Atención a la interculturalidad en los centros educativos. 91

of refugees in the US, and therefore I ran a descriptive statistics analysis to look at the states in which the eight refugee groups reside. I am particularly interested in states where more than 10%

of a specific refugee group reside because an ethnic network may be more readily accessible in these states for the refugees. The results are presented in the Appendix.

The findings on social capital appear to be ambiguous. While some refugee groups tend to congregate in one state such as Cambodian refugees in California, other refugee groups spread out between two to three states. Moreover, the state in which the refugee resides may be highly influenced by the US refugee resettlement program; placement allocations of refugees after admission in the country are largely dependent on the work of voluntary agencies (Office of Refugee Resettlement, 2015). Thus using the percentage of refugee sample living in a state as a proxy for social capital is not the most effective method to capture the extent of bonding and bridging social capital refugee groups possess that would impact their labor market outcomes.

Model 2: Refugees vs Non-Refugee Immigrants Regressions by Regions of Origin

Since human capital, source country standards of living, and social capital cannot fully explain the labor market performance of refugees in the US, Model 2 aims to control for common discriminatory factors such as national origin, color, race, and religion. In Model 2, I test my hypothesis to see the effect of refugee status on labor earnings by comparing refugees and non-refugee immigrants with similar backgrounds. I divided my sample into four categories according to the geographical location of their national origin: Asians, Europeans, Middle-Easterners, and Africans. Vietnamese, Cambodian, and Laotian refugees belong in the Asian category, while the Romanian and Russian are under the European category. The Middle-Easterner category includes the Afghan and Iraqi refugees, and Somali refugees are grouped under Africans. The following regression equation was run to test the relationship between refugee status and labor earnings for each of the four categories:

𝐿𝑛(𝑅𝑒𝑎𝑙𝑊𝑎𝑔𝑒𝑠) = 𝛽₀+ 𝛽₁(𝑅𝑒𝑓𝑢𝑔𝑒𝑒) + 𝛽₂(𝑋_𝑖) + 𝛽₃(𝑡_𝑖) + 𝜀

where 𝑅𝑒𝑓𝑢𝑔𝑒𝑒 is one for refugees and zero for non-refugee immigrants, 𝑋_𝑖 represents the independent variables as listed in Table 3, 𝑡_𝑖 denotes the year-specific dummy variable for each of the five census years, and 𝜀 is the error term. As in Model 1, the census year 2015 was used as the reference year. Regressions results are presented in Table 5; the wage differentials due to refugee status are highly statistically significant at the 1% level across all four categories.

Table 5: Regression Results for Model 2 (Coefficients Converted to Percentage Terms)

Europeans Asians Middle-Easterners Africans

Refugee -4.02%*** -2.76%*** -21.4%*** -11.7%***

*Significant at the 0.10 level; **Significant at the 0.05 level; ***Significant at the 0.01 level

As seen in Table 5, the wage differential due to refugee status for Asians and Europeans are statistically significant yet not economically substantial. An Asian refugee earns roughly 3%

less than his non-refugee counterpart with comparable demographics and human capital. On the other hand, Middle-Eastern and African refugees experience a much more economically

significant wage disadvantage when compared to non-refugeeimmigrants from the regions. Of all four categories, Middle-Eastern refugees suffer from the largest wage gap (-21.4%) in relation to non-refugee immigrants with comparable human capital and demographics from the region.

Similar patterns regarding the effects of demographic and human capital variables can be observed across all four categories. Females are at a significant wage disadvantage when

compared to males, and married individuals earn more than unmarried ones. Speaking English well has a statistically significant and positive effect on wages, and this is especially the case for Europeans. Achieving higher levels of educational attainment also increases labor earnings, with having obtained a bachelor’s degree or advanced degree being the most effective way to increase income since those who have obtained such a degree would enjoy a more than 100% wage advantage over those who have not.

Interaction terms between refugee status and the independent variables are added in the regression for Model 2. These interaction terms account for heterogeneity of return within each refugee group depending on the characteristics; for example, what is the effect of being a refugee female compared to a non-refugee female, or how do returns to schooling differ depending on refugee status. Interaction terms are computed by multiplying refugee status (one if refugee, zero if not) with the controlled variables listed in Table 3. The regressions results are presented in the Appendix.

