Introduction

Immigration is a hot-button issue, and researchers continue exploring why immigrants sometimes struggle economically compared to native-born citizens (Drouhot & Nee, 2019; Boccagni and Hondagneu‐Sotelo 2023; Nee and Alba 2013; Portes, 2020; Waters & Pineau, 2015; Zhou & Lee, 2007). Studies point to education and experience as key factors, but even with similar qualifications, gaps remain (Drouhot & Nee, 2019; Heath et al., 2008). This research explores whether cultural differences between immigrants’ home countries and the USA play a role.

The USA as an interesting target because it has a long history of immigration and continues to be a major destination for immigrants globally. This large and diverse immigrant population allows you to study a wide range of cultural distances. The USA has traditionally been seen as a “melting pot” where immigrants assimilate, but there is also a growing recognition of a “mosaic” where different cultures coexist. Immigrants play a vital role in the US workforce. Understanding their economic challenges can help create policies that maximize their contribution to the economy. Understanding immigrant-native disparities helps inform policies that promote immigrant integration and economic mobility. Large economic disparities can create social tensions. Studying them can help identify potential issues and develop strategies for a more inclusive society.

This study uses the American Community Survey (ACS), a massive survey from the US Census Bureau, to analyze socio-economic factors. The ACS provides rich data on immigrant life, including income, education, and language skills. By examining these factors, we aim to contribute new knowledge about immigrant economic integration in the USA. This is the first major study to use cultural distance as a lens to explain economic differences between immigrants and natives. Prior research overwhelmingly relies on one indicator of socioeconomic status, usually, income (Chetty et al., 2020; Restifo et al., 2023). Here, we explore three key indicators: income (wealth flow), homeownership (middle-class marker), and poverty status (low-income marker). Our focus on cultural distance is valuable because it goes beyond factors like education that immigrants may bring with them. Cultural differences can affect job opportunities, social networks, and even how people navigate bureaucracies, all impacting economic outcomes. By analyzing economic disparities through the lens of cultural distance, this study can offer valuable insights for policymakers, social programs, and immigrant communities themselves.

Literature

The integration of immigrants into host societies has been extensively explored in interdisciplinary literature within social science research (Drouhot & Nee, 2019; Portes & Rumbaut, 2001; Waters & Pineau, 2015). This body of research consistently demonstrates that immigrants tend to have lower socioeconomic status compared to native-born individuals across different countries (Drouhot & Nee, 2019; Heath et al., 2008; Schaeffer & Kas, 2023). This socioeconomic disparity is largely attributed to differences in educational attainment and work experience between immigrants and natives (Ferrer & Riddell, 2008; Hall & Farkas, 2008), as emphasized by human capital theory (Chiswick & Miller, 2009; Hall & Farkas, 2008). According to this theory, the skills acquired through education and experience are crucial for successful labor market integration (Brixy & Hessels, 2010; Chiswick & Miller, 2009, 2012; Cohrs & Stelzl, 2010), with immigrants possessing higher human capital expected to fare better in the job market. However, despite these expectations, immigrants often struggle to secure employment matching their skill levels (Hall & Farkas, 2008; Hagan et al. 2020; Lu & Hou, 2020), mainly due to the non-transferability of their skills to the host country (Chiswick & Miller, 2009; Lu & Hou, 2020).

Furthermore, certain immigrant groups may possess comparative advantages over others, such as proficiency in the host country’s language, which facilitates access to higher-status jobs (Chiswick & Miller, 2009; Hwang et al., 2010; Van Tubergen et al., 2004). Thus, disparities in labor market outcomes among immigrants cannot be solely explained by human capital theory (Abramitzky & Boustan, 2017; Portes, 2020; Restifo et al., 2023; White & Buehler, 2018).

Regarding the receiving context, immigrant groups are often ranked based on their perceived favorability (Alba & Nee, 2003; Massey & Pren, 2012; Portes, 2020). The “context of reception” perspective highlights how certain immigrant groups face discrimination while others are more welcomed in destination countries (Chetty et al., 2020; Schaeffer & Kas, 2023). This discrimination extends beyond economic opportunities and is influenced by factors such as racial hierarchy in the USA, where immigrants racially classified as Black may experience different economic trajectories than non-Black immigrants (Massey & Pren, 2012; Restifo et al., 2023). Essentially, systematic racism towards perceived “inferior” immigrant groups perpetuates unequal treatment (Alba & Nee, 2003; Favell, 2022; Massey & Pren, 2012; Portes, 2020), with cultural differences further complicating immigrant adaptation to the host culture (Dustmann et al., 2010; Ebner & Helbling, 2016; Valentino et al., 2019; White & Buehler, 2018).

