The Effects of International Migration on the Well-Being of Native Populations in Europe

With worldwide migration becoming increasingly prevalent in policy agendas over the past several decades, understanding the effects that migrants have on a host country's population continues to be an important research agenda. There is a large literature documenting the effects that migrants have on native wages, tax burden, unemployment, etc. However, very little is understood about how migrants affect the happiness, or subjective well-being, of natives.This paper uses the European Social Survey to analyze the effects of aggregate immigration inflows on the subjective well-being of native-born populations in a panel of 26 countries between 2002 and 2010. We find that recent immigrant flows have a nonlinear, yet overall positive impact on the well-being of natives. Specifically, we find that immigrant flows from two years prior have larger positive effects on natives' well-being than immigrant inflows from one year prior. Our findings are very small in magnitude and in practical application; only large immigrant flows would affect native well-being significantly.


I. Introduction
With international migration reaching unprecedented levels of importance on both national and international policy agendas, the need for reliable studies that identify and analyze the trends and effects of migration has never been more crucial. As of 2010, an estimated 214 million people, or around 3.1 percent of the world's population, were classified as international migrants, living outside of their country of birth. With only 2.5 percent of the population, or 75 million people, living outside their country of birth in 1960, this statistic illustrates a trend of increasing migration worldwide (United Nations, 2006). With rapid increases in global population, environmental deterioration, aging populations in OECD countries, and globalization, it is highly unlikely that this statistic will reverse in the future (OECD, 2011). However, we do not have a clear picture of how international immigration affects the well-being of the native population in a given country. This is a topic of critical importance, as any debate on immigration policy revolves around the ultimate effects of migration on the welfare of native populations.
In this study, we combine individual level data from the first five rounds of the European Social Survey (ESS) with immigration and macroeconomic variables from the OECD to explore the potential effects of recent international migration on the self-reported well-being of a country's natives. Our results indicate that immigrant inflows have statistically significant positive and nonlinear impacts on the happiness of natives, which vary in magnitude with the year of migrant inflows. For instance, immigrant flows lagged by two years have a larger impact on happiness of a country's population than immigrant flows lagged by one year. Immigration flows beyond the second year have statistically insignificant results. The variation in natives' well-being can be explained by a variety of factors, including immigrant assimilation, the 3 effect on native well-being (p.24). However, once immigrants become even more assimilated they again have zero effect on the population's well-being. While Akay, Constant, and Giuletti (2012) analyze immigration over a series of regions in Germany, we expand the scale of this study to a series of 26 countries and utilize the European Social Survey to analyze the impact of immigrants on a larger scale. Furthermore, we utilize an OLS fixed effects regression with lagged immigration variables as our benchmark model.
That being said, other related literature provides interesting background when approaching the topic of happiness and migration. Specifically, Polgreen and Simpson (2011) used the World Values Survey to discover a U-shaped relationship between emigration and happiness. In other words, emigration decreases as happiness increases in relatively unhappy countries, but rises as happiness increases in relatively happy countries. Furthermore, migration has been shown to negatively affect the happiness of family members left in the home country and that migrants, on average, tend to have a lower happiness score than non-migrants, suggesting that migrants' may be mistaken in thinking that moving will increase happiness (Borraz, Rossi, & Pozo, 2008;Bălțătescu, 2007;Knight & Gunatilaka, 2010;Bartram, 2010Bartram, , 2011. While these studies do not specifically address the welfare impacts that migrants have on native populations, they provide an interesting perspective on how happiness, or the perception of happiness, affects migration decisions and outcomes. 3 Though there is a relative lack of research examining immigration and native well-being, there are a multitude of studies that explore other channels through which migration affects the native population, from wages and labor market performance to internal immigration rates and population growth. It is these studies that provide a large impetus for our research. If immigrants 3 For further background on happiness research as it relates to migration, we refer the reader to Simpson (2013). 4 have significant impacts on the native population in other important manners, there could be a significant correlation between migration and native happiness.
