International migration is a complex phenomenon extensively discussed in the literature, primarily focusing on factors such as economic prospects, social ties, and political crises (Massey et al., 1993). However, in today’s globalized world, knowledge about the culture and lifestyle of distant countries is transmitted not only through the internet but also through in-person immersion via tourism, study abroad programs, and global initiatives like working holiday schemes. These interactions often inspire individuals to migrate for reasons beyond economic, social, or political considerations—specifically, the desire to experience different cultures. For example, some Americans have moved to Europe seeking a different “work culture” and a higher quality of life (Moser, 2023). Similarly, many move to Japan driven by interest in its unique cultural elements, traditions, or lifestyle. Reports show that some affluent Chinese may choose to relocate to Japan instead of the United States due to their attraction to Japan’s similar but unique cultural traditions and environments (Kuhn, 2024). Likewise, some Westerners may first be merely fascinated with Japanese animation and games. Yet, as their interests of the Japanese culture grew deeper after visiting Japan, they eventually decided to stay long-term and migrate to Japan (BBC StoryWorks, n.d.).

At the same time, recent immigration policies emphasizing “full cultural integration” influence migration decisions. Such policies encourage migrants to settle in countries where cultural proximity reduces the costs of integration. For instance, in 2025, Germany announced reforms requiring naturalization candidates to demonstrate B1-level proficiency in German and pass a citizenship test covering German society, lifestyle, and “historical responsibility” (European Commission, 2025). Likewise, the United Kingdom has also tightened language requirements for Indefinite Leave to Remain (ILR), raising the standard from B1 to B2, with the new regulation taking effect in March 2027 (GOV.UK., 2026). This trend of stricter immigration guidelines is not limited to Europe. In Asia, Japan plans to introduce more rigorous requirements related to Japanese language proficiency, cultural understanding, and adherence to societal norms (The Yomiuri Shimbun, 2025). Under these reforms, foreign nationals seeking permanent residency or citizenship must attain high levels of Japanese language skills to facilitate daily communication and social integration.

Despite these socio-political developments, the literature has yet to fully catch up with the increasing emphasis on cultural factors as reasons for migration. This article contributes to the field by exploring the role of cultural factors in migration decisions. We extend existing research beyond traditional economic, social, and political perspectives by examining the relationship between cultural distance and migration flows. This approach aims to deepen our understanding of the significance of culture, especially amid growing attention to socio-cultural integration through language acquisition, civic understanding, and values compatibility. Based on the United Nations Department of Economic and Social Affairs, Population Division (2019, 2024) and other data sets, the findings from the gravity model provide clear support for the importance of cultural proximity in explaining migration flows.

Scholars have conducted extensive research on the considerations involved in moving to a new destination, particularly economic, social, and political factors. Cultural factors are often less examined in migration studies (Grohmann, 2023; Wang et al., 2018). In the following section, we first briefly discuss the limitations of current literature, which focuses on economic, social, and political factors to explain migration flow between two societies. We then suggest how culturally related factors shape the migration flow between these societies.

Most studies in understanding migration flow suggest that the flow reflects economic benefits and social networks of migrants, or the political environment (Chan et al., 2022; Etling et al., 2020; Fong et al., 2025; Kan et al., 2025; Lam & Fong, 2025; Massey et al., 1993). Nonetheless, there are limitations to understanding migration flow solely based on these perspectives. First, these perspectives may overlook adaptation costs beyond economic gains and network factors in destination choices. They may overlook how culture acts as a cost that attracts or deters migration to similar or distant places. For example, studies have shown that “home-like” environments in host countries lower barriers and adjustment costs, which enhances migrants’ psychological well-being and suggests the attractiveness of moving to culturally similar destinations (Guo et al., 2023). Second, conventional perspectives may underestimate cultural aspirations and preferences in destination choices. In today’s globalized context, characterized by high mobility of people and information, migration flow and destination choices may be attributed to an individual’s affinity toward a country’s lifestyle or to media influence (Carling, 2024; de Haas, 2021). Thus, it may result in cultural asymmetric similarity between countries, which drives migration. Similarly, growing proximity between home and host countries through cultural remittances from migrants may perpetuate migration cycles (Levitt, 1998). This can lead to a “culture of migration” and drive migration beyond economic, social, and political factors (Massey et al., 1993). Hence, these limitations underscore the need to incorporate cultural factors to provide a more holistic lens and recognize crucial aspects in migration flow between two societies.

While there is no single standard definition of culture, it can be understood as symbols, practices, and beliefs that are collectively shared and agreed upon by a group of people (Swidler, 1986). It is often demonstrated in both tangible and intangible aspects such as cuisine, fashion, language, traditions, manners, and values. Most frequently used in the field of international business, the term “cultural distance” refers to the degree of similarities and differences between two cultures (Sousa & Bradley, 2008). In their study, Sousa and Bradley (2008) noted that the larger the cultural distance between the home and foreign country, the greater the uncertainty and risk of misunderstandings. For example, firms are more likely to expand and move to countries similar to the original country due to perceived lower information-acquisition and integration costs (Sousa & Bradley, 2008). In particular, similar cultures across countries may avoid the ambiguous standards for judging corporate performance caused by large cultural differences (Sousa & Bradley, 2008). Thus, it suggests that a small cultural distance between two countries may increase the predictability of expected and accepted attitudes and behaviors, thereby maximizing not only economic benefits but also facilitating smoother social and cultural interactions.

