1. Does the Promise of Economic Prosperity Attract Legal
Immigrants to the United States?
Econometrics 463 Fall 2015
Liu, Dylan (902)
stunash@tamu.edu
UIN: 121005902
Mashburn, Jacob (901)
mdistrict2012@tamu.edu
UIN: 321007525
Wednesday 9th
December, 2015
Abstract
Little research has been conducted in which a higher gross domestic product has
been tested as a possible “pull factor” for immigrants. Focusing exclusively on immigra-
tion to the United States, the flow of immigrants is obtained by using the percentages
of a country’s population that immigrates to the US in a given year, then is tested
for a possible correlation with the difference in GDP per capita of the United States
and the country of origin. To reduce lurking variables, other possible quantifiable pull
factors are included: the literacy rate, homicide rate, air pollution rate, unemployment
rate, and life expectancy of the country of origin.
1

2. 1 Introduction
Historically, the United States was a land of economic opportunity for immigrants. The
United States is unique in that it is not only the wealthiest country in the world, but
also whose citizenship is vigorously sought after by prospective immigrants from around the
world. In this study, using econometric analysis, we seek to quantify the effect of the this gap
of wealth between countries of origin and the United States on the flow of legal immigration
to the United States. More specifically, we test whether or not there exists a correlation
between the difference in annual gross domestic product per capita (GDP per capita) of an
immigrants country of origin and that of the United States, and the annual proportion of a
countrys population that legally immigrates to the United States.
Historically, American culture has championed the United States as a land of economic
opportunity for immigrants. In this study, we test whether or not prospective migrants from
other countries agree with this idea using econometric analysis- more precisely, whether
or not there exists a correlation between the difference between the annual gross domestic
product per capita (GDP per capita) of a home country and of the US and the number of
people who want to immigrate to the United States from that home country (measured by
year as well).
This flow of legal immigration to the United States will be measured using the annual
ratio of the number of people who immigrate to the US from a home country and that
country’s total population. This allows us to compare immigration between countries with
greatly varied population numbers. The advantage of this is illustrated with this hypothet-
ical scenario: comparing immigration purely by immigrant head count would create a bias
towards countries with a larger population over those with smaller populations, whereas our
interests lie in how immigrants from different economic environments are affected by the
GDP gap rather than the sheer quantity of immigration.
The significance of this question pertains to preparation- the US Citizenship and Immi-
gration Services of the Department of Homeland Security in particular will find this informa-
tion useful- they can allocate more resources, hire more employees to accommodate a larger
demand for immigration to the US in times of economic prosperity, and reallocate them in
times of recession. It can also quantify how much GDP actually affects the desirability of
a country as an immigration destination and illustrate whether or not wealth is, in fact,
the most important motivation for an immigrant’s decision to come to the US, compared
to other factors such as crime, pollution, or education. This can be useful information for
predicting the effects (on the US) of countries experiencing phenomena such as brain drain
(in which countries such as China and India are losing their wealthiest and most educated
citizens to the United States) or economic depression.
We hypothesize that there exists a moderately positive correlation between annual differ-
ence in GDP per capita and annual immigration rates from a country, because the assumption
that people want to maximize their standard of living stems from the economic assumption
of rationality. However, we predict the correlation would be most prevalent in developing
countries (such as Pakistan), and weak in third world countries (such as Ethiopia) and devel-
oped countries (such as Great Britain) due to the fact that citizens in third world countries
have less ability to immigrate and risk more from displacement. Citizens from first world
countries have less incentive to immigrate because the change in quality of life and GDP per
2

3. capita is negligible or worse in the United States, though ability to immigrate is most likely
a much easier process. Overall, we predict that the larger the difference in GDP per capita,
the higher flow of legal immigrants.
