countries 78 Notes: This table reports the results for the multinomial logit model. Each panel represents a separate regression with different aspects of culture dimensions. The fixed exchange rate is the base outcome. All variables are the one-year lagged value except for Culture. All controls listed in Table 5, columns (10)-(12), are included but not reported. In panel D, orthogonalized culture dimensions are the in-sample unexplained residual of regressing each culture dimension on the three others. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1. Variables Orthogonalized Individualism

(1) Crawling Peg 0.048** (0.024) -0.030*** (0.009) -0.039 (0.024) -0.012 (0.012)

47

Table 8 Robustness Check: Alternative Estimation Model and Alternative Exchange Rate Regime Classification (1) (2) Ordered Logit for IRR Panel Logit for Shambaugh Variables More Flexible Regime Non-peg Individualism 0.026** 0.067*** (0.010) (0.021) Trade Openness -0.009 -0.011* (0.006) (0.006) Trade Concentration -2.370 -3.575 (1.495) (2.668) Log GDP 0.182 0.873** (0.152) (0.415) Log GDP p.c. -0.407* -1.289** (0.247) (0.573) Real Shocks 49.849 -93.185* (124.221) (52.750) Nominal Shocks -0.018 0.031 (0.020) (0.022) Reserves 4.340*** 1.620 (1.197) (2.014) Inflation 0.017 0.059** (0.023) (0.027) Financial Development 0.010* -0.020*** (0.006) (0.007) Capital Control 0.034 -0.091 (0.051) (0.068) Freedom 0.108 0.222 (0.154) (0.143) Political Stability 0.069 0.001 (0.069) (0.097) Constant -9.928 (7.190) Observations 2,113 2,242 Number of Countries 78 76 Notes: This table reports the results of the IRR classification using an ordered logit model, and the Shambaugh classification using the panel logit model. All variables are the one-year lagged value except for Individualism. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

48

Table 7 Robustness Check: Instrumental Variable Estimation with Bootstrap Standard Errors (1) (2) (3) (4) Managed Variables Individualism Crawling Peg Free Floating Floating Pathogen -18.638*** Prevalence Index (0.738) Predicted 　 0.064*** 0.052*** 0.069** 　 (0.008) (0.007) (0.032) Individualism Trade Openness 0.006 -0.002 -0.004** -0.110*** (0.009) (0.002) (0.002) (0.040) Trade -0.723 -1.747*** -2.330*** -12.217*** (3.474) (0.635) (0.660) (3.720) Concentration Log GDP 3.990*** 0.198*** 0.197*** 0.000 (0.272) (0.053) (0.056) (0.204) Log GDP p.c. -0.109 -0.867*** -0.795*** 0.205 (0.518) (0.106) (0.114) (0.306) Real Shocks 615.011*** -82.718** -46.835 69.061 (180.302) (41.338) (41.443) (123.952) Nominal Shocks -0.080 0.009 0.022* -0.069* (0.064) (0.015) (0.013) (0.040) Reserves -7.696*** 2.287*** 5.482*** -5.000* (2.864) (0.739) (0.715) (2.817) Inflation 0.171 0.029 0.023 -0.023 (0.137) (0.090) (0.080) (0.087) Financial 0.016 -0.020*** -0.004* 0.017*** Development (0.012) (0.003) (0.002) (0.006) Capital Control -0.144 -0.062*** -0.017 0.165*** (0.124) (0.024) (0.022) (0.050) Freedom 4.231*** 0.006 0.095 -0.111 (0.321) (0.076) (0.076) (0.194) Political Stability -0.576 0.103 0.045 -0.247 (0.594) (0.114) (0.113) (0.253) Constant -79.976*** 2.049 0.463 3.166 (6.480) (1.347) (1.484) (4.652) Observations 2,113 2,113 Number of 78 78 Countries F-statistic 637.020 Prob > F 0.000 　 　 Notes: This table reports the results of IV estimation using the bootstrap method. Column (1) reports the first-stage result using the Pathogen Prevalence Index (Fincher et al., 2008) as the instrument. The F-statistic shows the Pathogen Prevalence Index exhibits significant explanatory power on Individualism. Columns (2)-(4) report the second-stage MNL model for IRR classification. All variables are the one-year lagged value except for Individualism and the Pathogen Prevalence Index. Bootstrap standard errors are achieved from 1,000 replications. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