Additionally, immigrant groups facing higher risk of discrimination often originate from countries more culturally distant from the destination countries (Coenders et al., 2008; Cohrs & Stelzl, 2010; Portes & Rumbaut, 2001). This cultural distance affects immigrant economic adaptation, as host societies tend to favor groups culturally similar to the dominant group (Alba & Nee, 2003; Feliciano & Lanuza, 2017; Restifo et al., 2023). While discrimination remains a significant barrier to immigrant integration, the interplay between origin and destination contexts, particularly cultural distance, also plays a crucial role in shaping immigrants’ economic outcomes (Coenders et al., 2008; Cohrs & Stelzl, 2010; Portes & Rumbaut, 2001).

Present Study

The present investigation employs the cultural distance hypothesis (Alesina & La Ferrara, 2005; Bisin & Verdier, 2017; Chiswick & Miller, 2005, 2009) to elucidate economic disparities between immigrants and the native population in the USA. This hypothesis suggests that greater cultural disparities between immigrants’ home countries and the host country result in more significant economic discrepancies. It posits that immigrants from cultures more akin to the receiving society find it easier to integrate into the labor market, access resources, and navigate social institutions. Conversely, immigrants from markedly different cultures may encounter challenges such as language barriers, divergent cultural norms, and institutional disparities, hindering their economic progress.

This study has the potential to make several key contributions to the existing literature on immigrant integration and economic outcomes. First, it moves beyond the traditional factors of education and work experience, incorporating the concept of cultural distance as a significant influence on economic disparities. Second, by analyzing how cultural distance interacts with socio-economic indicators like income, homeownership, and poverty, this research can provide a richer picture of the challenges faced by immigrants. Finally, our findings can inform policymakers on how cultural integration programs and policies can be tailored to address the specific challenges faced by immigrants from various cultural backgrounds.

While existing research explores various factors impacting immigrant integration, incorporating cultural distance as a central theme is a fresh perspective. Analyzing not just income, but also homeownership and poverty status, provides a more comprehensive view of economic disparities. Focusing on the USA, a major receiving country with readily available data, allows for in-depth analysis with real-world implications. By combining these elements, this study can offer a unique contribution to the field and provide valuable insights for future research.

This study extends existing literature on the cultural distance hypothesis by taking a holistic approach to national culture, viewing it not as a collection of isolated elements but as a multifaceted phenomenon resisting reduction to a single factor. This holistic approach necessitates a method for comparing national cultures, such as the one previously employed by studies on cultural differences between global regions (Cuypers et al., 2018; Yeganeh, 2011). The introduction of the Kogut and Singh (1988) index of cultural distance catalyzed cross-cultural research and its application in business contexts. This index aggregates cultural disparities using Hofstede’s (2001) cultural dimensions theory, which delineates national cultures across various dimensions including power distance, uncertainty avoidance, individualism-collectivism, and masculinity-femininity. Later, Hofstede and Minkov (2010) expanded this framework to include long-term vs. short-term orientation and indulgence vs. restraint.

In recent years, scholars have become increasingly critical of Kogut and Singh’s (1988) cultural distance index and of Hofstede’s (1980) underlying national culture framework. Specifically, a recent study by Konara and Mohr (2019) questions the applicability of the Kogut and Singh index within the field of international business and cross-cultural management. There are also empirical studies (e.g., Drogendijk & Slangen, 2006) that show the validity of the Kogut and Singh (1988) index. Indeed, the Kogut and Singh index has been widely cited and used in academic research, indicating its credibility and reliability within the field. We concur with Drogendijk and Slangen (2006) that the Kogut and Singh (1988) index is a comprehensive measure of cultural distance because it considers multiple dimensions of culture, including language, religion, social norms, and education, providing a relatively comprehensive view of cultural differences between countries. Nevertheless, we also recognize that the index may not capture all relevant aspects that could influence economic outcomes, such as cultural values, beliefs, and practices. Therefore, we used two approaches to calculate cultural distance—the Euclidean index of cultural distance championed by Konara and Mohr (2019) and the Kogut and Singh index. The Euclidean index of cultural distance provides a straightforward measure of cultural distance in terms of Euclidean distance units, which may be easier to interpret intuitively compared to other indices that rely on complex calculations or weighted averages (for a more detailed description of the Euclidean index, see Konara & Mohr, 2019). However, our calculations have shown that cultural distances calculated by the Euclidean method and by the Kogut and Singh method differ only by a percentage point or two. We have also conducted multivariate analyses (not shown) using the Euclidean indices of cultural distance. The results were identical to the ones presented in this paper.

