Jean Thirouin

Abstract

This paper attempts to further research on the fiscal impact of immigration. My findings are illustrated by computing the net fiscal impact, in present value terms, of admitting one additional immigrant, conditional on education, gender, and age at the time of immigration. I demonstrate that the average immigrant arriving past age 34 has a lifetime negative fiscal impact. Additionally, a college educated immigrant arriving past age 52 will have a lifetime negative fiscal impact while a non-college educated immigrant will roughly have a lifetime negative fiscal impact, regardless of age at arrival. Further, I confirm that age at arrival matters, and determine that arrival prior to working age influences educational attainment. Finally, I provide a household life-cycle model that sheds light on the fiscal contribution of immigrating families.

Introduction

With an aging population and a social security system set to run out of funds in the foreseeable future, is immigration the key to slowing the decline in the working-age share of the population while helping bolster a strained fiscal deficit? This question is at the heart of the public policy debate on immigration. The key to answering this question lies in understanding the age structure of immigrants and their life-cycle fiscal impact.

Immigration has greatly increased in the past decades. From 1940-1950 there were about 1mil immigrants, 1950-1960 had 2.5mil immigrants, 1960-1970 had 3.3mil immigrants, 1970-1980 had 4.5mil immigrants, 1980-1990 had 5.7mil immigrants, 1990-2000 had 11.3mil immigrants, and 2000-2010 had 8.8mil immigrants (Gibson 1999). Along with the increase in immigration has been an increase in public opinion on the best public policy pathway. Some have viewed immigrants as a “fiscal drain”, while others have believed in their ability to provide a solution to the aging population problem. Skilled workers that would immigrate early and immediately contribute through taxes would likely lead to large positive net fiscal effects, even accounting for the subsequent costs of retirement (Kjetil 2000).

A particular interest of immigration is their impact on the host country’s welfare system. The United States’ pay-as-you-go system relies on the working age population to support retirees, which lends itself to the argument in favor of increased immigration. From 1980 to 2010, though, the share of inflows of immigrants aged 50 to 74 increased from 8.9% to 14.5% (Yi 2014). As can be seen in Figure A, the mean age of immigrants has been steadily rising since the 1970s, with women, on average, older than men. If immigrants are entering the country at a later age, they may be worsening the aging situation. On the other hand, Figure B and C paint a different picture as they show an upward trend in the educational attainment of immigrants. Between 1950 and 2007, the foreign-born share of employees in the U.S with a masters, professional, or doctorate degree rose from 5.9% to 18.1% (Peri 2010). They may be coming in later, but if they are more skilled, their impact on the welfare system may also be larger.

Although immigration is a hot policy topic, there have only been three major changes in U.S immigration policy in recent history: 1) the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) or Welfare Reform, 2) the 1920s national-origin quotas, and 3) the 1965 elimination of such quotas (Klopfenstein 1998). The 1996 Welfare Reform served to restrict immigrant access to Federal welfare benefits such as Medicare and food stamps for their first five years in the country, however, states could grant aid out of their own funds. Additionally, in 2004 the annual cap for new H-1B visas was lowered from 195,000 to 65,000 – essentially an attempt to reduce competition between similarly-educated immigrants and natives (Peri 2010).

What is the best course of action for the government to solve a growing fiscal deficit tied to an aging population problem? Should the government focus more on changing the level or mix of immigrants (Auberbach Oreopoulos 2000)? The goal of this paper is to shed light on the quantitative aspect of this debate.

The remainder of the paper proceeds as follows. I provide a brief literature review in Section I. Section II describes my data, Sections III, IV, V provide my method along with my results, and Section VI constitutes a household life-cycle model. Section VII discusses limitations, government policy implications, and concludes.

 

I. Literature Review

 Immigration has a growing body of literature with two mains sides: 1) the labor market outcomes, and 2) the fiscal impact (Borjas 2013). Much of the focus has been on the demand side of the labor market impact caused by immigrants. More specifically, attention has been paid to the effects that immigrants have had on native wages. While research has shown immigrants to lower native wages, especially those with less than a college level education, others have argued for the complementary nature of immigrants (Ottaviano Peri 2011). My paper focuses on the fiscal impact of immigrants therefore I will mainly reference the literature on that front.