David Card, one of the prominent researchers looking at the impacts of immigration on natives, has sought to explore the specific effects of migration on the population of the United States. Card (2001) found that immigration flows have a small negative impact on the wages of low-skilled natives and did not cause large native outflows. He also found a small negative relationship between immigration and native employment rates. It is important to note that the magnitude of the estimated impacts of immigrants were small, with immigration (during the 1980s) reducing wages and employment rates in high migrant cities by one to three percent. His results confirm those of other studies, such as Friedberg & Hunt (1995), Card (1990), Butcher & Card (1991), and Card & Lewis (2007, which conclude that there are small effects, if any at all, of immigration on U.S. wages. Card (2007) also shows that immigrants have small but nonnegligible effects on the welfare of U.S. natives through a variety of other factors, including increased housing prices, expanded tax base, undesirable "peer group" effects, and the hindrance of "effective" governance. Borjas & Katz (2005) provide another perspective on the impact of immigration on U.S. natives. They find that the increased number of low-skill immigrants over the past several decades, mainly from Mexico, has negatively affected the wages of low-skilled natives and has benefitted those who are highly skilled. 4 4 Another study conducted by Ottavanio and  finds that immigration has a positive impact on high-skilled native wages and a small negative impact on low-skilled native wages. This result confirms Borjas' (2003) study in which he found that the wages of competing workers were lowered by 3 to 4 percent for every 10 percent increase in immigrant supply. In his 2003 study, Borjas states that immigration "substantially worsened the labor market opportunities faced by many native workers" (p. 1370). However, he 5 does recognize some of the potential advantages of immigration. He reports that natives could benefit from relative price decreases of low-skill intensive goods and services, increased labor market efficiency, and production complementarities (Borjas, 1995;Borjas, 2001;Borjas and Katz, 2005).
While studies performed by Borjas and Card have focused on immigration into the U.S., several studies have been conducted that focus on European immigration. Staffolani and Valentini (2010) examine the impact of immigration on the Italian labor market. They assert that all natives in the so-called "regular sector" experience increased wages with immigration inflows.
However, they also differentiate between white-and blue-collar jobs, stating that while natives with white-collar jobs always benefit from immigration, blue-collar natives can either win or lose depending on a variety of immigration factors. A similar study conducted by Falzoni et al. (2007) asserted that immigration negatively affected Italian blue-collar wages, but white-collar wages were not significantly affected. Other studies, conducted using data from Spain and the United Kingdom, countries that have both seen large immigration increases over recent decades 5 5 Gonzalez and Ortega (2009) note that Spanish provinces gained, on average, 17% of their initial workforce in immigration flows between 1998 and 2008. In the UK, immigration flows have increased by approximately 65% from 2000 to 2009 according to OECD data. , conclude that employment and wage rates are not significantly affected by immigration shocks (Carrasco, Jimeno, & Ortego, 2008;Dustmann, Fabbri, & Preston, 2005). Several studies have also examined the complementary aspects of immigration in Europe. Dustmann et al. (2003) notes that empirical evidence suggests that immigration inflows enhanced wage growth in the UK. In Italy, another study illustrated that migrants actually increased the wages of national manual workers (Gavosto et al., 1999). However, others, including Angrist and Kugler (2002), contend that the inflexible labor market institutions in Europe, encompassing firing costs, restrictive collective bargaining agreements, rigid wages, and high business entry costs, will 6 most likely exacerbate the pain caused by immigration to natives in the long-run. In fact, Angrist and Kugler (2002) find that increased immigration is negatively associated with native employment rates in a panel of European countries. Furthermore, Boeri (2010) contends that native perceptions of immigrants worsen during periods of recession, stating many natives believe that migrants prevented them from obtaining "the unemployment assistance to which they were legally entitled"(p. 651).