An alternative cross-cultural theory proposed by Inglehart and Wenzel (2005) suggests that global socioeconomic development yields two primary cross-cultural dimensions: traditional vs. secular-rational and survival vs. self-expression. The first dimension reflects the contrast between societies where religion holds significant social sway and those where it does not. Traditional societies prioritize religious authority and traditional family values, often exhibiting high fertility rates and negative views on issues like divorce and euthanasia. In contrast, secular-rational societies adhere to rational-legal norms and emphasize economic accumulation and individual achievement. The survival vs. self-expression dimension highlights differences in societal concerns, with survival-oriented societies emphasizing hard work and security, while self-expression-oriented societies prioritize cultural diversity and tolerance for alternative lifestyles.

Table 1 serves as a valuable reference for analyzing and comparing cultural distances between the USA and various countries. It presents cultural distance values for each country relative to the USA, calculated using both Inglehart-Wenzel (IW) and Hofstede (H) approaches. Notably, many Western European countries and countries sharing cultural heritage with the USA exhibit low cultural distances, while countries in the Near and Middle East show high cultural distances.

Table 1 Cultural distance between countries of origin and the USA
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The primary aim of this article is to apply cross-cultural theories to the study of immigrant economic adaptation. Cultural distance between origin and destination serves as the main predictor of economic indicators such as income, homeownership, and poverty status. This focus on cultural distance complements traditional predictors like education, acknowledging its impact on job opportunities, social networks, and bureaucratic navigation.

The study anticipates a negative association between cultural distance and immigrant economic outcomes based on existing evidence. The cultural distance predictor is calculated using both Hofstede’s and Inglehart and Welzel’s frameworks, which conceptualize national culture from different perspectives. Additionally, the study examines predictors such as English proficiency, educational attainment, and community disadvantage, expecting positive associations between English proficiency and education with socioeconomic status, and considering community disadvantage as a significant, yet often overlooked, factor in local economic conditions impacting immigrant economic outcomes.

Data and Method

In order to investigate potential differences between natives and immigrants regarding economic integration and inequality, this study employ data from the Integrated Public Use Microdata Series (IPUMS) 2002–2022 sample of the American Community Survey (ACS) (Ruggles et al., 2015). This dataset represents a 1-in-100 random sample of the US population, for which we apply individual-level sample weights to generate representative estimates. The analysis sample is limited to individuals aged 16 to 64 residing in metropolitan statistical areas (MSAs), resulting in a weighted sample of 22,944,616 respondents, including both foreign-born and US-born individuals.

The ACS stands as the most extensive publicly available household survey dataset containing pertinent information for our research (Ruggles et al., 2021). These data encompass a range of factors relevant to immigrant life, including demographic characteristics, socioeconomic status, language proficiency, educational attainment, and employment.

Table 2 provides a detailed description of all variables under study. Annual labor income represents the monetary compensation received by an individual in their capacity as an employee over the previous year. This encompasses various forms of earnings such as wages, salaries, commissions, cash bonuses, and tips. Given the dataset’s 20-year span, adjustments for inflation are made to account for changes in purchasing power. Consequently, determining annual labor income involves adjusting figures using the consumer price index, obtained from the U.S. Bureau of Labor Statistics (2023), to reflect changes from the base year of 2002.