Despite the strong implications of immigration for public finance, there is a limited amount of literature addressing the cost-benefit life-cycle aspect of immigration (Friedburg Hunt 1995). However, the related literature is important in formulating hypotheses and making assumptions in my later models. One main focus has been the importance of immigrating at an early age (OECD)(Shaafsma Sweetman 2001)(Myers et al. 2009)(Seeborg Sandford 2003). Comparably, human capital accumulation and language proficiency have been determined to be two of the most important characteristics of immigrants at arrival, closely tying in to age of arrival effects (Lagakos 2016). Intuitively, an immigrant arriving as a young child has a much higher likelihood of assimilating into the culture and surpassing the language barrier than an immigrant in his late thirties (Hoyt Chin 2016). Additionally, immigrants from poor countries will tend to accumulate much less human capital in their birth countries before migrating (Lagakos 2016).

No clear consensus has been reached about the use of welfare by immigrants, however. While some studies have shown that longer time spent in the host country (Sweden) have led to decreased rates of welfare use, others have shown the exact opposite (Hansen Lofstrom 2010). Another issue concerning welfare literature has been the lack of separation between welfare usage and welfare eligibility (Pekkala Kerr 2011). This is especially important to consider due to changes in eligibility over time.

There are two main techniques for evaluating the fiscal impact of immigrants. The first estimates the growth to GDP due to growth in labor supply (Borjas 1995)(Freeman 2006)(Drinkwater et al. 2007). The second relies mostly on accounting methods and estimating the total cost and benefits that immigrants will have on the economy, which varies greatly by stage of life (Pekkala Kerr 2011). The accounting method is the type my paper focuses on. More specifically, my paper sheds light on the varying net impact at all stages in an immigrant’s life. As well, I expand on my different life-cycle models to estimate household impacts of migrating families for alternate family structures.

II. Data

My data comes from the March Annual Social and Economic Supplement (ASEC) supplemental survey of the Current Population Survey (CPS). It is all downloaded from the Integrated Public Use Microdata Series (IPUMS) website which allows the identical coding of variables across time for easier cross-time comparison. Additionally, IPUMS-CPS provides all estimations with the person weights in the census data files (Flood King 2015). My sample consists of pooled microdata, information on individual persons and households, for the years 1994 to 2016.[1] Data from previous years was not used since the age classification of immigrants could only be constructed from a variable in my time frame.

The IPUMS-CPS data source provides variables crucial to my research such as age, year of immigration, gender, education, taxes, benefits received, among many others. I classify immigrants by marking immigration population (impop) = 1 if they answered the question about year of arrival. Unlike most of the variables in the CPS, the major tax variables such as FEDTAXSTATETAX, and FICA are not the result of direct questioning of respondents. Instead, the values for the variables come from the Census Bureau’s tax model which was updated in the fall of 2004 to produce more accurate tax estimates. The model simulates tax returns for each individual to produce the estimates needed by incorporating information from non-CPS sources such as the Internal Revenue Service’s Statistics of Income series, the American Housing Survey, and the State Tax Handbook. I adjust all monetary values for inflation to the 1999 CPI indicator using a predefined IPUMS-CPS variable which multiplies a factor for each non-1999 year to a dollar amount in that year.

III. Net Yearly Contribution Model

 A. Net Yearly Contribution – Average

The most important variable I constructed for each observation in my data set is an individual’s net contribution (netcontri) to the government. This is defined as their total taxes paid (taxtot) minus the total benefits received from the government (incgov). Since my data set did not contain predefined variables for total taxes nor total benefits received, using IPUMS’ recommendations, I constructed them by summing across individually reported values of its components. The compositions are defined below:Since netcontri is central to the remainder of my analysis, it is crucial that the variables it is comprised of are as closely reflective to the actual values as possible. I perform a sensitivity analysis in order to determine their viability. Taxtot has relatively little room for error since it already accounts for the three major sources of tax revenue – federal tax, state tax, and FICA. However, an individual’s income received from the government is more complex since it can come from many different sources. I experiment with alternate compositions such as the inclusion of disability income, survivor’s benefits, and earned income tax credit. Plotting the average difference between these compositions and the one mentioned above by age, I notice a negligible impact when looking at immigrants only. I run the same analysis for natives and notice a slightly greater impact past the age of 50 yet it remains insignificant. I plot both immigrants and natives separately because I am (correctly) predicting that the composition of variables such as survivor’s benefit differs for immigrants and natives.