49

Table 9 Robustness Check: Alternative Measurement of Culture Panel A: Updated Individualism from Tang and Koveos (2008) (1) (2) (3) Variables Crawling Peg Managed Floating Free Floating TK Individualism 0.049*** 0.043** 0.146*** (0.012) (0.017) (0.044) Controls Yes Observations 1,562 Number of Countries 47 Panel B: Updated Individualism from Tang and Koveos (2008) Combined with Hofstede's Individualism (1) (2) (3) Variables Crawling Peg Managed Floating Free Floating Combined Individualism 0.048*** 0.041** 0.088*** (0.012) (0.018) (0.032) Controls Yes Observations 1,562 Number of Countries 47 Panel C: In-Group Collectivism from GLOBE (1) (2) (3) Variables Crawling Peg Managed Floating Free Floating In-Group Collectivism -0.752** -0.862* -4.470*** (0.380) (0.476) (1.642) Controls Yes Observations 1,675 Number of Countries 54 Notes: This table reports the results of the multinomial logit model. Each panel represents a separate regression using a different measurement of culture. The fixed exchange rate is the base outcome. All controls listed in Table 5, columns (10)-(12), are included but not reported. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

50

Table 10 Robustness Check: Different Sample Compositions Panel A: Controlling for Eurozone Dummy (1) Variables Crawling Peg Individualism 0.040*** (0.012) Eurozone Country -1.398*** (0.504) Controls Observations Number of Countries Panel B: Subsample: Non-Eurozone Countries (1) Variables Crawling Peg Individualism 0.045*** (0.017) Controls Observations Number of Countries Panel C: Subsample: Developed Countries (1) Variables Crawling Peg Individualism 0.056*** (0.021) Controls Observations Number of Countries Panel D: Subsample: Developing Countries (1) Variables Crawling Peg Individualism 0.009 (0.018) Controls Observations Number of Countries Panel D: Subsample: 1980-2014 (1) Variables Crawling Peg Individualism 0.029** (0.013) Controls Observations Number of Countries Panel E: Subsample: 1990-2014 (1) Variables Crawling Peg Individualism 0.023* (0.014) Controls Observations Number of Countries Panel F: Subsample: 1971-2014

51

(2) Managed Floating 0.049*** (0.014) -3.154*** (0.632) Yes 2,113 78

(3) Free Floating 0.098*** (0.028) -0.856 (1.395)

(2) Managed Floating 0.048*** (0.018) Yes 1,672 60

(3) Free Floating 0.111*** (0.030)

(2) Managed Floating 0.065*** (0.021) Yes 1,104 39

(3) Free Floating 0.195*** (0.045)

(2) Managed Floating 0.025 (0.024) Yes 1,009 39

(3) Free Floating 0.102*** (0.034)

(2) Managed Floating 0.031** (0.014) Yes 1,948 77

(3) Free Floating 0.083** (0.037)

(2) Managed Floating 0.024* (0.014) Yes 1,504 77

(3) Free Floating 0.096** (0.048)

(2) (3) Managed Floating Free Floating 0.026** 0.061*** (0.012) (0.018) Controls Only OCA Fundamentals Observations 2,477 Number of Countries 79 Notes: This table reports the results of multinomial logit model. Each panel represents a separate regression. The fixed exchange rate is the base outcome. All controls listed in Table 5, columns (10)-(12), are included but not reported, except in panel F. “High Income Countries,” as defined by the World Bank, are categorized as Developed Countries. Panel F only controls for OCA variables to achieve more observations and cover a longer sample period. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1. Variables Individualism

(1) Crawling Peg 0.025*** (0.009)

52

Table 11 Robustness Check Panel A: Controlling for Religion Variables Individualism Religion

(1) Crawling Peg 0.031*** (0.011) -0.474 (0.984)

Controls Observations Number of Countries

(2) Managed Floating 0.034** (0.014) 0.448 (1.087) Yes 2,113 78

Panel B: Controlling for Government Budget Balance (1) (2) Variables Crawling Peg Managed Floating Individualism 0.023* 0.030** (0.013) (0.015) Government Budget Balance -2.014 -0.988 (1.981) (1.531) Controls Yes Observations 1,627 Number of Countries 74 Panel C: Controlling for Current Account Balance (1) (2) Variables Crawling Peg Managed Floating Individualism 0.029** 0.028** (0.013) (0.014) Current Account Balance -0.026 0.006 (0.027) (0.029) Controls Yes Observations 1,865 Number of Countries 77

(3) Free Floating 0.077*** (0.029) 1.017 (2.218)

(3) Free Floating 0.085*** (0.030) -20.538*** (5.090)

(3) Free Floating 0.084* (0.043) 0.067 (0.100)