Table 2 Description of study variables
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The average income stands at approximately $31,280, with a notable standard deviation of $14,650 (see Table 3). Upon examining the quartiles, it becomes apparent that the majority of individuals fall within the income range of $14,430 to $41,510, with the median income (Q2) being $38,860. Notably, the income distribution exhibits positive skewness, signifying that the mean income exceeds the median income. Regarding homeownership status, roughly 65% of the sampled population are homeowners, with a standard deviation of 0.47. This suggests that homeownership is relatively common in the USA, with a significant proportion of individuals owning their homes. Further, the data reveals that approximately 14% of individuals in the dataset are classified as living in poverty, with a standard deviation of 0.34.

Table 3 Descriptive statistics of the study variables
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The immigrant status serves as a binary variable distinguishing whether the respondent was born outside the USA (1) or not (0). Approximately 12% of the respondents in the study are immigrants. Two indices of cultural distance are utilized in this study, one derived from Hofstede (1980) and the other from Inglehart and Wenzel (2005). Both indices are calculated using the methodology proposed by Kogut and Singh (1988), with data obtained directly from Hofstede Insights (2023) and the World Values Survey Cultural Map (2023). This data covers the same study period as the ACS survey (2002–2022).

Work experience is quantified as the potential time available for work after completing schooling. It is computed as the difference between the respondent’s age and years of schooling minus six, following the approach outlined by Borch and Corra (2010) and Kollehlon and Eule (2003). Control variables encompass gender (1 = female; 0 = else), age (in years), marital status (1 = single; 0 = else), education (cumulative years of schooling), English proficiency (1 = speaking only English or knows English well or really well; else = 0), age at arrival (in years), and the year for which data was collected (2–22).

This study gauges community disadvantage through an index comprising three demographic characteristics that Wilson (1987) deemed crucial: the proportion of the MSA population that is Black, the proportion living under the federal poverty line, and the proportion of families in the MSA headed by single parents. These variables are used to compute a standardized index of community disadvantage, calculated for each year of the study period. The Cronbach’s alphas ranged from 0.76 to 0.83 across years.

The analytical approach encompasses descriptive statistics, bivariate associations, and multivariate regressions. All analyses were conducted using STATA. Since the data structure is hierarchical, with individuals nested within MSAs, the empirical analyses were carried out by using multilevel modeling techniques. The choice of a particular technique was based on the level of measurement of the outcome variable. We used multilevel linear regression to estimate predictors of household income, an interval variable. The other two dependent variables are binary measures. Parallel analyses (Tables 4–6) were estimated for all three dependent variables. Table 5 presents multivariate linear regression models predicting household income, while Tables 6 and 7 display multivariate logistic regression models predicting homeownership and poverty status, respectively. The following equation is used to estimate the economic outcomes (annual labor income, homeownership, or poverty status) in Table 3.

$$\text{Economic outcome}=\upbeta \_0+\upbeta \_1 X\_1+\upbeta \_2 X\_2+\upbeta \_3 X\_3+\uptau$$

where β0 is an intercept, β1 is the regression coefficient for immigrant status (X1), β2 are regression coefficients of control variables (X2), β3 are regression coefficients for cultural distance (X3), and τ is normal error.

Table 4 Correlations between study variables
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Table 5 Linear regression models for annual labor income
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Table 6 Logistic regression models for homeownership
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Table 7 Logistic regression models for poverty status
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Before deciding whether to keep all predictors in one model, we examined the inter-item correlations and variance inflation factors (VIF), as shown in Table 4. All correlations and VIF values of predictors, except for cultural distance, were below 0.5 and 3, respectively, indicating that multicollinearity was not an issue among these items. Consequently, most predictors were included in one model for the multivariate analyses.

Model 1 estimates the change in socio-economic status associated with being an immigrant without considering any other predictors. Model 2 adds various controls, including the year of the survey, English proficiency, work experience, age at arrival, educational attainment, marital status (1 = single, 0 = otherwise), age, gender (1 = female, 0 = otherwise), and community disadvantage. Cultural distance (IW) and cultural distance (H) were not included in the same model due to their collinearity (Table 4 shows a correlation of 0.51 between these two variables). Therefore, model 3 includes cultural distance (IW), and model 4 includes cultural distance (H) in addition to all factors present in model 2.