After constructing my net contribution variables for each observation in my data, the first step in my analysis is to find the average contribution at each age. Plotting the results gives me a first pass look at the impact an immigrant has on the government budget at each age in their life. As can be seen in Figure 1, an immigrant’s impact over their life follows an intuitive understanding of one’s life-cycle earnings. Earnings begin around 15 years of age and sees its greatest growth between the early twenties to the mid-thirties, where it stands around $5,300/year. It begins roughly leveling off to around $6,200/year, the peak earnings stage during the fifties. From there, the decline begins, which becomes sharp around the early sixties, the age of retirement, and roughly levels off around -$6,000/year for the remainder of an immigrant’s life.

To compare immigrants’ average earnings to natives, I run the same analysis restricting the data to natives only. Shown in Figure 1, a native’s average impact at each age follows a similar pattern with slight differences. Earnings begins at age 15 and rises faster than immigrants until the mid-thirties where it reaches around $6,500/year. Instead of leveling off, though, earnings continue to increase, albeit at a slower rate, until the fifties where they peak at $8,200/year. A similar sharp decline can be seen around the early sixties before roughly leveling off at -$7,500/year for the remainder of their life.

Figure 1 shows the aggregation of all immigrants and all natives, whether male, female, college educated, living in the US for a short/long time, etc. In other words, the results discussed above can be generalized to “an average immigrant” and “an average native”. However, when facing the decision to accept a foreigner into the country, other differentiating characteristics mentioned above are observed. Below I provide a contribution analysis based on the main characteristics observed when a foreigner attempts to immigrate to the United States, limited to the ones observed in my data set.

Net Yearly Contribution – Education

I continue my analysis of an immigrant’s yearly impact on the government budget by taking into account their level of education. I construct a binary variable college that is defined as 1 if an individual has completed at least a year of college all the way up to doctorate degrees, and 0 for any educational attainment below one year of college. I then find the average contribution per year at each age for both immigrants and natives, with and without a college education.

The results for college and non-college educated immigrants and natives are displayed in Figure 2. We can see that both college and non-college educated immigrants follow a very similar life-cycle pattern mentioned earlier in our “average immigrant” case. The stark difference is revealed in their earnings growth and peak earnings. An immigrant that has at least one year of college education will, on average, peak at around $10,000/year net contribution in his mid-fifties. A less educated immigrant, on average, will peak and stagnate closer to $2,000/year. Said in another way, a college educated immigrant, on average, will have a five times greater positive impact on the government budget than a non-educated immigrant.

Looking at the impact that education can have on an immigrant’s earnings clearly reveals its importance, yet even more can be learned when compared to the impact that education has on a native’s earnings. A college educated native mirrors the earnings of a college educated immigrant, with peak earnings slightly surpassing $11,000/year. Interestingly, non-college educated natives perform much better than non-college educated immigrants. A non-educated native reaches peak contribution closer to $3,500/year, or almost twice that of an identical immigrant.

Figure 2 reveals another comparison between immigrants and natives. We saw that natives are reaching higher peak earnings during their working life and therefore have a higher net contribution. Once retirement age in the early sixties is attained, though, we now see natives have a much higher negative contribution than immigrants. Are contributions during one’s working life offsetting the income received from the government post-retirement? If not, at what age is the cutoff? This phenomenon motivates the life-cycle model I build later in the paper that can shed light on immigrant and native impacts on the government budget throughout the entirety of their life.

It is impossible to tell strictly from the graphs what factors may be causing the discrepancy in earnings between immigrants and natives[1]. Non-educated immigrants most likely suffer from the language barrier, strongly decreasing their already limited employment options. A non-educated native may be able to benefit from relatively higher earnings based on an ability to be employed in communication-based jobs. At higher levels of education, however, immigrants don’t suffer as much from the language barrier as they can go into high earnings fields that are focused more on quantitative skills rather than communicative ones.

C. Net Yearly Contribution – Gender

Should the government have the right to discriminate against incoming immigrants depending on their education? If so, should they be able to do the same regarding an immigrant’s gender? Whether or not they should, a lot can be learned by analyzing the differential earnings of men and women. The trends observed earlier in both “average immigrants” and educated immigrants can further be explained by separating the data according to an immigrant’s gender. Additionally, these trends prove useful later in understanding the dynamics of the household life-cycle model.