There are numerous studies on the direct effect of immigration on a country’s GDP, but
little on the converse’s effect (GDP on immigration, or immigration demand). The Center for
Immigration Studies found that immigration increases GDP by 11% annually, and decreases
wages by about 402 billion a year. Our study is different in that most studies study the
direct effect of immigration on a certain variable such as GDP or wages, and we are studying
the effect a variable, in our case the GDP per capita gap between the United States and the
immigrating country. It quantifies how much GDP effects the flow of legal immigration of a
destination country excluding factors such as pollution levels, literacy levels, life expectancy,
and other factors, isolating the effect of just the gap in GDP per capita.
Similarly, a study concluded in 2012 examined 183 Metropolitan Statistical Areas to
investigate the relationship between immigration and seven measures of air pollution (Price
et al., 2012). The authors concluded that immigration has no effect on pollution (more
specifically, no effect on any of the seven measures). However, our study will investigate the
converse in one of our variables: does air pollution affect immigration? More specifically,
does the promise of cleaner air encourage immigration to the United States?
A rather interesting study conducted in 2006 investigated the potential causes of a widen-
ing gap between the life expectancies of United States natives and immigrants to the US
with data spanning from 1979-2003 (Singh et al., 2006). They concluded that growing eth-
nic heterogeneity of immigrants, and its migration selectivity and continuing advantages in
behavioral characteristics could explain the widening gap. It won’t be particularly helpful
in our study, but note the term “migration selectivity”- it refers to the limit on the number
of individuals who can migrate to another country. This can be naturally imposed due to
poverty (and thus lack of resources to afford migration) or imposed by the government of
the destination country.
In Section 2, we specify the regression model used, in Section 3, we present our findings,
and in Section 4 we conclude that the difference in GDP per capita is highly significant when
supported by other variables.
2 Model Specification
In our regression study, we have six observed variables: difference in the GDP per capita
of the country of origin and the US (measured in USD), the crime rate of the country
(measured in intentional homicides per 100,000 citizens), the country’s air pollution level
(measured in annual average micrograms of pollution per cubic meter of air1
), the country’s
life expectancy (measured in years), the country’s unemployment rate (measured in percent
of country’s workforce, not total population), and the country’s literacy rate (measured in
percent of population).
Because there are few developed countries that can match the size of the United States,
GDP will not accurately describe the desirability of immigration to the United States. To
1
To clarify, these numbers reflect the average amount of particle matter of 10 micrograms or less per
particle (abbreviated as PM10) per cubic meter of air (the unit for this is written as ug
m3 ).
3

4. address this we used GDP per capita which should correct this imbalance. This will measure
the average wealth of a citizen rather than the bulk wealth of countries that vary in size and
population. Citizens in developing countries have a greater desire to immigrate because they
not only have the means to immigrate, but being educated and wealthy, they serve as ideal
candidates for immigration. They have a much better chance at economic prosperity and a
vast increase in quality of life using their skills in the United States than their country of
origin, which may not have the infrastructure or quality of life that a developed country can
offer.
The data of the dependent variable, the percent of the country’s population that immi-
grates to the US, were obtained from the websites of the World Bank (total population) and
the Department of Homeland Security (immigration numbers). These immigration numbers
were taken from 2004-2013 for 21 countries randomly chosen for a total of 72 observations.
The data for GDP per capita, unemployment rates, and life expectancies were obtained
from the World Bank’s website. The homicide rates were obtained the website of the United
Nations Office on Drugs and Crime. The air pollution data was obtained from the World
Health Organization database. This data set, along with the immigration data, was the
main reason why we have only 72 observations: the pollution data had information on more
countries, but only for the most recent years, while the immigration data had more years’
worth of data, but fewer countries. Finally, the literacy rates were found at the website of
the United Nations.
Now that the relevant variables have been introduced, we can introduce our Ordinary
Least Squares regression. The equation is as follows:
I = β0 + β1G + β2L + β3E + β4A + β5H + β6U + u,
where I is the percent of a home country’s population that immigrants to the US in a
given year, G is the difference in GDP per capita between the country and the US, H is the
country’s homicide rate, A is the country’s air pollution, E is the country’s life expectancy, L
is the country’s literary rate, U is the country’s unemployment rate, and u is the unobservable
variation.