For all regression models, we conducted a Breusch–Pagan test to check for heteroscedasticity. A high p-value (typically above 0.05) indicates that we fail to reject the null hypothesis and cannot confirm heteroscedasticity at the chosen significance level. The p-values in all regression models ranged from 0.2 to 0.4, indicating the absence of heteroscedasticity. Therefore, the assumption of homoscedasticity was satisfied.

Multivariate Results

As previously indicated, Table 5 illustrates the regression analyses forecasting annual labor income. Initially, it is observed in model 1 that immigrant status is inversely linked to annual labor income, with this association being statistically significant at the p < 0.001 threshold. In model 2, which encompasses all control variables, the adverse impact of being born outside the USA remains significant but is somewhat attenuated. Models 3 and 4, incorporating cultural distance measured via IW and H methodologies, indicate no notable impact. This implies that cultural distance may elucidate the income disparity between immigrants and natives, constituting a pivotal finding of this paper.

Overall, the regression results shown in Table 5 provide insights into the relationships between various variables and annual labor income. Many variables are statistically significant predictors, and the direction of their influence on income is largely consistent with prior findings. Specifically, English skills, work experience, educational attainment, and age all have a positive association with income. The association is statistically significant at the p > 0.001 level in all models of Table 5. This finding is consistent with earlier studies highlighting the importance of human capital in shaping immigrant economic outcomes (Brixy & Hessels, 2010; Chiswick & Miller, 2009; Cohrs & Stelzl, 2010; Hall & Farkas, 2008; Restifo et al., 2023). Age at arrival is negatively associated with income in all models, and the association is statistically significant at the p < 0.001 level. This suggests that arriving at a younger age is associated with higher income. This finding is also consistent with the current models of immigrant adaptation (Nee and Alba 2013; Portes & Rumbaut, 2001; Waters & Pineau, 2015). Being single is positively and being female is negatively associated with income in all models, and these associations are statistically significant at p < 0.001 level. As expected, community disadvantage is negatively associated with income in all models, and the association is statistically significant at the p < 0.001 level.

Immigrants from culturally distant countries are disadvantaged than their counterparts from countries that are culturally close to the USA. This is the premise on which we have built this work. Our analyses confirm this hypothesis. Cultural distance calculated either using IW or H methodology is negatively associated with income and is statistically significant at p < 0.001 level. These results can be valuable for understanding the factors that affect labor income and for making policy or individual decisions related to income.

The performance of the models was comparatively evaluated based on the R-squared (R2), Akaike information criterion (AIC), and Bayesian information criterion (BIC). AIC and BIC are generally considered more appropriate measures than R-squared (R2) for comparing the goodness of fit between different regression models, especially considering the trade-off between model complexity and accuracy. R2 increases significantly from model 1 to model 2 (from 0.158 to 0.284), indicating a substantial improvement in explanatory power. AIC and BIC both show significant improvements from model 1 to model 2. Overall, model 3 appears to be the best model based on these statistics, balancing good explanatory power (R2) with the lowest AIC and a stable BIC, while not significantly worsening heteroscedasticity (Breusch-Pagan test).

Logistic regression models for predicting homeownership are shown in Table 6. Model 1 includes one factor—immigrant status. The change in the odds of homeownership associated with being an immigrant is negative and significant at the p < 0.001 level. This suggests lower odds of homeownership for immigrants compared to the US-born. In the absence of any controls, immigrants are approximately 18% less likely than non-immigrants to own a home. The magnitude of the difference in the odds between immigrants and non-immigrants decreases with the addition of other variables in the subsequent models of Table 7 but remains significant in all models of Table 6.

Proficiency in English, work experience, educational achievements, and age all contribute positively to the likelihood of owning a home. All factors representing human capital show a positive association with socio-economic status for both immigrants and non-immigrants alike. On the flip side, factors such as age at arrival, being single, being female, and community disadvantage are consistently linked with a lower likelihood of homeownership across all models in Table 6. These results align with existing knowledge. Firstly, previous research indicates that immigrants who arrive early in life tend to fare better than those who arrive later (Nee and Alba 2013; Portes & Rumbaut, 2001). Secondly, women and single individuals traditionally exhibit lower rates of homeownership compared to men and families (Goodman & Mayer, 2018; Thomas & Mulder, 2016). Lastly, communities facing higher levels of disadvantage are known to have lower rates of homeownership compared to less disadvantaged communities (South et al., 2016; Thomas & Mulder, 2016).