Panel D: Controlling for Central Bank Independence (1) (2) (3) Variables Crawling Peg Managed Floating Free Floating Individualism 0.023* 0.029** 0.077*** (0.013) (0.015) (0.028) Central Bank Independence -2.828*** -2.345** -0.704 (0.998) (1.056) (1.922) Controls Yes Observations 2,006 Number of Countries 　 78 　 Notes: This table reports the results of the multinomial logit model. Each panel represents a separate regression. The fixed exchange rate is the base outcome. All controls listed in Table 5, columns (7)-(9), are included but not reported. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1. 53

Table 12 Robustness Check: Channels Panel A: Prefer Having a Strong Leader (1) Variables Crawling Peg Strong Leader -1.975*** (0.622) Controls Observations Number of Countries

(2) Managed Floating -1.416** (0.721) Yes 1,707 63

(3) Free Floating -4.577*** (1.600)

Panel B: Important Child Qualities: Independence (1) (2) (3) Variables Crawling Peg Managed Floating Free Floating Independence 1.648 1.086 5.201* (1.304) (1.656) (3.084) Controls Yes Observations 1,740 Number of Countries 45 Notes: This table reports the results of the multinomial logit model. Each panel represents a separate regression. The reported independent variables are from the World Values Survey. Higher scores denote that more people agree with the statement. The fixed exchange rate is the base outcome. All controls listed in Table 5, columns (10)-(12), are included but not reported. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

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Appendix Tables Appendix Table 1 Ordered Logit Model for IRR with Other Three Culture Dimensions (1) (2) Variables IRR IRR Uncertainty Avoidance -0.012 (0.008) Power Distance -0.016 (0.010) Masculinity

(3) IRR

0.013 (0.010) Controls Yes Yes Yes Observations 2,113 2,113 2,113 Number of Countries 78 78 78 Notes: This table reports the results of the IRR classification using an ordered logit model. Each column represents a separate regression result. All controls listed in Table 5, columns (10)-(12), are included but not reported. All variables are the one-year lagged value except for culture. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

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Appendix Table 2 Panel Logit for Shambaugh with Other Three Culture Dimensions (1) (2) Variables Non-peg Non-peg Uncertainty Avoidance -0.012 (0.019) Power Distance -0.077*** (0.029) Masculinity

(3) Non-peg

0.008 (0.023) Controls Yes Yes Yes Observations 2,242 2,242 2,242 Number of Countries 76 76 76 Notes: This table reports the results of the Shambaugh classification using an ordered logit model. Each column represents a separate regression result. All controls listed in Table 5, columns (10)-(12), are included but not reported. All variables are the one-year lagged value except for culture. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

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Appendix Table 3 MNL model with Two-year Lag Variables (1) (2) (3) Variables Crawling Peg Managed Floating Free Floating Individualism 0.031*** 0.033** 0.080*** (0.012) (0.014) (0.031) Trade Openness -0.001 -0.005 -0.123*** (0.005) (0.006) (0.047) Trade Concentration -1.699 -2.249 -10.848* (2.058) (2.085) (5.706) Log GDP 0.266 0.242 0.055 (0.174) (0.206) (0.412) Log GDP p.c. -0.721** -0.674** 0.085 (0.295) (0.318) (0.593) Real Shocks -49.821 -12.843 104.618 (74.152) (88.403) (111.387) Nominal Shocks 0.007 0.017 -0.116 (0.034) (0.029) (0.089) Reserves 1.738 5.604*** 3.288 (1.960) (2.041) (4.449) Inflation 0.053 0.028 -0.007 (0.046) (0.048) (0.027) Financial Development -0.019*** -0.003 0.025** (0.006) (0.007) (0.012) Capital Control -0.074 -0.023 0.131 (0.071) (0.068) (0.170) Freedom 0.210 0.207 -0.170 (0.144) (0.168) (0.400) Political Stability 0.056 0.070 -0.237 (0.099) (0.085) (0.176) Constant -0.697 -1.567 1.301 (3.887) (4.903) (9.063) Observations 2,051 Number of Countries 78 　 Notes: This table reports the result of the multinomial logit model. The fixed exchange rate is the base outcome. All variables are the two-year lagged value except for culture. Robust standard errors are clustered at the country level. *** p<0.01, ** p<0.05, and * p<0.1.

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Highlights  Study examines the effect of national culture on exchange rate regime choice. 

Individualistic countries are more likely to adopt a freely floating rate regime.



The effects of other cultural dimensions are weaker than that of individualism.

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