Net Migration Rates of College Students

Student’s Name
Professor
Course
Date
A Regional Study of Net Migration Rates of College Students
Abstract
This research paper undertakes to establish and synthesize data on net migration of college students among various universities. The net migration among students is observed to correlate positively to several factors that are directly involved in the education sector. The correlation of college migration is positive to the availability of more universities, the level of education among the given students, the employment growth rate among the population, employability of graduates from a given university and the expenditure of college students. However in the contrary there has not been observed any correlation between college student’s migration due to taxation burdens, per capita incomes among the students or crime rates.






Introduction
Background Information
The research on migration of college students is less understood with little interest into researchers. However it may be noted that the dynamics if a student migration is not only felt by colleges alone but more likely the student and the community around feel the hitch of such actions. Student may opt to immigrate emigrate from colleges for various reasons. It may be noted from the report discussed herein that the net migration was considered factoring the net effect since by considering individual migrations then it would be hard to deduce the correlation of the migration to the net effect (Ishitani 32).
A large number of students migrating out of a given college may indicate an underutilization of resources and the consequent loss of revenue within the university (Alm 43). The capability of universities to establish infrastructure is well related to the capacity within the colleges thus it can be deduced that a net migration which is excessive will lead to a reduction in income from within the college themselves and may lead to loss of livelihoods within the neighborhoods of the given universities (Koyama and Subramanian 123). This is reflected through lower earning potential from surrounding business which thrives to supply goods and services to the student population of the given colleges (Orsuwan 321).
Literature Review
The data discussed herein is derived from colleges within the different states of the United States of America. The table therefore factors in the net migration levels for the newly enrolled and undergraduate students. Some states recorded few cases for net migration as a result of restrictions hence could not be used to deduce the correlation between student’s migration and some parameters. These states were Californian and New York. The financial implications of migrating in an out of the states could not be directly established. However it can be realized that the governments there face some financial issues owing to the loss of revenue or increase in revenues as out of state students usually pay higher as opposed to in state students (Guthrie 145) (Koyama and Subramanian 34).  The action of students to migrate to different colleges may be to search for better attributes or as a cost costing measure. Since in state students are likely to pay less fees than most students would try as much as they can to move to colleges within their state (Kyung 178). As such it can be noted that out of state students incur expenses that are additional as compared to other students (Koyama and Subramanian 76). The social attachment of being at home may encourage students to move to colleges that are closer to their homes (Ishitani 223).  However it may be noted that incentives like student’s bursaries and other financial aids among colleges will definitely attract students within a given college and usually poach such students from colleges that do not offer such services. Therefore it can be deduced that apart from social reasons that the decisions to move in or out of a given college is most likely due to a cost benefit analysis that is meant to work positively for the students involved (Guthrie 126).
METHODS
Model Description
A report by Petersons guide to American colleges indicated that out of state students always consider many parameters on their decisions towards migrating to or out of a college based on several factors. First they were cautious of the institution sir they were to join. They never considered the class size as the highest factor since most never consider class size related to quality of education being offered, they were considerate of interfaculty credit transfers since colleges that did not have credit transfers lost the chance of joining students who were not first years (Fahrmeir 145). The majority of the migrating population is considerate of the university performance, with most considering the global image and the rates of employability from given universities they thus considered some colleges to others thus increasing the migration into bigger universities noted to be higher than the others (Morgan 254). The student selectivity as among many reasons unsubstantiated since most students had other carried reason to join the universities from different regions apart from the ones within their states.
The studies discussed in this paper thus factored the regression models where a theoretical model must factor in all possible dependent variables that contribute to the migration of students.