Cultural disparity exhibits an inverse relationship with homeownership, being statistically significant at the p < 0.001 level. Similarly, in alignment with the antecedent table, the approach employed to compute cultural distance does not exert any discernible influence on its impact. Whether employing the IW or H method for calculating cultural distance, the outcome remains consistent: immigrants from nations characterized by greater cultural divergence from the USA are less likely to own a home compared to their counterparts who originated from countries culturally akin to the USA. It is imperative to underscore that these regression findings maintain their robustness even with the incorporation of variables that are statistically significant predictors of homeownership.

In Table 6, Pseudo-R2 increases significantly from model 1 to model 2, indicating a better fit. AIC and BIC both show substantial improvement from model 1 to model 2, with slight improvement in models 3 and 4. The Breusch-Pagan p-values decrease from model 1 to model 2, indicating an increase in heteroscedasticity likelihood, but remain stable beyond model 2. Still, the Breusch-Pagan p-values are far above the p < 0.05 threshold, which suggests that heteroscedasticity is not a problem.

The logistic regression models presented in Table 7 offer insights into predicting poverty status. In the initial model, the odds of being in poverty are notably higher for immigrants compared to native-born individuals, and this effect is statistically significant at the p < 0.001 level. However, it is crucial to highlight that the significance of this effect diminishes in the second model with the inclusion of other predictors. Interestingly, introducing cultural distance (in two versions) in models 3 and 4 completely eliminates the significance of the immigrant status effect. In essence, the difference in poverty status between immigrants and non-immigrants becomes non-significant only when cultural distance is taken into account. This discovery carries significant implications, to be further discussed in the subsequent section. All effects added in model 2, with the exception of the year of the survey, are significant and align with the expected directions. In model 1, English skills show a negative association with poverty status, reaching significance at the 0.05 level, but this association does not hold in the other models.

The data indicate that having more work experience, higher educational attainment, and being older are all linked to a lower likelihood of being in poverty, and these associations are statistically significant at the p < 0.001 level. In model 1, age at arrival is positively associated with poverty status, reaching significance at the p < 0.05 level, but this association is not consistent across other models. The analysis also reveals that the likelihood of living below the poverty line is higher for individuals who are single, women, and those residing in disadvantaged communities. Consistent with earlier analyses (refer to Tables 5 and 6), the results underscore the significance of cultural distance as a determinant of socio-economic status. Immigrants from countries with greater cultural distance are more likely to face poverty compared to their counterparts arriving from less culturally distant countries.

Discussion

Despite consistently lagging behind native-born residents in socioeconomic well-being (Boccagni & Hondagneu-Sotelo, 2023; Drouhot & Nee, 2019; Heath et al., 2008; Waters & Pineau, 2015), the reasons for this immigrant-native gap remain debated (Feliciano & Lanuza, 2017; Portes & Rumbaut, 2001; Waters & Pineau, 2015). While education and work experience undoubtedly play a role (Ferrer & Riddell, 2008; Hall & Farkas, 2008), our research suggests they are not the whole story.

Furthermore, evidence shows significant variation in how different immigrant groups are perceived (Drouhot & Nee, 2019; Massey & Pren, 2012; Raux, 2023; Schmidt et al., 2022). This is not unique to the USA (Chetty et al., 2020; Drouhot & Nee, 2019; Heath et al., 2008; Massey & Pren, 2012). Cultural bias seems to be a culprit, with immigrants perceived as more culturally distant facing greater prejudice (Coenders et al., 2008; Heath et al., 2008; Schaeffer & Kas, 2023).

In this paper, we conducted an empirical analysis examining the socioeconomic outcomes of US adults, specifically comparing immigrants and non-immigrants during the period from 2002 to 2022. The primary objective of this study was to systematically assess the significance of cultural distance between immigrants’ countries of origin and the USA as a predictive factor for income, homeownership, and poverty status. Previous literature on the economic integration of immigrants in advanced countries has highlighted various causal factors, including certain indicators of cultural distance (Ebner & Helbling, 2016; Hwang et al., 2010; Xi, 2013). However, prior research has not delved into alternative measures of cultural distance while simultaneously accounting for human capital, community disadvantage, and other relevant factors. This article contributes to existing literature by concurrently testing all key causal factors and extends the inquiry by examining the mediating effects of two alternative measures of cultural distance on the relationship between immigrant status and income, homeownership, and poverty status.