Figure 3 illustrates the net contribution gap between male and female immigrants. It is evident that men have much higher earnings growth throughout their working life. Both men and women seem to reach peak contributions around their fifties, but men on average contribute close to $8,500/year while women peak closer to $4,000/year. Once retirement is reached, both men and women follow similar net contribution trajectories into the negatives.

Figure 3 also allows for the comparison of the effect of gender on the net contributions of both natives and immigrants. Native men and women, similarly to immigrants, have differential impacts on the government budget at each age before retirement. However, the men’s highest contributions are about $10,500/year with the women’s around $5,500/year, both almost $2,000 more than immigrants for each gender. Unlike immigrants, native women have a slightly smaller negative impact than native men into their seventies and beyond.

IV. General Lifecycle Model

The net contribution model reveals the general trends that are typically seen in an immigrant’s life-cycle impact on the government budget at each age. Throughout their working life, fifteen to early sixties, they have a net positive effect on the budget each year by paying more in taxes than they are receiving in welfare. Additionally, the model provides insight into the effect of differing characteristics on the magnitude of immigrants’ impacts. As shown, education and gender play an important role in predicting future earnings.

However, the model fails on two fronts: 1) it doesn’t reveal whether an immigrant (or native) has a net positive impact on the government budget over the entirety of their life, and 2) it does not factor in a potentially crucial factor, the amount of time spent in the United States. I build two different models to address each problem. The first sums an immigrant’s contribution over their lifetime, showing at what age an incoming immigrant no longer benefits the country’s government budget. The second expands on this model by restricting the sample for each age of arrival, essentially allowing me to elicit the effect that duration in the country has on earnings.

A. Lifecycle Model 1 – Average

My first pass life-cycle model is generalized to the “average immigrant” and builds off the previous yearly net contribution model. I use the net contribution at each age and sort them negatively, from oldest to youngest[3]. I then sum up every yearly contribution. Each resulting data point, plotted in Figure 4, indicates an immigrant’s future impact on the government budget at that age. This allows me to answer the question, “What is an immigrant’s lifetime impact if they arrive at age x?”.

Figure 4 also indicates the point at which positive contributions during the working life offset negative contributions after retirement. An immigrant arriving at age 39 will break even in terms of lifetime contributions to the government budget. From a pure government budget standpoint, this indicates that any immigrant over the age of 39 should not be allowed to enter the United States. In Figure 4, we can also observe the lifetime contributions of natives. It is quite interesting to see that natives have the exact same cutoff point, 39 years of age – even though they contribute more during their working life, they receive more benefits post-retirement. Lastly, we can see that, on average, an immigrant and native who start off at the same point, age 15, will differ in their lifetime impact by $19,000, with natives contributing $91,000 and immigrants contributing $72,000.

B. Lifecycle Model 1 – Education

Analogous to the yearly contribution education model, I use the lifetime contribution method developed in the “average immigrant” case to demonstrate the effects of education on an immigrant’s total impact to the government budget. Using the previously constructed college variable, I separate the data into college educated and less than one year of college education. Sorting the ages from oldest to youngest, I sum up all observations and plot the resulting data points shown in Figure 5.  I include the equivalent analysis for natives to use as a comparison “control” group.

The results seen in Figure 5 illustrate the drastic impact of education that was not as evidently clear from the yearly contribution model. No matter at what age they arrive to the United States, an immigrant with a high school diploma and less will never have a net positive impact on the government budget. In other words, they will always take out more from the United States than they will be able to give back over their entire lifetime. In stark contrast, an immigrant with at least one year of college education and above will be profitable over their entire lifetime so long as they arrive before age 53. In addition, a college educated immigrant who starts working at age 15 will, on average, contribute $250,000 to the government budget.

Figure 5 reveals another interesting trend that was unapparent in the yearly contribution model when comparing natives and immigrants. Although college educated immigrants seemed to reach lower peak contributions than natives during their working life, over the course of their entire life they outperform them in net positive contribution. It doesn’t make intuitive sense in this context to compare the break-even point since natives don’t just “arrive” in the country at a later age. However, it is still possible to draw conclusions about efficiency in impact over a lifetime by noting that it takes an immigrant arriving at age 53 or later to become a net loss while a native must start working at age 49 or before to positively contribute. Non-college educated natives and immigrants follow very similar impact trends over their lifetime, with natives’ post-retirement benefits outweighing their higher earnings during their working life, leading to very similar total impacts of -$66,000 and -$75,000, respectively.