These studies depict the large potential for immigration to have both adverse and beneficial effects on the native population of a country. 6 Some of the factors that immigration has been shown to affect, such as wages and employment, are also correlated with happiness. 7 Given the nature of the ESS cross-sectional data that we use to study the relationship between happiness and migration inflows, endogeneity is a potential issue we have to contend with in our study. While we can examine how the happiness and immigration are associated, current theory does not provide us with the information necessary to determine the direction of causality (Simpson, 2013). Even though we tend to frame the discussion in the mindset that migrants could possibly have an effect on the happiness of natives, one should keep in mind that it could be happiness (or lack thereof) of the native population that attracts (or dissuades) migrants from moving to the native country. Following the suggestion of Simpson (2013), we account for endogeneity through the use of multiple lagged independent variables, specifically regarding our main variable of interest, immigration flows. Utilizing lagged immigration Therefore, it could be likely that immigration is also correlated with measurements of subjective well-being and happiness, thus motivating our study. 7 variables also enables us to analyze any dynamic effects that immigrant inflows may have on native well-being.

II. Empirical Specification
The determinants of well-being can be modeled as: individuals not being able to objectively report their well-being would be captured by our error term (Frey and Stutzer, 2002). The estimated coefficients are represented by α, β 1, and β 2 . , k t y F − where y represents the lag structure, which mitigate potential endogeneity issues. It is also necessary to account for immigration flows relative to each country's population stock. This gives a more accurate depiction of the effects of immigration on native populations by allowing us to account for a ratio of the immigrant inflows to the level of population in country k. Therefore, all immigration flows from country k will be divided by , k t y P − , the population of country k in time period t. 8 See section IIIa for more information about exaggeration effects. 8 Equation (1) provides the basis for the benchmark specification of our empirical equations. Substituting happiness as our measure of well-being and including all controlling variables and fixed effects, we get our baseline empirical specification: 2 , , , where the dependent variable is the happiness index of individual i in country k at time period t.
Our independent variables of interest are those concerning immigration inflows. Each term is composed of the lagged immigration flows F (in thousands) into country k in time period t-y being divided by the population P of that country (in thousands) in the same time period. We take the natural logarithm of each term, and square the terms to consider non-linear relationships.
We also have a dummy variable 'EU' that takes a one if a country is in the European Union at a given time t, and zero otherwise. To control for additional factors that may influence the relationship between happiness and immigration inflows, we also include time In addition to examining the impact of immigration on happiness, we also explore the influence of migration on overall life satisfaction. Thus, we also substitute happiness with a measure of life satisfaction and compare the results with our baseline specification in our results section. 9

III. Data
Due to the relative richness of European immigration data, the primary data source used for examining our model is the European Social Survey (ESS). The ESS is a multi-stage crosssectional survey conducted biannually that covers over 30 nations, both within and outside of the European Union (EU). The survey was established in 2001 and is currently conducting its sixth round. For the purposes of this study, we utilize the cumulative dataset composed of the first five survey rounds (2002, 2004, 2006, 2008 and 2010). In addition, we use only countries that had enough immigration data available over the rounds they participated in. This left us with 26 observable countries, each with at least two rounds of ESS data containing approximately 500 to 2,000 respondents each (reported in Table 1).

a. Subjective Measures of Well-Being
As a relatively new subject of research, self-reported well-being measures have been greeted with some skepticism within the economic community. Because such measures are subjective and cannot be directly observed, unlike most data utilized by economists, some economists have rejected them as "unscientific." In addition, some argue that such measures are too simplistic and do not present meaningful data (Frey and Stutzer, 2002). If this is the case, economists studying happiness would be presented with significant problems when analyzing and interpreting results. However, recent papers by Di Tella and MacCulloch (2006), Frey and Stutzer (2002), Kahneman and Krueger (2006), and Layard (2006) suggest that the study of happiness within economics can bring about meaningful and beneficial results to the profession, especially in regard to policy formation. Kahneman and Kreuger (2006) provides several key arguments for the benefits of using such data, not the least of which include more accurate welfare analysis and a greater 10 understanding of how to maximize societal welfare. Layard (2006) complements this discussion by advocating for the use of happiness in the field of economics, stating that better theory and policy would result from greater "insights of revealed preference" (p. C33). In fact, Layard believes that "the prime purpose of social science should be to discover what helps and hinders happiness" (p. C32).