The cultural distance between two countries can be observed and measured based on various cultural elements. While some studies measure the asymmetric similarity between countries based on aspects like food and drink preferences and found a positive relationship with migration flows (Coimbra Vieira et al., 2024; Vieira et al., 2022), our study focuses on language, historical link, and values, which remain the most prominent and classic indicators of cultural similarity proxies in the literature (Belot & Ederveen, 2012; Coimbra Vieira et al., 2024; White & Buehler, 2018). These cultural elements collectively address the communicative, historical, and attitudinal aspects that are highly relevant to their migration destination choices, and may be associated with migration flows beyond economic benefits, social ties, and political satisfaction.

Language is one of the most fundamental cultural markers in migration flows between countries. The role of language is crucial in migration-decision processes due to its expected effects on migrants’ higher likelihood of socio-economic success, perceived lower psychological and skill costs, and faster language acquisition for integration into host societies (Adserà et al., 2026). Regarding the higher likelihood for socio-economic success, research shows that language is not only a tool for communication but also a key indicator of how convenient it is for migrants to transfer their human capital to the host societies and increase the chance of success in the new labor market (Adserà & Pytliková, 2015; Belot & Hatton, 2012). A common language also reduces the psychological and skill costs of migration. For instance, English is the most widely spoken and learned second language globally. Given the familiarity of English pre-immigration at school and the consumption of English-speaking media, migrants may find moving to English-speaking destinations more appealing and consider moving to English-speaking countries as a pull factor, as it reduces the cost for skill transferability and risk of having significant language barriers (Adserà & Pytliková, 2015; Belot & Ederveen, 2012). In addition to having a common language, languages that are linguistically close to each other also facilitate faster learning and make it easier for migrants to acquire the dominant language in host societies (Chiswick & Miller, 2005; Isphording & Otten, 2011). For example, a native Spanish speaker may find it much easier than a native Korean speaker to understand Italian without prior learning of the language (Chiswick & Miller, 2005). Overall, it suggests that speaking the same language and having a smaller linguistic distance between countries may be conducive to better economic opportunities, lower the linguistic adaptation costs, and accelerate the integration process for migrants due to faster language acquisition, thereby shaping migration flows.

Hypothesis 1: Countries with the same official language are more likely to have a higher migration flow.

Hypothesis 2: Countries with a high linguistic similarity are more likely to have a higher migration flow.

Second, having a historical link may increase similarities between countries that shape migration flows. A shared historical link is often associated with past colonial relationships. Colonial governments commonly implemented their own practices and established similar types of institutions during their rule (Aleksandrov, 2022). Even though most former colonies have either gained independence or are no longer ruled by ex-colonizers, countries with past colonial relationships may demonstrate similarities through common official languages and comparable social and legal systems as part of the colonial legacy. For instance, the high number of countries with English as an official language is an evident colonial legacy of the British invasion in different parts of the world in the past centuries. Other than languages, the colonial governments may introduce systems and infrastructures that are still adopted in contemporary societies. For instance, despite its return to China in 1997, Hong Kong continues to uphold the common law framework that recognizes the rule of law as a foundational principle in Hong Kong society. Similarly, Aguzzoli et al. (2024) studied Brazilians who moved to Portugal and noted that colonial history facilitates contextual similarities between the home and host countries, such as common language and institutional settings, and that they are influential to migrants’ adjustment in the host country. Riley and Emigh (2002) also found that Eritrean, a former Italian colony, showed a higher migration flow than Somalia to Italy, given that the Eritreanians felt a better sense of familiarity with the Italian administrative structure and interaction, and a closer religious tie compared to how Somalians felt towards Italy. Thus, given the similar structures and systems, people are more likely to have a higher sense of perceived closeness and lower costs for adaptation, which may result in higher migration flows to countries with past colonial ties.

Hypothesis 3: Countries with a shared colonial history are more likely to have a higher migration flow.

Third, cultural values are internalized and reflected through people’s behaviors and beliefs, which may influence their social experiences in the host societies, thereby potentially shaping migrants’ migration destination choices and the overall migration flow. Scholars have attempted to disentangle culture in different dimensions. For instance, according to Hofstede (1980, 2011), culture can be classified into six dimensions that influence social values, behaviors, and institutional practices. The six dimensions are: (1) High versus Low Power Distance (PDI), (2) Individualism versus Collectivism (IDV), (3) Masculinity versus Femininity (MAS), (4) Uncertainty Avoidance (UAI), (5) Long-term versus Short-term Orientation (LTO), and (6) Indulgence versus Restraint (IVR). Studies have shown that high cultural similarity increases migration flows, where power distance, individualism, uncertainty avoidance, and perceived gender roles demonstrate the most significant effects (Mihai & Novo-Corti, 2020, 2022; White & Buehler, 2018).

Building on Hofstede’s dimensions and evidence from various studies showing the conceptual and empirical overlap between them and other cultural frameworks (Kaasa, 2021), our discussion narrows the focus on values that reflect the countries’ levels of traditionalness and priorities on autonomy, tolerance, and subjective well-being. These values are captured in the two dimensions in the Inglehart-Welzel Cultural Map: (1) Survival-Self-Expression (SSE), and (2) Traditional-Secular-Rational (TSR) values. The two-dimensional values are viewed as constantly changing (Beugelsdijk & Welzel, 2018). The cultural map posits that, under modernization, societies shift from being economic and security-oriented to prioritizing more on post-material and emancipative matters, such as equality, environmentalism, and individual rights. This value shift parallels Maslow’s hierarchy of needs, which suggests that individuals satisfy their physiological and safety needs before pursuing self-actualization (Maslow, 1943). Similarly, the cultural map predicts societies to change from being traditional and valuing authority to becoming more open and tolerant to diversities, such as alternative family structures and abortion. Given the unique historical developments of countries and their similar and distinct positions with one another on the map, the two-dimensional values are useful in examining whether the migration flows are higher between countries that are positioned nearby, and if migration patterns mirror the expected trend of societies, where people move from survival-oriented and traditional societies towards liberal and secular destinations.