C. Lifecycle Model 1 – Gender

Lastly, I expand upon the trends seen in the yearly contribution gender model by looking at the impact gender has on lifetime government budget impact. As mentioned earlier, discriminating due to gender may not be the right policy decision, but understanding the life-cycle paths of both men and women can be useful in forecasting household impacts. This is especially relevant for a country with an immigration policy like the United States, which places a large emphasis on allowing family members to re-unite through immigration (Borjas 1996).

Figure 6 follows the same method as the education and average lifecycle models, this time separating yearly net contributions based on gender, and summing up each age into a lifetime impact. Figure 6 demonstrates the lifetime disparity in contributions between immigrant men and women. Assuming a working life starting at 15, men, on average, will contribute $141,000 while women, on average, will basically break even at $2,500. It is interesting to note the parabolic shape of the lifetime impact curve, which indicates that the worse age at which an immigrant can arrive is 64 for women and 66 for men.

Figure 6 also compares the gender impact between immigrants and natives which allows us to conclude that both native men and women, on average, have a greater lifetime impact, assuming they begin working at age 15. Having seen that, on average, immigrants perform worse than natives, it makes sense that both immigrant men and women perform worse than natives. Since educated immigrants, on average, perform better than natives, however, then it must mean that one of two things (or both) could be happening. Either there are less educated immigrants than natives in my sample, pulling the immigrant averages down, or there is an omitted effect, such as the duration of stay.

My sample data reveals that 38% of immigrants have at least one year of college education and 37.7% of natives have the same educational attainment. Therefore, the ratio of college educated to non-college educated is irrelative in this context. Earlier, we explored the age at immigration trends which revealed that, on average, immigrants have started to come in their late twenties, early thirties. I believe that the “average” immigrant’s life-cycle impact is picking up this effect of later arrival in one’s life.

V. Age at Immigration Lifecycle Model

In my second version of the life-cycle model, I test the hypothesis that earlier immigration and longer duration in the United States positively impacts earnings and therefore an immigrant’s net contribution. My belief is that a 35 year old immigrant who arrived at age 1 has a different impact on the government budget than a 35 year old immigrant that arrived at age 35. The topic of many studies, it is continually shown that the earlier an immigrant arrives to the United States, the higher the likelihood that their earnings will surpass those that arrive later in their life. This phenomenon has been attributed to different factors, with higher cultural assimilation and education attainment two of the most important (Sandford Seeborg 2003)(Hoyt Chin 2010)(Schaafsma Sweetman 2011). In the same vein, the earlier an immigrant arrives, the longer he/she can positively contribute through lengthened working life taxation.

A. Lifecycle Model 2 – Average

The basis for my second life-cycle model is very similar to the previous one. The main difference is that I restrict the sample for each age of arrival. To do this, my first step is constructing the variable ageimmig. For each observation, I take the year at which they immigrated and subtract it from the year in which they responded to the survey. I then build a loop that runs through each age, starting at 0 and ending at 90. For each iteration of the loop, I restrict the sample to that loop number’s age.

Similarly to previous models, my next step is calculating the average yearly contribution at each age. This time, having a restricted sample means that I am averaging the yearly contributions for only those that immigrated at age x. I then sort my data from oldest to youngest, and sum each age to arrive at a lifetime impact. The lifetime contribution of arriving at age x is the only value that I am interested in since I am calculating that same impact for each different age of arrival. With this in mind, for each iteration of the loop, I only save that one observation. Running another loop, I append the lifetime contribution for each age of immigration to build my final model. Each observation in this model shows the lifetime impact of immigrating at age x, and unlike previous models, it accounts for the time spent in the United States. The results are plotted in Figure 7.

There are a few different results to focus on from this graph. The most important one, this time more robust than in previous models, is the age of immigration where working life contributions will offset retirement benefits received. Figure 7 suggests that any immigrant arriving after the age of 34 will, on average, have a total negative impact on the government budget if they remain in the United States throughout the remainder of their life.