The following table provides the summary statistics for our data sets:
Variable Obs. Mean Std. Dev. Min. Max.
I = Immigration
Population
72 0.0012262 0.0018631 0.0000483 0.0076464
Difference in GDP per capita 72 40709.78 14529.88 -2305.123 52493.35
Homicide Rate 72 13.77083 15.83911 0 70.2
Air Pollution 72 72.50417 43.01643 13 198
Life Expectancy 72 70.81958 7.566052 49 82
Unemployment Rate 72 7.9225 7.103988 1.8 46
Literacy Rate 72 81.7625 18.74266 39 100
Our null hypothesis is H0 : β1 = 0, or in other words, that difference in GDP per capita
is not correlated with the country’s US immigration ratio. The alternative hypothesis is
Ha : β1 = 0, which says that they are correlated.
Our first variable that we are trying to determine if it affects immigration to population
ratio is the difference in GDP per capita of the origin country and the United States. It
should have a positive correlation with immigration to population ratio, as the larger the
4

5. gap between GDP per capita, the larger the wealth gap and the greater incentive immigrants
have of leaving in pursuit of better economic opportunities in the United States and as an
escape from widespread poverty.
There are a plethora of reasons immigrants leave their countries, one of which is violence.
People have an inherent need to feel safe, and a desire to keep their loved ones safe. To
address this, we included intentional murder rates as one of our variables. We predict that
there is a positive correlation between intentional murder rates of the country of origin and
the flow of permanent legal immigrants. Developed countries such as Russia may have a
smaller gap in GDP per capita, but their higher murder rates effect some citizens enough
that they would immigrate to safer countries like the United States. This would create a
bias because the immigration would be due to homicide rates rather than GDP per capita.
Citizens in poorer countries in warzones or generally unsafe areas like Iraq and Afghanistan
are also pushed into immigration to escape conflict rather than because of GDP per capita.
Also worth noting is how war and conflict ravage a country’s economy- this suggests the
possibility of a correlation between homicide rate and GDP per capita. War, conflict, and
crime heavy countries would create a bias from intentional murder rates for the flow of legal
immigration.
Another important variable is pollution, namely countries like China and India that have
seen explosive growth, but also critical levels of pollution. Breathing in China is equivalent
to smoking 40 cigarettes a day, and smog is so heavy that in December of 2015 Chinas
capital, Beijing, was shut down completely due to the dangerous levels of pollution. In India,
indoor plumbing is a luxury many people lack, and the resulting open defecation has left the
Ganges River bubbling with methane from human waste and dead bodies. Environments
like this have caused huge exoduses of China and Indias most wealthy and educated to the
United States, which would create a huge bias, as people are immigrating despite a closing
gap in GDP per capita. It should also be noted that China and India supply some of the
highest levels of legal immigration to the United States. It’s also possible that countries with
lower GDP per capita cannot afford sanitation and clean air initiatives, thus suggesting a
correlation between GDP per capita and air pollution levels. We predict pollution levels to
have a positive correlation to the flow of legal immigrants.
Another variable we included that we thought would create an omitted variable bias is
unemployment rate. Many developed countries may have a comparable standard of living to
the United States, but lack of careers, jobs, and economic opportunities would push citizens
to move where they have a higher chance of economic prosperity. Countries like Spain
and Greece are developed countries with relatively high standards of living, but due to the
recession, also high levels of unemployment. Immigrants from countries like these would
create a bias in the flow of legal immigration as the difference in GDP may be small or
nonexistent, but flow of immigration would be relatively high. The possibility of correlation
between GDP per capita and unemployment rate stems from macroeconomic theory- more
people are hired in times of economic expansion and more people are fired in times of
economic recession. We expect the rate of unemployment to have a positive correlation with
the flow of legal immigration.