There are two key rationales for including these two value dimensions in our study on migration flows. First, compared to other cultural value dimensions, the Survival-Self-Expression (SSE) and Traditional-Secular-Rational (TSR) values cover a broader and greater breadth in reflecting the overarching cultural ideals worldwide. By acting as a foundation of the diverse cultural values demonstrated in other frameworks (Kaasa, 2021), the two value dimensions provide a more encompassing orientation for understanding the cultural standing of different countries and how the value distance between two countries relates to their migration flows. Second, the TSR value dimension captures the countries’ levels of importance in religion, which is also a significant cultural element that greatly reflects a culture’s values and standards. Religious beliefs shape people’s behavioral practices and expressions, such as food preferences (Owais et al., 2025), clothing styles (Siraj, 2011), and gender expectations (Chan & Di, 2024). In the context of migration, the distinct attitudes toward religion across countries may influence migrants’ adaptability and integration in the host societies (Drouhot & Nee, 2019). Hence, including the Survival-Self-Expression (SSE) and Traditional-Secular-Rational (TSR) value dimensions in our study allows a comprehensive examination of the relationship between diverse aspects of cultural values, including the importance of religion, and migration flows.

Based on previous research, the Survival-Self-Expression (SSE) and Traditional-Secular-Rational (TSR) value dimensions are highly relevant to migration flows and migrants’ levels of integration in the destination countries. Vieira et al. (2022) and Lanati and Venturini (2021) found a positive relationship between cultural value similarity and bilateral migration flows, particularly in countries upholding self-expression values. Scholars also used the two dimensions to examine immigrants’ integration in the host societies and found that a smaller distance in the survival-self-expression dimension between the origin and destination countries enhances integration (Maehler & Daikeler, 2024). Similarly, Qvist and Qvist (2025) used the dimensions to measure the proximity between Denmark, a country with high scores for self-expression and secular-rational values, and the immigrants’ origins, and found that countries with higher scores in survival-self-expression values predict higher interethnic unions with native Danes compared to countries with lower scores in this dimension, such as the Muslim communities. Overall, it suggests cultural similarity in values lowers the adjustment costs, which in turn may facilitate social integration and act as a pull-factor for migration decisions that drive higher migration flows towards culturally comparable places.

Hypothesis 4: Countries with similar cultural values are more likely to have a higher migration flow.

In short, we suggest that the understanding of migration should go beyond the conventional focus on economic, social, and political factors. Cultural similarities or aspirations, including language, colonial ties, and values, should also be considered important factors in understanding migration patterns.

This study utilized available data from various sources to investigate the relationship between cultural similarity and migration flow. The selection of countries was primarily based on data from the Migrant Integration Policy Index (MIPEX). The index includes 56 countries and has the fewest number of available countries compared to other data sets used in this study. However, the countries included in the analysis were limited due to the availability of data of other data sets used in the study, including Abel and Cohen’s (2022) bilateral international migration flow estimates, the Centre d'Études Prospectives et d'Informations Internationales (CEPII), the World Values Survey (WVS) wave 7 (2017–2022), the United Nations (UN), and the World Bank. In addition, we did not include countries lacking up-to-date data for all variables used in the analysis. Subsequently, a total of 45 countries were chosen for analysis. The countries are Albania, Argentina, Australia, Austria, Bulgaria, Brazil, Canada, Switzerland, Chile, China, Cyprus, Czechia, Germany, Denmark, Estonia, Spain, Finland, France, the United Kingdom, Greece, Croatia, Hungary, Indonesia, India, Iceland, Italy, Jordan, Japan, South Korea, Lithuania, Latvia, Mexico, Netherlands, Norway, New Zealand, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Sweden, Turkey, Ukraine, and the United States. The study includes 1,980 observations (45 x 44). The percentage of migration among these countries in total global migration is 62%, which illustrates the high representativeness of the data in this analysis (United Nations Department of Economic and Social Affairs, Population Division, 2019). The study has excluded 11 European, African, and Middle Eastern countries from the MIPEX country list, namely Belgium, Ireland, Israel, Luxembourg, Malta, Moldova, North Macedonia, Saudi Arabia, Serbia, South Africa, and the United Arab Emirates.

The dependent variable is the bilateral international migration flow estimates among the 45 countries. We used Abel and Cohen’s (2022) data, which provides five-year cumulative origin-destination flow estimates between 200 countries from 1990 to 2020. This study employs the estimates from the 2015–2020 period, where they are derived from the UN’s International Migrant Stock 2020 revision data set and World Population Prospects data set. The 2015–2020 estimates also align with the pre- and early COVID period as well as the Russian invasion in Ukraine, which is effective in capturing the role of cultural similarities in migration flows before significant disruptions and impacts from the pandemic and the war, respectively. The flows are produced by Abel and Cohen (2022) using six estimation methods, such as stock differencing and the pseudo-Bayesian approach of demographic accounting. The advantages of using their data in this study include its high coverage of countries in bilateral migration flows, the filled gap of missing official flow data, and the aligned timing of the cultural similarity measures, such as the Inglehart-Welzel cultural dimension scores in wave 7 (2017–2022), and the migrant stock data from the UN 2019 revision for the variable of migrant population.