The second result, which confirms my hypothesis regarding the positive effects of arriving early and having a longer duration of stay, can be observed by looking at the trends of immigrants arriving between age 0 to 15. Since all immigrants arriving in that age range start working at age 15, in theory, if there is no effect of duration of stay or earlier arrival, they should all have the same lifetime impact. Clearly, Figure 7 illustrates the opposite – a child immigrating at age 1 will contribute on average roughly $287,000 while a child immigrating at age 10 will contribute on average only $179,000. These findings are in line with (Schaafsma Sweetman 2011) and (Hoyt Chin 2010).

Thirdly, there is one interesting “spike” around age 20-27 (there are a few others around age 7 and age 45 but those qualify more as noise than structural trends). Why is it that immigrants arriving between the ages of 20 and 27 incrementally begin earning more while the overall trend shows that no matter what age you arrive, the older you are, the lower your total impact? We can’t draw any conclusions about this phenomenon from Figure 7 but subsequent variations to the model will attempt to answer this question. My theory is that those arriving between 20-27 have a higher likelihood of having completed college, are looking for work abroad, and therefore are, on average, more educated than those coming in around 18 years old.

Fourth, as we saw with previous lifetime contribution models, Figure 7 also highlights a parabolic shape to the age of arrival curve. We can see that (ignoring the noise spike around age 44), the curve levels off around age 60 before rising again. This result makes intuitive sense since 60 is right around the age of retirement. If an immigrant arrives around that age and never contributes, only receiving benefits from the government, the later the immigrant arrives past that point, the smaller his negative impact will be on the government since his life expectancy will decrease.

Lastly, I want to point out that we are dealing with age of arrival observations across many different years so taking averages mitigates the effects of economic downturns or booms in certain periods. It is also important to note that Figure 7 has more noise than previous models. This is in part due to the sampling size I am using and having further restricted each data point to observations for that age of immigration only.

The age at immigration trends discussed earlier revealed that immigrants have, over the past five years, been coming in to the United States around age 28. From an immigration policy standpoint, it is interesting to see the lifetime impact that the average 28-year-old immigrant has on the government budget amounts to roughly $99,500. Although the graph has not been included, it is also interesting to see that a 27-year-old man has a $234,000 lifetime impact and a 29-year-old woman has a $25,600 lifetime impact, on average.

B. Lifecycle Model 2 – Education

 I continue to build on variations to my second life-cycle model by evaluating the impact trends due to differing educational attainment. Comparable to my hypothesis regarding the effects of earlier arrival, I also believe that the effect of education will shed more light on the effect of age at arrival. Simultaneously, age at arrival has an impact on educational attainment, as a younger immigrant will have an easier time assimilating to the culture, therefore giving him/her a better chance at higher human capital accumulation (Schaafsma Sweetman 2011)(Myers et al. 2009).  Once again, I run the same loops as discussed in the previous section, this time separating my sample into those with at least one year of college education and those with less.

In Figure 8 we can see that earlier arrival evidently has an impact on lifetime contribution. Restricting once again our analysis to those arriving between ages 0 and 15, however, we don’t observe the same major differences in lifetime contributions for those college educated. While age 0 seems to be an outlier, ages 1 to 14 all oscillate in the earnings ranges of $345,000 to $400,000. What could be the reason for not seeing a clear impact of earlier arrival? This is most likely because they all end up with a college education. The impact of earlier arrival may be increased educational attainment, manifested in the oscillation observed in Figure 8. The homogeneity in my sample would simultaneously suggest that earlier arrival, age 1 versus age 10, affects future lifetime contribution by affecting the chances of achieving a college education. Therefore, when looking at a sample of only college educated immigrants, the effect of earlier arrival (before working age) is non-existent.

Around age range 40-47, we observe a significant spike most likely due to highly-educated immigrants, those with PhDs, arriving at those ages. Another interesting feature of Figure 8 is the lifetime impact cutoff point of $0, which takes place at age 52; this is much older than the 34 years of age suggested for the “average” immigrant. This lifetime graph is also very particular in that it doesn’t have the same parabolic shape as all the previous ones. Rather, past age 52 it oscillates around $0. I do not have any intuitive theory as to why this takes place, however, my best guess is that older college educated immigrants coming to the United States are most likely wealthier. Instead of coming for work or medical care, they come to settle and retire – creating minimal impact on the government budget. Additionally, I addressed this data concern in the previous section, and it may have an even stronger impact since my sample has greater restrictions, but my results may be in part due to a lack of enough observational data. This may also explain the increased noise in the college-educated data relative to the non-college educated.