Model 3: Wage Gap Decomposition by Regions of Origin

I conducted a counterfactual analysis in Model 3 to further examine the differences in labor earnings between refugee and non-refugee immigrants from similar countries of origin.

The same four categories of national origin as Model 2 are used. The counterfactual analysis is a method to measure how refugees would perform in the US labor market if they were paid

according to the earnings function of non-refugee immigrants. To perform the analysis, I first took the means of the dependent and independent variables for refugees under each of the four categories. Only individuals who were employed full-time year-round were selected to conduct the analysis. Next, I ran a multiple regression analysis for non-refugee immigrants in each category using the following regression equation to determine the constant (𝛽₀) and coefficients (𝛽₁ and 𝛽₂) for the non-refugee immigrant earnings function:

𝑅𝑒𝑎𝑙𝑊𝑎𝑔𝑒𝑠 = 𝛽₀+ 𝛽₁(𝑋_𝑖) + 𝛽₂(𝑡_𝑖) + 𝜀

where 𝑋_𝑖 represents the independent variables as listed in Table 3, 𝑡_𝑖 denotes the year-specific dummy variable for each of the five census years, and ɛ is the error term. The census year 2015 was used as the reference year. The regressions results are presented in the Appendix.

I then multiplied the coefficients obtained from the non-refugee immigrant earnings function by the refugee means for each variable. The coefficients of the non-refugee immigrant earnings function signal the opportunities they enjoy in the US labor market. By multiplying the refugee means and the coefficients of the non-refugee immigrant earnings function, I can predict how refugees would perform in the US labor market if they were rewarded like comparable non-refugee immigrants. The resulting values and the constant from the non-non-refugee immigrants earnings function are then summed to compute counterfactual average real wages for refugees.

The actual average wages and the computed counterfactual average wages for refugees of each category are presented in Table 6; wages are rounded to the nearest dollar.

As seen in Table 6, refugees from all four categories would benefit from significantly higher average wages if they had the same reward structure as non-refugee immigrants from their respective regions. The Middle-Eastern refugees would experience the largest increase in labor earnings (31.4%) if they were treated as non-refugee immigrants from the region with similar human capital and demographics, while the European refugees have the least boost (10.7%) if they were paid according to the earnings function of comparable non-refugee immigrants. Since

the predicted counterfactual wages for refugees are higher than the actual wages, this suggests that non-refugee immigrants are treated more favorably in the US labor market and enjoy more opportunities than refugees. For example, there may be a higher return to educational attainment and English proficiency for non-refugee immigrants than refugees with similar backgrounds.

However, there exists a discrepancy between refugee and non-refugee immigrant wages even after computing refugee earnings according to the earnings function for non-refugee immigrants.

This residual wage gap between refugee and non-refugee immigrant wages is most likely due to human capital and demographic differences between the two groups.

Table 6: Actual and Counterfactual Average Real Wages for Refugees Divided by Geographical Region of Countries of Origin (Rounded to Nearest Dollar)

Asians Middle-Easterners Africans Europeans Actual Refugee Real Wages —(1) $58,792 $45,949 $31,799 $72,541 Predicted Refugee Real Wages According

to Non-Refugee Immigrant Earnings Function—(2)

$66,021 $60,395 $37,947 $80,301

Actual Non-Refugee Immigrant Real

Wages—(3) $68,224 $79,569 $59,829 $73,394

% difference of (1) and (2) as calculated by [^(2)−(1)

(1) × 100%] 12.3% 31.4% 19.3% 10.7%

% difference of (2) and (3) as calculated by [^(3)−(2)

(2) × 100%] 3.3% 31.8% 57.7% -8.6%

Thus, I separated the wage gap between refugees and non-refugee immigrants into two parts. The first part is the wage differential due to unequal opportunities in the labor market ((difference between (1) and (2)), and this can be captured by computing a predictive

counterfactual wage for refugees if they were treated as non-refugee immigrants. Refugees may have less opportunities in the US labor market due to both discrimination based on employer taste (Capps et al., 2015; Poutvaara & Wech, 2016) and limited access to job opportunities in the mainstream labor market if they rely predominantly on their ethnic bonding social capital to seek employment (Lancee, 2010; Tegnene, 2015). I argue that the residual wage gap ((difference

between (2) and (3)) that is not captured by the opportunity effect is likely due to differences in characteristics such as human capital between refugees and non-refugee immigrants.