We include 5 sets of variables in our analysis to measure culturally related predictors. The datasets we used are from the CEPII (Mayer & Zignago, 2011; Melitz & Toubal, 2014) and the Inglehart-Welzel Cultural Map (Haerpfer et al., 2022; World Values Survey, 2023). The cultural variables are categorized into three main aspects: language, history, and values. The advantages of using the CEPII data sets include their extensive coverage of countries, more accurate calculations of the geographical distances between countries, and a comprehensive bilateral data set for cultural and historical dummies. As for the dimensional scores, they were estimated by Inglehart and Welzel based on factor analysis on large-scale survey data and were extensively cited in academic research, which ensures the credibility of the data source.

For measuring cultural proximity, we included two variables to measure different aspects of language usage in the country. Both the data of the language variables were obtained from the CEPII language data set, which includes bilateral data of Common Official Language and Linguistic Proximity (tree-based) from across 195 countries (Melitz & Toubal, 2014). The first key independent variable is the common official language. It is binary categorical data, where 0 means no common official language, and 1 means there is at least one.

Although the two languages may be different, they can be closely related, making it easier for immigrants to learn the new language. The second key independent variable is linguistic proximity (tree-based). It is an adjusted measure of linguistic similarity that is based on the genealogical structure of language trees from Ethnologue, which focuses on the structural relationships between words and phrases (Fearon, 2003; Laitin, 2000). It is a continuous variable with four key values scaling from 0 to 0.75: 0 for two languages belonging to entirely different family trees; 0.25 for two languages belonging to separate branches of the same family tree (such as English and French); 0.5 for two languages belonging to the same branch (English and German); and 0.75 for two languages belonging to the same sub-branch (German and Dutch).

Our analysis also includes the possible colonial linkage of the two countries. The variable of colonial history between countries is based on the CEPII GeoDist data set, which includes bilateral distances across 225 countries in terms of geography, language, landlocked status, and colonial link (Mayer & Zignago, 2011). For this variable, it is binary categorical data where 0 means no colonial relationship and 1 means there was one.

Apart from language and history, the values of countries include variables drawn from the Inglehart-Welzel Cultural Map (World Values Survey, 2023). It measures scores across 111 countries based on two main cultural dimensions: (1) survival values versus self-expression values; and (2) traditional values versus secular-rational values. In the first dimension, lower or negative scores indicate a more economically oriented society, whereas higher or positive scores suggest a more emancipative society. In the second dimension, lower scores represent societies that are more religious. In comparison, higher scores reflect societies that are more secular-oriented. By examining cultural values between countries in relation to migration flows, this study aims to understand whether similar cultural beliefs, norms, and standards are associated with destination choices among migrants.

To measure culture of the society, we include two variables related to values. The Survival-Self-Expression (SSE) value difference is used to measure the distance between the origin and destination. It is a continuous variable where the value is calculated as an absolute positive. The value difference of this value dimension between the two countries ranges from 0 to 4.3. The other variable related to value is the Traditional-Secular-Rational (TSR) value difference between the origin and destination. It is a continuous variable where the value is also calculated as an absolute positive. The Traditional-Secular-Rational (TSR) value difference between the two countries ranges from 0 to 3.19.

Considering the cost and benefit framework in adapting to the destination countries, we expect that countries with a common official language and a high linguistic proximity, a colonial history, and small Survival-Self-Expression (SSE) and Traditional-Secular-Rational (TSR) value differences are related to higher migration flows.

The control variables are the economic, social, and political factors that have traditionally been studied by scholars as key drivers of migration. They include population, GDP per capita, geographical distance, migrant population, and the integration policy index.

For population, we obtained the data from the United Nations Department of Economic and Social Affairs, Population Division (2024), and calculated the average of each country from 2015 to 2020. For GDP per capita, the data was collected from the World Bank (n.d.) by calculating the average from 2015 to 2020. As for geographical distance, the data is based on the CEPII GeoDist dataset (Mayer & Zignago, 2011), where the bilateral distances were calculated as a population-weighted generalized mean of the distances between the 25 largest cities in a country, where each inter-city distance is weighted by the city’s share of the country’s total population. Regarding the migrant population, it is considered a social factor of migration. Foreign-born migrants living in destination countries act as social networks for potential emigrants in their origin countries, facilitating information exchange and social support. In this study, the 2019 revision of migrant stock data from the UN is used. Finally, for the integration policy index, migration flows are significantly shaped by political factors like integration policies. This study uses data from MIPEX (Solano & Huddleston, 2020), a credible tool widely used in research centres for examining the effectiveness of governmental policies on migrant integration in the host societies. The 2020 overall scores (ranging from 0 to 100) of the destination countries, where the data were collected in 2019, were used. The overall scores cover 8 policy areas, including labor market mobility, education, political participation, access to nationality, family reunion, health, permanent residence, and anti-discrimination. Both the correlation matrix analysis and the VIF test were conducted for the aforementioned variables. No significant multicollinearity issues (mean VIF = 1.74) were found among them.

In the analysis, several covariates were added. First, the covariate of lexical-based linguistic proximity, which is also based on the CEPII language data set, is included in our analysis as a robustness check for the tree-based linguistic proximity variable. The difference between tree-based and lexical-based is that the former reflects deeper linguistic ties and structures, whereas the latter focuses more on surface-level word overlaps. Thus, while the tree-based proximity captures enduring divergences rather than word overlaps, the lexical-based proximity is included to assess the effect of word similarities in migration flows, which provides a more comprehensive understanding of linguistic similarity and its relationship with migration flows. Second, the origin and destination countries’ Survival-Self-Expression (SSE) and Traditional-Secular-Rational (TSR) values were also added as covariates to examine the push-pull relationship between countries in migration flows based on cultural values.