One does, however, have to treat happiness measures with caution. Special considerations must be taken into account when comparing individuals across cultures and time. Di Tella and MacCulloch (2006) mention that interpersonal comparisons of happiness indicators among small numbers of individuals continue to be problematic. According to their research, these problems can be attributed to an "exaggeration" effect, where individuals scale their happiness differently from others. That being said, it has been seen that these problems are reduced dramatically when the number of individuals being compared increases. With over 140,000 observations being used in each regression, "exaggeration" effects and any other biases due to comparing small numbers of individuals should virtually disappear from this study. In addition, a gamut of recent studies has lent increasing legitimacy to the practice of comparing well-being over both time and countries. 9 The ESS provides two separate measures of individual well-being: happiness and life satisfaction. The following questions were asked during the survey process: Our study utilizes country fixed effects to account for any differences in mentality or culture among countries.
• "Taken all things together, how happy would you say you are?" • "All things considered, how satisfied are you with your life as a whole nowadays?" Both of these questions were answered on a 0 to 10 scale, with 0 being "extremely unhappy/dissatisfied" and 10 being "extremely happy/extremely satisfied." Several studies have shown a high correlation between life satisfaction and happiness (Schyns, 1998;Blanchflower and Oswald, 2004). Other studies have shown that there is a distinct difference between the two measures. Stevenson and Wolfers (2008) have revealed that life satisfaction and happiness measure certain variables differently (e.g. GDP per capita) and that there is a psychological difference between the two concepts. We therefore include both measures in our analysis.

b. Immigration
We next discuss the control variables. First, the immigrant status of ESS respondents proved to be slightly problematic as the ESS does not directly ask whether one is a native of the country or not. However, the survey does ask a series of questions relating to immigration status, including whether the respondent was born in the survey country and whether they had citizenship status in that country. For this paper, we use a strict classification of natives, by classifying them as those who were both born in and had citizenship in the survey country.
Under this classification, 9.0% of respondents over the 5-year cumulative ESS dataset were considered non-native and were excluded from our analysis.
To obtain immigration flow statistics for each of the 26 countries used in the study, we utilized the international migration database of the OECD. 10 10 Supplementary data for non-OECD countries was obtained from Eurostat's and the World Bank's statistical databases.
This allowed us to acquire immigration statistics for not only the year that the survey was conducted but also for the three years preceding the immigration. We were then able to merge these immigration statistics with the ESS database. The inclusion of the lagged immigration data helps to reduce endogeneity. 12 In addition to happiness and immigration data, several socioeconomic controls also needed to be included in the regressions as controls. Based on the findings of previous studies, we decided to include variables for income, gender, age, health status, education level, religiosity, and children at home. Also included were several macroeconomic variables for each country, including real GDP growth rate and the civilian unemployment rate. We will now describe each of these variables.

c. Income
One of the most controversial topics in happiness research has centered on the relationship between income and happiness. Easterlin (1973) found that while individual happiness increases with rising income, increases in real GDP per capita across society are not associated with rising happiness. Therefore, one's subjective well-being will change with increases in income, but will change inversely with the increase in the income of those around them. Easterlin's conclusions have led many to believe that income is not strongly linked to individual well-being above a certain threshold where basic needs are fully met. 11 To analyze the effect of income in our study, two issues needed to be addressed. First, all income reported by the ESS was recorded in brackets (or ranges), rather than discrete numbers.
To deal with the income brackets, we followed a technique similar to that used by Ball and Chernova (2008) and Bartram (2011), using the midpoint of each income bracket as an Nevertheless, Ball and Chernova (2008) published a study with results contradictory to Easterlin's conclusion.