The model
Net immigration= F (enrollment rates)
Where enrollment rates were autonomous functions of colleges;
Net immigration = 682 + e 0.0834Log e
Where e = enrollment
a0
682.5380344

a1
0.0834Log e





Results and discussion
From the simulation model was established;



Simulation worksheet of net migration against University enrollment

net migration NET
University enrollment, in 000's
Cumulative probability

5322
350
0.005

-994
735
0.01

3202
1120
0.015

613
1505
0.02

2254
1890
0.025

3742
2275
0.03

-4862
2660
0.035

1395
3045
0.04

2796
3430
0.045

771
3815
0.05

-199
4200
0.055

1621
4585
0.062

-8176
4970
0.069

5388
5355
0.076

4021
5740
0.083

2342
6125
0.09

1843
6510
0.097

1634
6895
0.104

-1294
7280
0.112

-3026
7665
0.12

8452
8050
0.128

-999
8435
0.136

-618
8820
0.144

1670
9205
0.152

2446
9590
0.16

-223
9975
0.168

-37
10360
0.176

-605
10745
0.184

1989
11130
0.192

-19820
11515
0.202

-315
11900
0.212

-3217
12285
0.222

9078
12670
0.232

1584
13055
0.242

1713
13440
0.262

344
13825
0.282

1380
14210
0.302

6529
14595
0.322

4767
14980
0.342

1173
15365
0.372

400
15750
0.402

1713
16135
0.44

1038
16520
0.48

4605
16905
0.53

1796
17290
0.59

3806
17675
0.66

2348
18060
0.74

1938
18445
0.82

1050
18830
0.9

-441
19,175
1


a0
682.5380344

a1
0.036415



A sensitivity analysis of this model indicates that net migration of college students is highly responsive to the enrollment rates.
Considering that all parameters under consideration that affected migration into and out of universities, it can be noted that on average a net 682 students could be migrating in college. This can be attributed to some factors that cannot be accounted in the report and as a result always some errors are accounted for in any model. It can be understood that the human mind is complex and it may be hard to model a perfect relationship between factors that lead to migration. The aspects under consideration in the colleges mostly was their global rating on performance, however it may be noted that some students may consider the class size or the course they are taking. However this was not given much priority since it was assumed that universities in every state had the necessary parameters being similar in these attributes. All states had universities with varying class size thus this attribute was less weighted on its effect on the decision of a college student to migrate. Secondly, every state being regulated by a common national government, it was established that similar facilities could be accessed across almost all states (Hsing 187). There were no high variances in the courses offered in different states and it was thus assumed that this factor would not majorly influence the migration of students within different states.
From the results it can be interpreted that a change in one unit of university enrollment there would be 682+ university enrollment × coefficients to give student migration. The coefficient is 0.036; therefore this is insignificant since it cannot be rounded off to a whole number. It can thus be interpreted that there is little or no correlation between the numbers of university enrollment to the number of net migration of students. This can be attributed to consumer preferences not to factor in class sizes. This can also deduce that the college enrollment of a given institution dos not relate to other factors that showed a positive correlation. Most students will not consider the colleges they migrate to on the basis of college population since they believe that quality offered may not be correlated to the population of given institution. As a result we can see that a one unit of university enrollment gave an insignificant result thus can be considered as a major factor towards the migration of students in and out of state colleges. However this report may not be used in some countries where high population of college enrollment is indicative of low tuition fees or a higher quality of education (Kyung 276). This can be deduced as independent consumer choice based on other factors other than the total population. Some states with university enrollment of over 213,000 students were observed in the data while others can be seen to be a mere thousand. This can indicate future trends in the ranking of various among universities such as the correlation of tuition fees to university total enrollment, it can be deduced that in the United States of America that such a correlation generally lacks (Orsuwan 182).
From the table, it can be seen that for every increase in one unit of tuition fees there was an increment in -8.40380782484872 in the net immigration. This is indicative of the judgment decisions of the in-state and out of state immigrants who are highly responsive to the tuition fees charged by the colleges. As such it can be deduced that an increase in fees is indicative of a negative correlation. Keeping other factors constant, the number of students migrating to colleges was negative for any unit of fees increment. This in reality affects most education systems within the world thus the planning bodies can use this parameter to control the rate of net migration within universities. However it may be remembered that all other factors being kept constant that 50 students migrated without considering the factors under this study.