This study uses the gravity model as the theoretical framework. The model posits that migration between origin and destination countries is proportional to their population and economic sizes, given the push-pull relationship, and inversely related to geographical distance (Beine et al., 2016). For population, a larger population in the origin countries is more likely to have more available potential emigrants that “push” them out, and a larger population in the destination countries suggests a more extensive network and opportunities that “pull” migrants in. Similarly, for GDP per capita, a low GDP per capita in the origin countries “pushes” migrants out to destinations with a higher GDP per capita in search of a better economic environment, such as higher wages and more job opportunities. As for geographical distance, a smaller distance between the origin and destination countries results in a larger migration flow, given the lower time and financial costs of travelling between countries. By extending the use of the gravity model of migration with linguistic characteristics and cultural values, this study aims to investigate whether small cultural distances act as a low-cost for adaptation, hence associated with higher migration flows.

Under the gravity framework, this study uses the Poisson Pseudo-Maximum Likelihood with High-Dimensional Fixed Effects (PPMLHDFE) for analysis. This method is chosen given its prevalence in gravity models, its ability to absorb origin and destination fixed effects, and its handling of zeros in migration flows and heteroskedasticity (Correia et al., 2020; Silva & Tenreyro, 2006).

We begin by reporting the descriptive statistics for the selected variables across the 45 countries, as shown in Table 1. Among the 1,980 observations, the bilateral international migration flow from 2015 to 2020 ranged from 0 to over 2.5 million. Most country pairs do not have a common official language, with an average close to 0. Similarly, most pairs also lack close tree-based linguistic proximity and colonial history. Regarding cultural values, most country pairs have a small Survival-Self-Expression (SSE) value difference, with a mean around 1.52 out of 4.33. The TSR value difference is also small, averaging approximately 0.77 out of 3.19. It suggests that most country pairs are culturally distinct in terms of language and history, but relatively similar in terms of values.

We employed PPMLHDFE regression to analyze the relationship between the independent and control variables and migration flows, as shown in Table 2. In the main analysis, five models were analyzed. Model 1 is the baseline gravity variables that include population, GDP per capita, and geographical distance. Model 2 adds the migrant population and the integration policy index to the gravity variables. Model 3 adds the language variables along with the control variables. Model 4 further adds the history variable. Finally, Model 5 is the full model that adds the value difference variables on top of the rest of the variables. In the robustness checks, two models were analyzed. Model 6 is the same as Model 5, keeping the Survival-Self-Expression (SSE) and Traditional-Secular-Rational (TSR) value difference variables, but replacing the tree-based linguistic proximity variable with lexical-based to provide robustness of the effect of linguistic proximity. Model 7 also builds on Model 5, keeping the tree-based linguistic proximity variable, but replacing the value difference variables with the raw Survival-Self-Expression (SSE) and Traditional-Secular-Rational (TSR) values of the origin and destination countries to assess the push-pull relationship in migration flows based on cultural values. Both the correlation matrix analysis and the VIF test were conducted for the above variables. No significant multicollinearity issues for Models 1 to 5 (mean VIF = 1.59), Model 6 (mean VIF = 1.58), and Model 7 (mean VIF = 2.45) were found.

We observe that population and geographical distance are significantly related to higher migration flows in all seven models. Beyond the gravity variables, the results also show that the migrant population in destination countries is positively associated with migration flows, indicating a significant relationship between social networks and higher migration flows. However, although we expected the integration policy indexes to be associated with migration flows, our results show no significant relationship.

The results reported in Table 2 show support for our hypotheses that a common official language and high linguistic proximity are positively related to migration flows. We observe that colonial links have no significant association with migration flows in the main analysis. Regarding values, our results revealed a positive relationship between the Survival-Self-Expression (SSE) value difference and migration flows, while the Traditional-Secular-Rational (TSR) value difference shows no significance. In other words, more distinct, rather than similar, Survival-Self-Expression (SSE) values between countries are associated with higher migration flows. In our robustness checks presented in Table 3, common official language and both high tree-based and lexical-based linguistic proximity are consistently positively related to migration flows. In addition, the relationship between colonial link and migration flows remains insignificant, which is consistent with the main analysis. Lastly, regarding values, our robustness results reveal that lower self-expression values in the destination country are associated with higher migration flows, indicating that migration flows are directed more toward survival-oriented destinations rather than the expressive ones. Similarly, lower secular-rational values in the origin also correlate with higher migration flows, which suggests that emigration likely occurs from traditional societies rather than secular societies. Self-expression values in the origin and secular-rational values in the destination, however, show no significant effects. Overall, the analysis shows stark support for the first and second hypotheses on the same or similar languages and higher migration flows. As for the strong significance of population and the insignificant results of GDP per capita in all seven models, the finding suggests that migration flows in our analysis may be primarily driven by demographic reasons, such as overpopulation in the origin and opportunities in the destination, rather than people seeking higher wages. We also conducted regional analyses¹ to assess the relationship between cultural similarity and the bilateral migration flows in Asia-Pacific, Europe, and the Americas. Our analyses showed that a common official language is positively associated with migration flows in both the Asia-Pacific and Europe. Tree-based linguistic proximity also demonstrates a positive association with migration flows in Europe. Colonial link shows a negative or no relationship with migration flows in the three regions. Regarding values, the country pairs in Asia-Pacific show a negative relationship in the Traditional-Secular-Rational (TSR) value difference, which supports our fourth hypothesis regarding similar cultural values and higher migration flows.

In this article, we aim to examine the significance of cultural similarity as a cost-reduction strategy that may increase migration flows. Based on the findings from the gravity model, we find that smaller cultural distances between countries are associated with higher migration flows. Our analysis of cultural proximity considers countries’ languages, colonial links, and cultural values.