They assert that changes in both relative and absolute income have significant impacts on the well-being of an individual. This result is also confirmed by Frijters, Haisken-DeNew, and Shields (2004), who found that large increases in real household income following the reunification of East and West Germany were associated with increases in life satisfaction.
11 See Bartram (2010Bartram ( , 2011, Frey & Stutzer (2002), and Clark, Frijters, & Shields (2008 Including self-reported health status has other benefits as well. Helliwell and Putnam (2004) state that "including self-reported health among the predictors of subjective well-being…has the added advantage of tending to offset the effects of any 'positivity' or 'optimism' response bias, because such a response bias ought to affect both self-assessed health and subjective well-being" (p. 1440).
, it was necessary to account for age as well as age-squared. Helliwell and Putnam (2004) note that there is a slight positive correlation between education and happiness. However, they explain that this impact may be due to the correlation between higher education and increased health, and that education may in fact have no direct impact on measures of well-being. To confirm this result, we employ a continuous variable that accounts for the number of years of education for an individual.
Our study also includes controls for the marital status of an individual.  (2010) found that there were differences in life satisfaction indicators between religions (e.g. Muslim populations had, on average, a lower life satisfaction than Christians in Israel). Helliwell and Putnam (2004) note that "more frequent interactions with other people in both church and community settings tend to increase the extent to which those individuals think that others can be trusted and thereby to enhance their subjective well-being" (p. 1441). Their research also determined that it is possible to differentiate between the subjective measures of religiosity, such as religious belief, and more objective ones, such as "church" attendance frequency. In addition, Ball and Chernova (2008) conclude that happiness of an individual is positively correlated with increased religious importance. As such, we include a measure from the ESS on how religious an individual is. 15 Children have also been shown to be a statistically significant factor in the life satisfaction of an individual. Ball and Chernova (2008) find that people with two or more One's self reported answer to how religious they are will be referred to as their religiosity.
children were, on average, more satisfied than those who did not have any children. However, people with only one child were not statistically more satisfied than those without any. The ESS includes a variable in which individuals denote whether they have a child living at home during the time of the survey or not.
To control for macroeconomic trends that could potentially spur or hinder immigration inflows into a country, we include several key macroeconomic variables in our regressions. All macroeconomic variables included in our analysis (e.g. real GDP growth and civilian unemployment rate) were retrieved from the OECD statistical database and are specific to each country.

e. Summary Statistics
Using the first five rounds of ESS data, we were able to obtain a large sample of natives  There are also significant differences in happiness and life satisfaction across our panel of countries (reported in Table 3). Denmark had the highest overall happiness (8.350) as well as the highest overall life satisfaction (8.497). Behind Denmark, Switzerland and Finland both have very high happiness levels (8.121 and 8.032, respectively). The countries with the lowest overall happiness are Turkey (6.1), Russia (6.08), and Bulgaria (5.33).
As commonly expected, we find that the distribution of real income was skewed to the right. To correct for this, we took the natural log of all income measures. We found that the mean real income was approximately €22,122 with a standard deviation of €22,473 (in 2005 €).
As seen in Table 4, the means of the remainder of the control variables are as expected.
The average age in the study is 46.4, 53% of observations are female, and at 12.08, the average number of years of education is roughly equivalent to completion of secondary education.
Finally, 55.7% of the sample is married, 25.8% is single, 7.9% is widowed, 7.3% is divorced, and only 1.2% of the sample identify as separated.