For non-college educated immigrants, the earlier arrival effect does take place. Within that sample of immigrants, arriving at age 1 makes a significant difference from arriving at age 10. Since their educational attainment remains low throughout their life, a potential theory might be the effect that the language barrier has on future earnings – the later they arrive, the stronger the effect. The most puzzling aspect of Figure 8 for non-college educated immigrants is that the lifetime impact $0 cutoff takes place at age 12. Arriving at age 12 versus arriving at age 13, unlike arriving at age 24 instead of age 25, will not increase your net contribution by an extra year since you may only start working at age 15. This provides even stronger evidence that there must be some significance in arriving at an earlier age, even prior to working age eligibility. Unfortunately, I am unable to explain why the parabolic shaped curve’s inflection point takes place around age 45. The only theory I could have for a 55-year-old making less of a negative impact on the government budget than a 45-year-old is if they both come in, receive benefits right away, and don’t find work. The plausibility of a working age foreigner receiving benefits upon immigration is relatively low, however.

VI. Household Lifecycle Model

Immigrants who come to the United States are often not alone – they come with families. I test my hypothesis that the impact of a family on the government budget differs from the impact of a sole immigrant through the construction of a household life-cycle model. This model helps me better approximate the impact of children on a household while varying the different types of household possibilities.

I construct four different types of immigrant households: 1) a parent and child, 2) a father, son, and daughter, 3) a mother, son, and daughter, and 4) a father, mother, son, and daughter. I assume that the parents have children at age 25 and that once the parents die the children keep contributing until their own death. For each household, I attempt to answer the question, “What is the contribution of a household when the parent(s) immigrate(s) at age x with child(ren) aged x-25?”. More importantly, the answer to this question helps shed light on the optimal immigration policy when considering multi-person households. I plot my results in Figure 9.

To project the 2-person household’s net contribution, I sum the contribution of a parent immigrant arriving at age x, with their child’s contribution being an immigrant arriving at age x–25. Doing this for every x>24 provides a full view of all age possibilities at parent arrival. I construct the 3-person households by summing the contribution of either a male or female parent arriving at age x>24 with their son and daughter arriving at age x-25. Similarly, to construct a 4-person household I sum the contribution of a male immigrant and female immigrant arriving at age x>24 with a male immigrant and female immigrant arriving at age x-25.

My results show that a 2-person household will positively impact the government budget over their lifetime if the parent arrives before age 57. Similarly, a 3 and 4-person household will also positively impact the government budget as long as the parents arrive prior to age 57. The shape of the household lifecycle curves reveal that a 3-person male parent household, on average, will have a greater positive contribution on the government budget if the parents arrive prior to age 57, however, the 4-person household will have a greater negative contribution if they arrive past that age.

The 4-person household follows a very similar pattern to the 3-person male parent household, revealing that a female spouse does not seem to have much of an impact on the government budget. In fact, around parent arrival age of 30-57 the 3-person male parent household shows a stronger positive impact than the 4-person household. The 3-person female parent household and 2-person household both have less positive and negative overall impacts on the government budget. A 3-person household led by a male shows a stronger positive impact prior to arriving at age 57 than one led by a female, however, past age 57 the magnitudes of their impact are very similar. It is also important to note that around age 47, the 3-person households both begin to have a more positive impact than the 4-person household. This is likely due to the parents hitting retirement age sooner and with 2 parents rather than 1 in the household, the negative impact is amplified.

Interestingly, all curves follow a slight parabolic shape whereby a parent arriving around age 72 will have the worse impact on the government budget than if they came at an earlier or later age. This may be explained by the amount of time that both the parent and child will spend receiving aid from the government past retirement age. A parent coming in at age 72 can expect to receive welfare until death, while their child at age 47 will contribute less over their working life than when they hit retirement. Past that point, the older households will not receive welfare for as many years and therefore will have less of a negative impact.

VII. Conclusion

A. Limitations

One of the main limitations of my paper is that it ignores the impact of immigrants on native wages and employment displacement effects, and instead relies purely on estimating their fiscal impact. The argument is usually made that immigrants are displacing native workers when they gain employment, which would mean that the taxes paid for the job displaced does not change whether an immigrant or native holds the position. Under my model it would seem as if the immigrant is benefitting the government by the total amount of taxes paid. Additionally, while the immigrant may not receive welfare, the now displaced worker may begin receiving welfare from the government, creating a net loss.