For language, our results show that migration flows tend to be higher between countries that share the same official language (Hypothesis 1 supported). Additionally, we find that countries with a close linguistic root to their native language are associated with higher migration flows (Hypothesis 2 supported). Regarding colonial links, our findings indicate that they do not correlate with increased migration flows (Hypothesis 3 not supported). Lastly, similar cultural values in specific dimensions are linked to higher migration flows (Hypothesis 4 supported).

These findings have three key findings that are worth further elaboration. First, among the cultural variables studied, language appears to be the most influential. Our results align with existing literature, which shows that a common language and similar linguistic roots are associated with higher migration flows (Adserà & Pytliková, 2015; Belot & Ederveen, 2012; Chiswick & Miller, 2005). This can be explained by the perceived closeness and lower adaptation costs for migrants, particularly regarding language comprehension in institutional, legal, and social settings. Consequently, a shared official language may motivate migration to culturally similar destinations, contributing to increased flows. Furthermore, sharing a broader linguistic family and structure, along with overlapping vocabularies, strongly correlates with migration, especially among European countries, which tend to have closer language backgrounds compared to regions like Asia-Pacific and the Americas.

Second, our analysis of colonial links reveals no significant association with higher migration flows, which contradicts some previous studies (Aguzzoli et al., 2024). Two explanations are possible. First, the historical context of colonialism, primarily involving European colonizers and cross-continental expansion, may explain this null result in both our main and regional analyses. Second, anti-colonial sentiments, especially during and after World War II, may hinder migration from former colonies. Negative perceptions of colonial history could override feelings of familiarity with colonial systems, thereby reducing migration flows. For instance, the negative relationship between colonial links and migration flows in the Asia-Pacific region could reflect unfavorable perceptions of Japanese colonization among Koreans.

Third, the findings indicate that similarity in specific cultural values is associated with higher migration flows, consistent with prior research (Lanati & Venturini, 2021; Mihai & Novo-Corti, 2022). Notably, the difference in traditionalism and secularism (the TSR dimension) is particularly significant in the Asia-Pacific region (reported in regional analysis upon request), highlighting increased migration between culturally comparable countries. However, migration flows increase among countries with large differences in survival-self-expression values (the SSE dimension), suggesting the flexibility of migrating to economically oriented or liberal-minded societies. Nonetheless, consistent with prior studies (White & Buehler, 2018; Yeganeh, 2024), we observe that individuals from traditional, religious, and hierarchical societies are more inclined to migrate. Societies emphasizing authority or obedience may exert push factors, prompting migration. Additionally, migration flows toward societies that value material security may reflect our selected countries’ priority on economically oriented cultural practices.

Our study has limitations. Although we aimed to include as many countries as possible, African and Middle Eastern countries were excluded, potentially overlooking the effects of political refugees, especially to Europe since the 2010s, and their influence on cultural similarity measures like colonial links. Future research should incorporate these regions to better understand the role of cultural similarity in global migration flows. Additionally, the uneven number of countries across regions—over 30 in Europe but fewer than 10 in Asia-Pacific and the Americas—may lead to underestimations of regional effects. Future studies could adopt a more balanced regional sample to refine these insights.

This research advances the migration literature by emphasizing the importance of cultural factors in migration decisions, moving beyond traditional economic, social, and political explanations. By integrating language, history, and values as cultural predictors, we highlight language—particularly shared official languages and linguistic roots—as the most influential element in shaping migration flows. As migration drivers extend beyond economic, social, and political considerations, this study takes a significant step in emphasizing the role of cultural factors in understanding global migration patterns.

Notes

REFERENCES

• Abel, G. J., & Cohen, J. E. (2022). Bilateral international migration flow estimates updated and refined by sex. Scientific Data, 9(1), 173. https://doi.org/10.1038/s41597-022-01271-z
  Article

  PubMed

  PMC
• Adserà, A., & Pytliková, M. (2015). The role of language in shaping international migration. The Economic Journal, 125(586), F49–F81. https://doi.org/10.1111/ecoj.12231
  Article

  PubMed

  PMC
• Adserà, A., Pytliková, M., & Rapoport, H. (2026). Language and migration. In S. Weber & V. Ginsburgh (Eds.), The Palgrave Handbook of Economics and Language (pp. 299–342). Palgrave Macmillan.
• Aguzzoli, R., Śliwa, M., Lengler, J., Brewster, C., & Quatrin, D. R. (2024). How does colonial history matter for expatriate adjustment? The case of Brazilians in Portugal. Journal of International Business Studies, Advance online publication. https://doi.org/10.1057/s41267-024-00754-y
  Article
• Aleksandrov, G. V. (2022). A civil body politick: Governance, community, and accountability in early New England. Frontiers in Political Science, 4, 804673. https://doi.org/10.3389/fpos.2022.804673
  Article
• BBC StoryWorks (n.d.). It all started with anime: Moving to Tokyo and why we stayed. BBC. https://www.bbc.com/storyworks/travel/experience-tokyo/it-all-started-with-anime-moving-to-tokyo-and-why-we-stayed
• Beine, M., Bertoli, S., & Fernández-Huertas Moraga, J. (2016). A practitioners’ guide to gravity models of international migration. World Economy, 39(4), 496–512. https://doi.org/10.1111/twec.12265
  Article
• Belot, M., & Ederveen, S. (2012). Cultural barriers in migration between OECD countries. Journal of Population Economics, 25(3), 1077–1105. https://doi.org/10.1007/s00148-011-0356-x
  Article
• Belot, M. V. K., & Hatton, T. J. (2012). Immigrant selection in the OECD. The Scandinavian Journal of Economics, 114(4), 1105–1128. https://doi.org/10.1111/j.1467-9442.2012.01721.x
  Article
• Beugelsdijk, S., & Welzel, C. (2018). Dimensions and dynamics of national culture: Synthesizing Hofstede with Inglehart. Journal of Cross-Cultural Psychology, 49(10), 1469–1505. https://doi.org/10.1177/0022022118798505
  Article