IV. Results
The primary question being addressed in this paper is whether or not immigration flows, as a whole, have a statistically significant effect on the well-being of native populations in a given country. We first address the results and significance of our benchmark empirical model, with happiness as our dependent variable. We then conduct robustness checks, specifically looking at specifications with alterations in our immigration, individual, macro, and interaction terms. Finally, we compare the results of our regressions with life satisfaction as the dependent variable with those using happiness. 18 The benchmark results (Table 5) are presented in multiple sections: immigration variables, macro controls, and demographic controls. 17 In fact, when evaluating the differential effect of increases in the immigrant ratio for our first year lag, we find that the squared term dominates, leading to an overall positive effect on happiness. For example, our estimates for the first lag of the immigrant ratio suggest that the net effect of a ten percent increase in the ratio of immigrant flows to natives increases happiness by 0.008 points; the linear term causes a -0.086 point reduction in happiness, but the squared-term leads to a 0.094 increase, thus a gain in 0.008 points on a 0 to 10 scale, holding all other variables, and population, constant.
By examining the results of our immigration variables, the first lag term of the immigration ratio yields a negative coefficient, while the second lag term yields a positive coefficient, and both are significant at the 5% level or better. This suggests that perhaps the effect of immigrants on the happiness of natives changes the longer the immigration population stays in the host country. In addition, the magnitude of the estimated coefficient is larger for the second lag, indicating that immigrant flows have slightly varying impacts on happiness over time. Our estimation also indicates that the squaredterms for the immigrant ratio are both significant, suggesting a non-linear relationship between lagged immigrant ratios and the happiness of natives.
18 17 "ImmPop" represents the immigrant-population ratio in our regression tables. Lagged variables will include an added specification indicating how many years they are lagged by. For instance, immigration flows that are lagged two years will be denoted as "ImmPop2YearLag." If the increase in immigration occurred two years previous, the impact on native happiness would be, on average, a slightly larger 0.012. If we look at the overall effects of immigrant flows over the prior two years, we find a similar increase in That is, immigration flows from different years may have varying impacts on the local labor market due to rigidities and time constraints. This could be especially true if migrants mainly impact the local population through labor market interactions. As Akay, Constant, and Giuletti (2012) suggest, when the economic outcomes of immigrants converge with that of natives, there may initially be utility generating benefits for natives, such as complementary factors of production, lower relative prices of goods and services produced by migrants, and improved labor market efficiency. But, this convergence may ultimately result in increased labor market competition, resulting in decreased utility for natives. Finally, these differences could be due to 20 heterogeneity in immigration cohorts. As Borjas (1989) states, immigration cohorts change in characteristics over time due to the adaptation and assimilation processes. These changes could include increases in productivity and changes in skill levels. As a result, immigrants from different time periods should not be considered to be a homogenous group. Our results confirm this, showing that immigrants over several years have differing impacts on the native populations.
This may well be attributable to the assimilation process, or to changes in the composition of migrant patterns. 19 In addition, the findings of previous researchers 20 who look at the impact of immigration on natives also suggest that immigration has a small impact, if any at all, on native populations.
While we find significant effects, the magnitude of the effects are relatively small, given all other factors. For instance, compared to one's subjective health status, where having 'very good' health status has a relatively large and significant effect on native well-being 21 The second section of Table 5 illustrates the results of our regression that are specifically focused on our macroeconomic control variables. We can see that the macroeconomic variables have the signs that one would expect intuitively, with real GDP growth being associated with positive increases in happiness and increased unemployment being associated with decreased  (2002), Veenhoven (1996), andErhndhart et al. (2000). In addition, our EU dummy suggests that respondents who lived in a country that transferred from non-EU to EU status experienced a 0.20 increase in happiness, on average, after the change in country status. This increase could be due to any changes in culture, policy, law, etc. that may come with EU accession.
The third section of our table depicts the results for our demographic control variables in our primary models. Every control variable is statistically significant at the 10% level or better.

In line with the results of other researchers 22
While the primary model is specified correctly, it is important to perform checks on the robustness of such a model to ensure that our results are consistent across multiple specifications.
, we find a U-shaped relationship between happiness and age, with happiness being the lowest at an age of approximately 48. In addition, improved health, increased education, heightened religiosity, and being female are all associated with increases in happiness. Our results also indicate that real income has a positive effect on happiness up to a certain point, at which point additional income has diminishing impacts on happiness.