We included life expectancy as one of our variables due to the fact that life expectancy is
a good measurement of general health. If life expectancy is low, then it can be an indicator
of numerous problems such as disease, famine, or a lack of sufficient healthcare. All of these
5

6. factors can push citizens to leave the country in search of places that increase their chances
of living healthy, long lives. We predict that the lower the life expectancy of a country, the
higher the immigration to population ratio.
Finally, literacy rates are also an important variable we decided to include. Insuring our
children have the brightest futures possible is a key motivator for immigration, and crucial
in that endeavor is the need for our children to be educated. However literacy rates also can
be an indicator of the probability of a immigrant to be let into the United States, as it is
easier for an educated immigrant to enter the United States than an uneducated one. We
predict that literacy rates will have an positive correlation with immigration, as the lower
the literacy rate the more difficult it will be to enter the United States.
3 Empirical Results
The following table summarizes our Stata output, using four regressions:
1. restricted (R): Ii = β0 + β1Gi + ǫi and
2. partially restricted (R3): Ii = β0 + β1Gi + β4Ai + β5Hi + ǫi.
3. partially restricted (R5): Ii = β0 + β1Gi + β3Ei + β4Ai + β5Hi + β6Ui + ǫi.
4. unrestricted (UR): Ii = β0 + β1Gi + β2Li + β3Ei + β4Ai + β5Hi + β6Ui + ǫi.
The number in parenthesis below each coefficient denotes that coefficient’s standard error.
Dependent Variable Immigration
Population
(R) (R3) (R5) (UR)
GDP per capita Difference 2.05 ∗ 10−08
2.84 ∗ 10−08
** 3.42 ∗ 10−08
** 3.53 ∗ 10−08
**
(1.51 ∗ 10−08
) (1.36 ∗ 10−08
) (1.42 ∗ 10−08
) (1.44 ∗ 10−08
)
Homicide Rate 0.0000628*** 0.0000629*** 0.0000631***
(0.0000108) (0.0000107) (0.0000108)
Air Pollution -0.0000178*** -0.0000136*** -0.0000146***
(4.46 ∗ 10−06
) (4.97 ∗ 10−06
) (5.17 ∗ 10−6
)
Life Expectancy 0.0000457 0.0000626*
(0.0000282) (0.000036)
Unemployment Rate 0.0000341 0.0000286
(0.0000218) (0.0000231)
Literacy Rate -0.0000104
(0.0000137)
Intercept .0003918 0.000491 -0.0035546 -0.0038308
(.0006534) (0.0004569) (0.0024411) (0.0024761)
Observations 72 72 72 72
R2
0.0256 0.5431 0.5718 0.5755
F statistic 1.84 26.94 17.62 14.69
* Significance at 10% level
** Significance at 5% level
*** Significance at 1% level
6

7. Figure 1: A two-dimensional graph of the GDPs per capita (in USD) vs. the immigration
ratios. Note that, as we predicted, the correlation is weakened for the countries whose GDP
per capita is far lower than the US’.
Based on the data, a one dollar increase in the difference in GDP per capita results in
a 0.0000000353 increase in the immigration to population ratio- to put this into a real-life
perspective, this means that for a 1,000,000 population, a one thousand dollar increase would
increase the annual average number of people who immigrate to the US from that country by
35. Referring back to the hypothesis test, the F statistic for regression (R) is 1.84, which is
too insignificant to reject the null hypothesis. Purely based on this single variable regression,
it would appear that the GDP per capita difference is not a good regressor. However, when
comparing the other regressions to this one, the results look promising for this regressor: the
coefficient both increased and because significant at the 10% level (exact p-value was 0.017
in (UR)) after adding the other five variables. This suggests that the positive effect, though
small, on the immigration ratio, is present. Further evidence includes the (R3), (R5), and
(UR) regressions’ F statistics, which are all high enough to be considered significant, as well
as their R2
values. However, the low R2
value for the fully restricted regression suggests
that one of our other variables could better explain the variation in the immigration ratios.