  PubMed

  PMC
• Carling, J. (2024). Why do people migrate? Fresh takes on the foundational question of migration studies. International Migration Review, 58(4), 1757–1791. https://doi.org/10.1177/01979183241269445
  Article

  PubMed

  PMC
• Chan, A. K. W., Cheung, L. T., Chong, E. K. M., Lee, M. Y. K., & Wong, M. Y. (2022). Hong Kong’s new wave of migration: Socio-political factors of individuals’ intention to emigrate. Comparative migration studies, 10(1), 49. https://doi.org/10.1186/s40878-022-00323-y
  Article

  PubMed

  PMC
• Chan, E., & Di, D. (2024). Faithfully gendered? How religious attendance shapes views of women’s leadership in politics and business cross-nationally. Socius, 10, 23780231241286377. https://doi.org/10.1177/23780231241286377
  Article
• Chiswick, B. R., & Miller, P. W. (2005). Linguistic distance: A quantitative measure of the distance between English and other languages. Journal of Multilingual and Multicultural Development, 26(1), 1–11. https://doi.org/10.1080/14790710508668395
  Article
• Coimbra Vieira, C., Lohmann, S., & Zagheni, E. (2024). The value of cultural similarity for predicting migration: Evidence from food and drink interests in digital trace data. Population and Development Review, 50(1), 149–176. https://doi.org/10.1111/padr.12607
  Article
• Correia, S., Guimarães, P., & Zylkin, T. (2020). Fast Poisson estimation with high-dimensional fixed effects. The Stata Journal, 20(1), 95–115. https://doi.org/ 10.1177/1536867X20909691
  Article
• de Haas, H. (2021). A theory of migration: The aspirations-capabilities framework. Comparative Migration Studies, 9(1), 8. https://doi.org/10.1186/s40878-020-00210-4
  Article

  PubMed

  PMC
• Drouhot, L. G., & Nee, V. (2019). Assimilation and the second generation in Europe and America: Blending and segregating social dynamics between immigrants and natives. Annual Review of Sociology, 45(1), 177–199. https://doi.org/10.1146/annurev-soc-073117-041335
  Article
• Etling, A., Backeberg, L., & Tholen, J. (2020). The political dimension of young people’s migration intentions: Evidence from the Arab Mediterranean region. Journal of Ethnic and Migration Studies, 46(7), 1388–1404. https://doi.org/10.1080/1369183x.2018.1485093
  Article
• European Commission (2025, December 11). Changes to citizenship requirements and research on migrant students and refugee integration in Germany. https://home-affairs.ec.europa.eu/news/changes-citizenship-requirements-and-research-migrant-students-and-refugee-integration-germany-2025-12-11_en
• Fearon, J. D. (2003). Ethnic and cultural diversity by country. Journal of Economic Growth, 8(2), 195–222. https://doi.org/10.1023/A:1024419522867
  Article
• Fong, E., Ni, X., & Man, P. K. (2025). Participation in social movement and emigration intention: A study of Hong Kong social movement in 2019. International Migration Review, https://doi.org/10.1177/01979183251316523
  Article
• GOV.UK (2026, March 5). Higher standard of English now required to settle in the UK. https://www.gov.uk/government/news/higher-standard-of-english-now-required-to-settle-in-the-uk
• Grohmann, T. (2023). Cultural similarity and migration: New evidence from a gravity model of international migration (IOS Working Papers No. 404). https://www.econstor.eu/handle/10419/281137
• Guo, M., Wang, Y., Liu, J., & Dong, X. (2023). Ethnic enclaves, social capital, and psychological well-being of immigrants: The case of Chinese older immigrants in Chicago. Aging & Mental Health, 27(6), 1077–1085. https://doi.org/10.1080/13607863.2022.2084506
  Article
• Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano, J., Lagos, M., Norris, P., Ponarin, E., & Puranen, B. (Eds.). (2022). World Values Survey: Round seven—country-pooled datafile, version 6.0. JD Systems Institute & WVSA Secretariat. https://doi.org/10.14281/18241.24
  Article
• Hofstede, G. (1980). Culture’s consequences: International differences in work-related values. SAGE Publications.
• Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1), 8. https://doi.org/10.9707/2307-0919.1014
  Article
• Isphording, I. E., & Otten, S. (2011). Linguistic distance and the language fluency of immigrants (Ruhr Economic Papers No. 274). Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
• Kaasa, A. (2021). Merging Hofstede, Schwartz, and Inglehart into a single system. Journal of Cross-Cultural Psychology, 52(4), 339–353. https://doi.org/10.1177/00220221211011244
  Article
• Kan, M.-Y., Loa, S. P., & Richards, L. (2025). Generational differences in local identities, participation in social movements, and migration intention among Hong Kong people. The American Behavioral Scientist, 69(3), 277–298. https://doi.org/10.1177/00027642231192023
  Article
• Kuhn, A. (2024, January 17). Affluent Chinese have been moving to Japan since the COVID lockdowns. NPR. https://www.npr.org/2024/01/17/1221849861/china-japan-immigration
• Laitin, D. D. (2000). What is a language community?. American Journal of Political Science, 44(1), 142–155. https://doi.org/10.2307/2669300
  Article
• Lam, K. W. K., & Fong, E. (2025). Time to leave: Emigration tendencies in Hong Kong after the 1997 Handover and the 2019 social movement. Population, Space and Place, 31(3), https://doi.org/10.1002/psp.70027
  Article