The magnitude of the wage differentials due to opportunities, and characteristics

differences varies across the four categories as seen in Table 6. For Asian refugees, the majority of their wage gap with Asian non-refugee immigrants can be explained by less favorable

treatment in the labor market; if they were paid according to the non-refugee immigrant earnings function, they would earn 12.3% more than their actual wages. On the other hand, there would only be a 3.3% increase in wages for Asian refugees if they had the same characteristics as Asian non-refugees. In the case of Middle-Eastern refugees, the wage differentials due to the

opportunity effect (31.4%) and characteristics differences (31.8%) are more similar in

magnitude. By treating the Middle-Eastern refugees like comparable non-refugees, the wage gap between them and the non-refugee immigrants diminishes by $15,000; the remaining $19,000 is most likely due to differences in means for the human capital and demographic variables.

The effect of unequal opportunities in the labor market is relatively insignificant than characteristics differences when examining the wage gap between African refugees and non-refugee immigrants. If African non-refugees were paid according to the earnings function for African non-refugees, there would be a 19.3% boost to their earnings, but if they had the same

characteristics as the non-refugees, they would earn an additional 57.7%. On the other hand, if European refugees were rewarded as non-refugee immigrants in the US labor market, they would in fact earn $7,000 (8.7%) more than non-refugee immigrants do. This indicates that European refugees, in general, have more desirable skills than non-refugee immigrants from the region do, and their performance would exceed that of the European non-refugee immigrants if they were given the same opportunities as the non-refugee immigrants.

The findings in Model 3 suggest that the skill sets possessed by refugees from the four different categories of national origin vary; for example, European refugees are likely to arrive in

the US with greater levels of human capital than non-refugee immigrants from the region. On the other hand, African refugees may have come into the country with less transferable skills than non-refugeeimmigrants from the region as indicated by the larger wage differential due to characteristics differences. The magnitude of the effects of unequal opportunities on refugee labor earnings also differs, with the Middle-Eastern refugees benefiting the most if they were paid according the earnings function of Middle-Eastern non-refugee immigrants. This result is intriguing since the counterfactual wages indicate that there are discrepancies in the returns to human capital and demographics between refugees and non-refugee immigrants even after my effort to control for discriminatory factors such as race, color, national origin, and religion.

VII. Conclusion

As one of the world’s top destinations for immigration and humanitarian resettlement, the United States continues to welcome immigrants from a great variety of background. With the growing size of the refugee population in the country, it is important to explore the factors that contribute to the success of refugee labor market integration and hence design a more effective resettlement program for refugees in the US. In this paper, I chose to focus on refugees from eight countries: Vietnam, Cambodia, Afghanistan, Romania, Russia and other USSR states, Laos, Iraq, and Somalia. I used American Community Survey data over five years, 2011 through 2015, to study the labor market outcomes of the eight refugee groups in comparison to each other and to non-refugee immigrants with similar backgrounds.

This paper has two foci; firstly, it looks at how source-country specific characteristics as measured by the source country’s GDP per capita impact the labor market performance among the eight refugee groups. The second focus is on the wage gaps between refugees and non-refugee immigrants with similar backgrounds. Descriptive statistics results show that Vietnamese, Romanian, and Russian refugees have the highest employment rates and labor earnings, while Somali refugees have the highest unemployment rate and receive the lowest

wages. There are also differences across the eight refugee groups in terms of their human capital, such as their age, English proficiency level, and educational attainment. There is a ten year difference between the average ages of the oldest refugee group, Vietnamese refugees (48.1 years old), and the youngest group, Somali refugees (38.8 years old). About 97% of Romanian refugees speak well or only English, while only 68% of Laotian refugees do so. More than 67%

of Russian refugees obtain at least a Bachelor’s degree, in comparison to the 12.3% of Laotian refugees that do so.