Although, after adding homicide rate and air pollution, the coefficient and standard error
for this regressor changed little, suggesting that this regressor’s estimated coefficient in (UR)
are reliable.
Another possible explanation is illustrated by Figure 1, in which a clear positive correla-
tion can be seen from −0.5 ∗ 104
to 4 ∗ 104
, the region that includes all developed countries
and most developing countries in our sample. As we predicted in our hypothesis, this corre-
lation is substantially weakened in the 40000 and beyond range, which represents the third
world countries and exceptionally poor developing countries in our sample. The possible
reasons for this include refugee programs or the Diversity Visa Lottery (which gives away a
total of 50,000 visas annually to countries who send few people to the US) causing a higher-
than-predicted increase in immigration to the US, and, as we suggested in our introduction,
citizens of poorer countries having too few resources to immigrate. Had we chosen to exclude
7

8. third world countries from our regression, the GDP per capita difference would have been
a much closer fit (or so we predict). This would have resulted in a substantially higher R2
value for the fully restricted regression.
The variable with perhaps the strongest correlation is the intentional homicide rate- in
every regression on the table, its p-value remained close to zero (in all results, Stata displayed
the p-value as 0.000). Also, the regression (R2) (which used only GDP per capita difference,
homicide rate, and and intercept) was omitted from the table due to the table’s size, but its
R2
value was .4365, an increase of .4109 from the fully restricted regression. This suggests
that this variable explains a significant portion of the variation in immigration rates, so out
of all five extra variables, at least this one should be included. According to its coefficients,
an homicide rate by 1 results in a .0000631 increase in immigration to population ratio- for
a population of 1,000,000 and a given year, another death pushes an extra 63 people to the
US, on average.
Air pollution level had the second strongest correlation, according to p-values, which
remained below 0.01 for every regression on the table. The increase in R2
from (R2) to (R3)
(which added this variable to the regression) was .1066; this was not as much of an increase
as when we added homicide rate, but it is still large enough to warrant including this variable
to the regression. The only reason to be concerned over the reliability of the coefficient is
its standard error- it consistently increased with the addition of the other variables. The
coefficients for this regressor say a 1 ug
m3 increase in air pollution results in a 0.0000146
increase in the immigration to population ratio; for 1,000,000 people, that’s 14 on average
that move to the US. Our prediction of a positive correlation between pollution levels and
immigration was incorrect, most likely due to the fact that higher pollution levels tend to
accompany a presence of industry, which brings infrastructure and jobs to the economy.
Based on the data, life expectancy is positively correlated with the immigration to popu-
lation ratio, with a year increase resulting in a .0000626 increase in the ratio. However, this
regressor’s coefficient did not achieve a p-value below 0.1 until both unemployment and lit-
eracy rates were included in the regression. Also, from (R5) to (UR), the coefficient changed
relatively significantly, which suggests the possibility that this coefficient is not reliable.
According to the coefficients, a percent increase in unemployment rate results in a
.0000286 increase in the immigration to population ratio and a percent increase in literacy
rate results in a 0.0000104 decrease in the immigration to population ratio. Our prediction
of a positive correlation between literacy rate was incorrect, likely due to the fact that the
higher the literacy rate, the less incentive people have for leaving the country, as countries
with high literacy rates tend to have the infrastructure to support effective school systems
and are fulfilling a demand for skilled jobs. Since the inclusion of these two variables, along
with life expectancy, only increased R2
by 0.0324, there is little evidence to suggest we need
these variables to build an accurate model other than to eliminate omitted variable bias
resulting from them. Literacy and unemployment rates had the lowest p-values out of all
the regressors in (UR), and notice that the standard error for unemployment rates increased
with the addition of literacy rate.