  PubMed

  PMC
• Lanati, M., & Venturini, A. (2021). Cultural change and the migration choice. Review of World Economics, 157(4), 799–852. https://doi.org/10.1007/s10290-021-00418-1
  Article
• Levitt, P. (1998). Social remittances: Migration driven local-Level forms of cultural diffusion. International Migration Review, 32(4), 926. https://doi.org/10.2307/2547666
  Article

  PubMed
• Maehler, D. B., & Daikeler, J. (2024). The cultural identity of first-generation adult immigrants: A meta-analysis. Self and Identity, 23(5-6), 450–483. https://doi.org/10.1080/15298868.2024.2399559
  Article
• Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370.
  Article
• Massey, D. S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. E. (1993). Theories of international migration: A review and appraisal. Population and Development Review, 19(3), 431–466. https://doi.org/10.2307/2938462
  Article
• Mayer, T., & Zignago, S. (2011). Notes on CEPII’s distances measures: The GeoDist database (CEPII Working Paper No. 2011-25). CEPII. https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=6
• Melitz, J., & Toubal, F. (2014). Native language, spoken language, translation and trade. Journal of International Economics, 93(2), 351–363. https://doi.org/10.1016/j.jinteco.2014.04.004
  Article
• Mihai, I., & Novo-Corti, I. (2020). Cultural distance and migration patterns in the EU: The Romanian case. European Research Studies. 23(3) 410.
  Article
• Mihai, I., & Novo-Corti, I. (2022). An exploratory analysis of the interactions between the determinants of migratory flows. Papers in Regional Science, 101(1), 163–182. https://doi.org/10.1111/pirs.12639
  Article
• Moser, B. (2023, August 13). I live in the Europe of my American dreams. The New York Times. https://www.nytimes.com/2023/08/13/opinion/american-expat-life-europe.html
• Owais, U., Patel, R., & Abbott, S. (2025). The influence of religiosity on food choice among British Muslims: A qualitative study. Nutrition and Health, 31(2), 749–756. 10.1177/02601060241244883.
  Article

  PubMed

  PDF
• Qvist, H.-P. Y., & Qvist, J. Y. (2025). Interethnic union formation among 1.5- and second-generation immigrants: The Role of cultural proximity. International Migration Review, 59(1), 468–501. https://doi.org/10.1177/01979183231177969
  Article
• Riley, D., & Emigh, R. J. (2002). Post-colonial journeys: Historical roots of immigration and integration. Comparative Sociology, 1(2), 169–191. https://doi.org/10.1163/156913302100418484
  Article
• Silva, J. M. C. S., & Tenreyro, S. (2006). The Log of Gravity. The Review of Economics and Statistics, 88(4), 641–658. https://doi.org/10.1162/rest.88.4.641
  Article
• Siraj, A. (2011). Meanings of modesty and the hijab amongst Muslim women in Glasgow, Scotland. Gender, Place & Culture, 18(6), 716–731. https://doi.org/10.1080/0966369X.2011.617907
  Article
• Solano, G., & Huddleston, T. (2020). Migrant Integration Policy Index 2020. Barcelona Center for International Affairs & Migration Policy Group. https://www.migpolgroup.com/wp-content/uploads/2021/01/Solano-Giacomo-Huddleston-Thomas-2020-Migrant-Integration-Policy-Index-2020.pdf
• Sousa, C. M. P., & Bradley, F. (2008). Cultural distance and psychic distance: Refinements in conceptualisation and measurement. Journal of Marketing Management, 24(5-6), 467–488. https://doi.org/10.1362/026725708X325959
  Article
• Swidler, A. (1986). Culture in action: Symbols and strategies. American Sociological Review, 51(2), 273–286. https://doi.org/10.2307/2095521
  Article
• The Yomiuri Shimbun (2025, December 31). Japanese language requirement eyed for permanent residency status; LDP plans revisions of laws on foreigners. The Japan News. https://japannews.yomiuri.co.jp/politics/politics-government/20251231-301652/
• United Nations Department of Economic and Social Affairs, Population Division (2019). International migrant stock 2019. https://www.un.org/development/desa/pd/content/international-migrant-stock
• United Nations Department of Economic and Social Affairs, Population Division (2024). World population prospects 2024. https://population.un.org/wpp/downloads?folder=Standard%20Projections&group=Population
• Vieira, C. C., Lohmann, S., Zagheni, E., Vaz de Melo, P. O. S., Benevenuto, F., & Ribeiro, F. N. (2022). The interplay of migration and cultural similarity between countries: Evidence from Facebook data on food and drink interests. PLOS One, 17(2), e0262947. https://doi.org/10.1371/journal.pone.0262947
  Article

  PubMed

  PMC
• Wang, Z., de Graaff, T., & Nijkamp, P. (2018). Barriers of culture, networks, and language in international migration: A review. Region: the journal of ERSA, 5(1), 73–89. https://doi.org/10.18335/region.v5i1.203
  Article
• White, R., & Buehler, D. (2018). A closer look at the determinants of international migration: Decomposing cultural distance. Applied Economics, 50(33), 3575–3595. https://doi.org/10.1080/00036846.2018.1430337
  Article
• World Bank (n.d.). GDP per capita (constant 2015 US$). https://data.worldbank.org/indicator/NY.GDP.PCAP.KD
• World Values Survey (2023, February 17). WVS Cultural Map: 2023 version released. https://www.worldvaluessurvey.org/WVSNewsShow.jsp?ID=467
• Yeganeh, H. (2024). Conceptualizing the patterns of change in cultural values: The paradoxical effects of modernization, demographics, and globalization. Social Sciences, 13(9), 439. https://doi.org/10.3390/socsci13090439
  Article

Citations to this article as recorded by