Ordinary least squares regression analyses were used to compare the labor earnings of refugee groups. Regression results show that after controlling for demographic and human capital variables, there are still economically and statistically significant wage gaps between Vietnamese refugees and seven other refugee groups. Romanian and Russian refugees enjoy a positive wage advantage over Vietnamese refugees, whereas Afghan and Iraqi refugees

experience the largest negative wage gap in relation to Vietnamese refugees. Since human capital cannot account for all of the wage gaps between the eight refugee groups, the findings suggest that source country-specific characteristics may have an impact on refugee labor market performance. When examining the standards of living as approximated by the averaged 2011-2015 GDP per capita of the source country in relation to the US, there seems to be a relationship between GDP per capita of source country and the refugee labor earnings in the US. The only exception to this is the Iraqi refugees. Although Iraq has a higher GDP per capita than Vietnam, Iraqi refugees earn significantly lower wages than Vietnamese refugees do. This finding prompts me to look into the possibility of discrimination against certain characteristics of Iraqi refugees, and hence I control for discriminatory characteristics in my second hypothesis.

To test my second hypothesis on whether refugee status still matters after controlling for common discriminatory factors such as national origin, color, race, and religion, I divided my immigrant sample into four categories according to their national origin: Asians, Europeans,

Middle-Easterners, and Africans. Regressions were run for each of the four categories to investigate the wage differentials between refugees and non-refugee immigrants with similar backgrounds. I found that for Asians and Europeans, refugee status has a minor impact on labor earnings; refugees and non-refugee immigrants from those regions do not experience an

economically significant wage gap between them. On the other hand, Middle-Eastern and African refugees experience a much more significant wage disadvantage when compared to non-refugee immigrants from the same regions. Of all four categories, Middle-Eastern non-refugees suffer from the largest wage gap in relation to non-refugee immigrants with comparable human capital and demographics.

A counterfactual analysis was conducted to examine how wages for refugees of each category would change if they were paid according to the earnings function of non-refugee immigrants from the same region. Results indicate that the computed counterfactual wages are significantly greater than the actual wages received by refugees from all four categories, and hence refugees would benefit if they were rewarded as non-refugee immigrants from their respective regions. This suggests that even after controlling for national origin, color, race, and religion, non-refugee immigrants are treated more favorably in the US labor market and enjoy more opportunities than refugees do. The residual wage gap between the counterfactual refugee wages and actual non-refugee immigrant wages most likely arise from differences in

characteristics such as human capital between refugees and non-refugeeimmigrants.

Overall, my results support my two hypotheses on labor market integration of refugees in the US. Source-country specific characteristics such as standards of living play a role in

determining how refugees perform economically since they are related to the quality of source country human capital and its transferability to the US labor market. However, the contradicting findings in the case of Iraqi refugees suggest that discrimination from employers based on certain characteristics may contribute to the wage differentials among the eight refugee groups. The

existence of discrimination is further confirmed by computing counterfactual wages for refugees according to the earnings function of non-refugee immigrants with similar backgrounds. For all refugee groups, labor earnings would be significantly higher if they were presented with the same opportunities as non-refugee immigrants in the US labor market. Moreover, the effect of refugee status on labor earnings largely depends on the national origin of the refugee. The wages of Asian and European refugees do not differ significantly from those of comparable non-refugee immigrants, whereas Middle-Eastern and African refugees suffer from a notable wage

disadvantage when compared to non-refugeeimmigrants from the regions.

Future research should focus on measuring how the social capital possessed by refugees affect their labor market performance. Due to the data constraints of ACS, I was not able to quantify the bridging and bonding social ties the refugees have established in the US. I believe that with a growing refugee population in the US, social ties formed by these refugees within and outside of their ethnic network would be of increasing interest when studying the labor market assimilation of refugees. Another suggestion for future research is to further analyze the impacts of refugee interaction terms on wage differentials between refugees and non-refugee immigrants.

This paper touches on how interaction terms account for the heterogeneity of return within each refugee group depending on the characteristics, but this topic deserves a more in-depth analysis in the future.

My findings highlight the importance of US-specific human capital in successfully integrating refugees in the US labor market, and hence one major policy implication to draw from this is to incorporate more practical job training programs into the refugee resettlement program so refugees can gain labor market experience in the US. Another way to increase refugee US-specific human capital is to provide refugees with more opportunities to obtain education in the US, which is valued more than education attained abroad (Friedberg, 2000).

Although this will not make the negative effects of discrimination disappear, it would help