8

9. 4 Conclusion
By itself, the difference in GDP per capita cannot accurately predict the proportion of people
from a country who immigrate to the US. However, when supplemented by other variables in
our regression, its coefficient becomes much more reliable. We found that the variables which
had the most significant effect on the immigration to population ratio, besides the GDP per
capita difference, were intentional homicide rate and air pollution level. In conclusion, we
find that the GDP per capita gap between an immigrants’ country of origin and the US is a
reliable predictor of the flow of immigration from that country to the United States.
5 References
1. Center for Immigration Studies. “The Fiscal and Economic Impact of Immigration on
the United States.” N.p., n.d. Web. 18 Oct. 2015. Retrieved from: http://cis.org/
node/4573.
2. Monger, R., Yankay, J. Department of Homeland Security. “US Lawful Permanent Res-
idents: 2013” Web. 15 Nov. 2015. Retrieved from: http://www.dhs.gov/sites/default/
files/publications/ois lpr fr 2013.pdf
3. Jefferys, K., Rytina, N. Department of Homeland Security. “US Legal Permanent Resi-
dents: 2005” Web. 20 Nov. 2015. Retrieved from: https://www.dhs.gov/sites/default/
files/publications/USLegalPermEst 5.pdf
4. Monger, R., Rytina, N. Department of Homeland Security. “US Lawful Permanent
Residents: 2008” Web. 20 Nov. 2015. Retrieved from: https://www.dhs.gov/xlibrary/
assets/statistics/publications/lpr fr 2008.pdf
5. Monger, R., Yankay, J. Department of Homeland Security. “US Lawful Permanent
Residents: 2010” Web. 20 Nov. 2015. Retrieved from: https://www.dhs.gov/xlibrary/
assets/statistics/publications/lpr fr 2010.pdf
6. Migration Policy Institute. “International Migrant Population by Country of Origin
and Destination” Web. 20 Nov. 2015. Retrieved from: http://www.migrationpolicy.
org/programs/data-hub/charts/international-migrant-population-country-origin-and-
destination
7. Price, Carmel E., and Ben Feldmeyer. “The Environmental Impact Of Immigration:
An Analysis Of The Effects Of Immigrant Concentration On Air Pollution Levels.”
Population Research And Policy Review 31.1 (2012): 119-140. EconLit with Full
Text. Web. 18 Oct. 2015.
8. Singh, Gopal K, and Robert A Hiatt. “Trends And Disparities In Socioeconomic And
Behavioural Characteristics, Life Expectancy, And Cause-Specific Mortality Of Native-
Born And Foreign-Born Populations In The United States, 1979-2003.” International
Journal Of Epidemiology 35.4 (2006): 903-919. MEDLINE Complete. Web. 18 Oct.
2015.
9

10. 9. UNdata. “Adult Literacy Rate” Web. 15 Nov. 2015. Retrieved from: http://data.un.
org/Data.aspx?d=SOWC&f=inID%3A74
10. United Nations Office of Drugs and Crime. “Homicide Counts and Rates (2000-2013)”
Web. 15 Nov. 2015. Retrieved from: https://data.unodc.org/#state:1
11. World Bank, The. “GDP (Current US$)” Web. 15 Nov. 2015. Retrieved from:
http://data.worldbank.org/indicator/NY.GDP.MKTP.CD
12. World Bank, The. “Life expectancy at birth, total (years)” Web. 15 Nov. 2015.
Retrieved from: http://data.worldbank.org/indicator/SP.DYN.LE00.IN
13. World Bank, The. “Population, total” Web. 15 Nov. 2015. Retrieved from: http://
data.worldbank.org/indicator/SP.POP.TOTL
14. World Bank, The. “Unemployment, total (% of total labor force) (modeled ILO esti-
mate)” Web. 15 Nov. 2015. Retrieved from: http://data.worldbank.org/indicator/SL.
UEM.TOTL.ZS
15. World Health Organization. “Ambient (outdoor) air pollution in cities database 2014”
Web. 20 Nov. 2015. Retrieved from: http://www.who.int/phe/health topics/
outdoorair/databases/cities/en/
10