22
Therefore, we provide robustness checks for our benchmark model in Table 6. In regression 2, we only include lagged immigration flows from one year prior and its square. The coefficients on our variables of interest become insignificant in this model, but the combined effect of both variables is positive and statistically significant at the 5% level. 23 Table 7 shows a continuation of robustness checks. In regression 6, all year fixed effects are removed from our model. We can see that this change causes several changes on our immigration variables. Mainly, the coefficient on our squared immigration flow term lagged by one year and our EU dummy is now insignificant at the 10% confidence level. In addition, the positive effects of our overall immigration flows are diminished. These results suggest that year fixed effects play an important role in accounting for the effect of immigration flows on the This result suggests that the inclusion of immigration flows from two years prior is necessary for the proper interpretation of our results. Regression 3 (in Table 6 In order to provide a more detailed story on the impacts of immigration on native happiness, we also include several interaction terms in our regressions in Table 8. First, we add an interaction between current immigration flows and real GDP growth to see if there is an association between GDP growth and migrant inflows. The results from regression 9 indicate that an increase in the real GDP growth rate will decrease the impact that immigration flows from one year ago will have on the happiness of natives. Adding this interaction term results in our EU dummy becoming statistically insignificant, indicating that this term captures the part of the EU dummy that effects native happiness. Adjusting this variable to interact with our lagged immigration flows from two years ago, we receive similar results. 24 We also include terms that interact immigration flows with years of education. Our results indicate that having more years of education decreases the impact that immigration inflows have on the happiness of native populations. While this result is somewhat surprising, it should be noted that these results are small in magnitude. We also interacted native skill levels with immigration flows, finding that being a high skilled native decreases the impact that immigration inflows have on happiness. These results are also small in magnitude, which is not surprising as skill levels were classified according to the native individual's education level. 24 We also include an interaction term that interacts immigration inflows with the migrant stock of a country 25 Finally, Table 9 depicts a comparison between our happiness and life satisfaction models. One can see that the difference between using life satisfaction and happiness as a measure of subjective well-being is small in this instance. This is not surprising given the results of Blanchflower and Oswald (2004) as well as Schyns (1998). The main difference seems to be consistently higher, albeit marginally, standard errors when using life satisfaction. However, in line with the conclusions of many researchers, it cannot be concluded that there is one particular advantage of one measure of subjective well-being over another in every circumstance.
, and find that the impact of migrants does not hinge on the size of a country's foreign population stock (the results are available upon request). Immigrant flows remain statistically significant, with similar signs and magnitudes as our benchmark model. 24 Natives with any education at or above the college-level were considered to be highly skilled. 25 Foreign population stocks from the year 2000 were included for most countries in our sample. Due to data limitations, however, we proxied foreign population stock data from recent years for some countries, including Russia (2002), Poland (2001), and Bulgaria (2001).

V. Conclusion
The goal of this study was to analyze the effects of aggregate immigration inflows into a given country on the well-being, both happiness and life satisfaction, of native populations.
While this study is only a preliminary exploration of a relatively unexplored topic, its findings When the conclusions of this study are combined with prior research on the impact of immigration on native populations, it becomes evident that immigration likely has a net positive impact on the welfare of natives. As a result, one could infer that the costs of immigration, such as marginally negative wage and employment impacts for natives, could easily be balanced or even surpassed by the benefits of migration, such as improved labor market efficient, aggregate economic growth, and lower relative prices of immigrant produced goods and services. 26 However, research on this topic remains scarce, and the exact channels through which immigration impacts the well-being of immigrants have not yet been pinpointed. Further research could examine the specific happiness impacts of immigrant groups of various human capital levels, demographic factors, and length of stay on native populations. The amount of interaction between immigrants could also have a significant influence on how immigrants affect the happiness of natives. In order to carry out future research, more detailed datasets combining disaggregated immigration statistics and happiness are necessary. Much work has yet to be done before a comprehensive understanding of the impacts of immigration on native welfare can be achieved.