Another potential limitation in my paper is the lack of a death discount factor, which would better predict lifetime impacts by discounting it based on life expectancy. According to the Social Security Administration’s Actuarial Life Table, male life expectancy ranges roughly between 75-80 years old while female life expectancy ranges between 80-85. These ranges are very similar to where my data cuts off, which mitigates the effects of a large impact on my results.

Lastly, this paper does not account for the public cost of education. My results show that the younger an immigrant arrives, the higher the lifetime contribution. However, while an immigrant arriving prior to schooling age is shown to have a greater positive fiscal impact than one that may arrive at age 25, for example, the cost of education could impact the net impact. If both immigrants end up having the same level of schooling, one’s native country will have paid for it while the other will have it paid for by the United States. It makes it extremely difficult to compare such a scenario since other factors such as cultural integration can have a large impact on future success, regardless of whether two immigrants have the same level of education.

B. Policy Implications

 To better understand policy implications of the results displayed in this paper, it’s important to understand the current U.S. Immigration Policy Goals. These revolve around 4 axes: 1) Economic: increase labor supply, especially where skill deficits exist, 2) Humanitarian: reunite families, 3) Cultural: ethnic and racial diversity, 4) Political: allowing or refusing certain political refugees. This contrasts to the Canadian immigration “points system”, which allocates visas to prospective immigrants by awarding points based on language proficiency (28), education (25), experience (15), age (12), employment contract (10), and adaptability (10).

My results clearly indicate that the United States should actively solicit young, highly-skilled immigrants. Similar to Canada, my results for the average immigrant point to the optimal working ages of 18-35, my cutoff point showing age 35 as the beginning of a net loss. The only difference being that they award no points for immigrants below the age of 18, while my results all indicate that the younger the immigrant arrives, the higher the lifetime contribution.

Based on the two main metrics observed, age at arrival and education, my results suggest that immigration can be a solution to the aging population and fiscal deficit. If the U.S. goal of immigration aims to solve this growing problem, the government has a clear incentive to implement a similar “points” system to Canada. Other potential government interventions could include ways to reform social benefits provided to immigrants through optimal time-dependent structures. These would ensure that they are making a net positive impact on the system over their lifetime, while also considering their impact on the native population.

C. Final Remarks

This paper demonstrates that immigrants have strong quantitative implications for fiscal policy in the United States. In particular, this paper investigates the optimal lifetime contributions based on age at arrival, education, and gender. The findings throughout the paper are illustrated by computing the net fiscal impact, in present value terms, of admitting one additional immigrant to the United States, conditional on education, gender, and age at time of arrival. The lifetime contributions vary considerably across these three characteristics, with large and positive values for college-educated immigrants arriving in the earlier part of their life.

Using a yearly net contribution model, two life-cycle models, and a household contribution model, I demonstrate that the average immigrant arriving past age 34 has a lifetime negative fiscal impact. Additionally, a college educated immigrant arriving prior to age 52 will have a lifetime positive fiscal impact while a non-college educated immigrant will roughly have a lifetime negative fiscal impact, regardless of age at arrival. Further, I confirm that age at arrival matters, and determine that arrival prior to working age influences educational attainment. Finally, I provide a household life-cycle model that sheds light on the fiscal contribution of immigrating families.

My research has a few avenues for add-on research. First, I do not distinguish between the level of welfare and taxes for each individual’s contribution. Understanding that dynamic might better motivate welfare or tax reform. Second, I focus on the mix of immigrant characteristics, but not on the level of immigration[4]. Third, while I have data on education, age, and gender, a further analysis could look at the effects of language proficiency, skill, and native country of birth (Lagakos 2016). Lastly, it is important to note that while these results are based on U.S. immigration policy, there is an external validity concern, as each country has its own unique welfare system policies that also differ in magnitude.

Index

Footnotes

[1] 2017 NAS report on fiscal impact of immigration justifies pooling by finding great similarity between patterns in 1995-97 and 2011-13

[2] Literature review addresses this point.

[3] Neat trick I learned from Professor Hendren!

[4] I do this because Kerr shows only minor displacement effects, even after large immigration inflows.

References

 

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