This paper presents distinctive empirical evidence underscoring the pivotal role of cultural barriers in immigrant adaptation, surpassing differences observed in human capital, marital status, community economic conditions, and other controls. The findings strongly support the notion that cultural links between origin and destination countries significantly influence the successful adaptation of immigrants. Both measures of cultural distance proved significant, indicating that economic outcomes for immigrants from countries culturally similar to the USA are notably superior to those from culturally dissimilar countries. Consequently, cultural proximity emerges as a catalyst for immigrant integration.

While this conclusion aligns with previous assertions by scholars of immigrant integration (Alba & Nee, 2003; Xi, 2013; Zhou & Lee, 2007), this work contributes a more precise documentation than previously found in the literature. Specifically, we introduced refined measures of cultural distance and examined multiple economic status outcomes, a less common approach in existing research. While previous studies have highlighted factors such as language (Chiswick & Miller, 2005, 2012), our research places particular emphasis on the influence of cultural distance between immigrants’ countries of origin and the USA. This suggests that cultural proximity acts as a catalyst for immigrant integration, a notion supported by scholars in the field (Hwang et al., 2010; Xi, 2013).

Limitations

The research presented in this paper is not without its limitations, which warrant acknowledgment and consideration for future studies. The findings of this study are based on data specific to the USA and may not fully generalize to other countries with different immigration policies, socio-economic landscapes, and cultural contexts. Thus, caution should be exercised when applying these findings to other settings.

While the study introduces refined measures of cultural distance, the choice of these measures and their operationalization may not fully capture the complex and multidimensional nature of cultural differences between immigrant groups and the host society. We advocate for future studies focusing on alternative indicators of culture as predictors of immigrants’ socio-economic status. Our current empirical understanding of the cultural system’s significance in immigrant incorporation, especially its implications for economic outcomes, remains limited.

Despite controlling for various socio-economic factors, there may be other unobserved variables that influence immigrant economic outcomes but are not accounted for in the analysis. Future studies should explore factors such as social networks and access to resources that could play significant roles in shaping immigrants’ socio-economic trajectories. Recognizing the reasons behind immigrant economic disparities compared to natives can guide efforts to address barriers to immigrant economic integration studies.

The research opens doors for further exploration in a few areas. The study uses established frameworks to measure cultural distance, but future research could delve deeper into which specific cultural dimensions (e.g., power distance, individualism) have the strongest impact on economic outcomes. The study focuses on the USA. Examining cultural distance and economic outcomes across different receiving countries could reveal interesting comparisons. By investigating these areas, researchers can gain a more nuanced understanding of the complex relationship between culture and economic integration for immigrants.

Conclusion

While our findings align with existing literature on immigrant integration, our study contributes a more nuanced understanding by introducing refined measures of cultural distance and examining multiple economic status outcomes. However, despite these advancements, our empirical understanding of the role of cultural barriers in immigrant economic integration remains limited. The implications of our research extend beyond academia. It appears that the USA may need to acknowledge the challenges in integrating immigrants from culturally distant countries. We argue that acknowledging these challenges is crucial for developing effective policies aimed at immigrant integration. Initiatives such as promoting foreign language learning can help bridge cultural gaps and facilitate immigrant adjustment into the host society.

Based on the findings of this research, we propose some potential policy implications. The study suggests cultural distance creates barriers to economic success. This could lead to a focus on integration programs that help immigrants understand American social norms, business practices, and communication styles. Moreover, existing policies might consider cultural factors when providing assistance to immigrants. For example, programs could be tailored to address the specific challenges faced by immigrants from cultures more distant from the USA. Finally, English proficiency is already a control in the study, but it highlights the continued importance of supporting language acquisition for immigrants.

Yet, it is important to recognize that complete assimilation may not be feasible nor desirable. Instead, efforts should focus on addressing cultural barriers to enhance overall immigrant integration while respecting and celebrating diverse cultural identities. Recognizing the barriers faced by immigrants from culturally distinct backgrounds is crucial for informing proactive policies that foster a more inclusive and welcoming environment for immigrants in the USA.