The processed data indicates that for every one unit increase in the per capita income of a student keeping all other factors constant resulted in higher probability of migrations. This means that for every one unit increase in the per capita income, 9 college students migrated. This is a positive correlation indicating that student’s migration to different universities is related to their per capita incomes. As such students with a higher per capita income are likely to migrate to different universities. This can be attributed to a purchasing power where in every income increase there is a consequent increase in the purchasing power and a consequent consumer choice (Ward and Gleditsch 76) (Morgan 67). Most students with a high per capita income will find it easy to migrate to anew college since they can afford to foot the expenses and transportation costs. However students may have higher per capita incomes, the choice of the college was not established in this report. It can however be deduced that students with a higher per capita income are more likely to make independent choices without constraints such as costs like tuition fees and transportation costs.
The unit parameter of expenditure per student resulted in 1 unit increase in the net immigration per student. This is a positive correlation indicating that students preferred colleges that had higher expenditures. This was out of my expectation since I would expect students to be cautious of the expenses incurred. This can however happen if some other aspects of consideration compelled students to accept colleges with higher expenditures. This can be assumed for the fact that colleges with higher student’s expenditure may be indicative of the quality of education being higher or an access to better services. Where cost is not a factor of major consideration, quality is considered and as such, assuming that the average American students can afford a basic living, and then there is a likelihood of quality being considered other than costs (Smith 342). The universities that are found in cities may result in higher expenditures for students, however it can be deduced that some students may consider moving to such colleges other than the ones in remote locations. By doing so the price paid is living under higher expense, however this is common where consumer priorities plays a key role in consumer choice.
A growth rate of employment in a given rate had a positive correlation with every unit of growth rate increase leading to a higher unit increase in the net migration. This is positive indicative of the desire for students to prefer to move to states that have higher growth rates hoping for employment thereafter. The job market in such states is able to absorb college graduates thus a majority of students may have opted to consider this factor aiming to be retained in the state colleges in their job market. This data can be deduced that migrating student’s numbers can easily be influenced by the chances of being employed thereafter. This can also be deduced that students will find it easy to get a job in a state where they have studied their college education in. such students may however be subject to regulations that may restrict them to join the given state’s job market and in such a scenario, such students may migrate owing to availability of jobs within their study sessions such as work study programs. Study programs among states with higher employment rates are more likely to offer more jobs to students and hence such colleges within such a state are more likely to face an influx of new migrant students (Smith 54).
The employability rates of alumni from different universities were positively correlated with college immigration rate. However this rate was insignificant indicating that few students lay emphasis on such records. Despite the correlation being relatively insignificant, it can be deduced that students preferred colleges with higher employability rates.
The availability of financial aid to students had a positive correlation to the net migration indicating that students favored to move to colleges where they received financial aid. This is true for many setups where a section of students may not have the resources to finance their education in the long run. As a result such students may opt to colleges with financial aid like bursaries, higher education loans board and government grants. For every single unit in financial aid, there was a net increase in five students in college.
Conclusion
This model was indicative of the scenario observed in American universities. All the data tabulated tallied to my expectations as indicated in the data analysis section. To sum up it can be concluded that some students will migrate to in state or out of state universities without considering the tuition fees or living expenses. However it can be concluded that per capita income; financial aid and tuition fees in colleges affect student migration to a greater extent.
Recommendations
I would recommend future studies to take into account several factors that were not included in this report. I would like the government regulations to be considered in the net migration of students.
Future Work
This study would be done in a more comprehensive way if more time was granted. The correlation among the parameters used in this study was not carried to obtain any data. A similar study would be better in data interpretation if it considered the interrelations among the parameters such as the correlation among students considering tuition fees important to those considering their per capita income in their decisions to migrate to different universities.
















Works Cited

"The Condition of Education - Elementary and Secondary Education - Student Effort, Persistence and Progress - Status Dropout Rates - Indicator April (2015)." National Center for Education Statistics (NCES) Home Page, a Part of the U.S. Department of Education. N.p., n.d. Web. 22 Apr. 2016.
Alm, James, and John V. Winters. "Economics of Education Review." Distance and intrastate college student (2009): 728.
Fahrmeir, L. Regression : models, methods and applications. Berlin ; New York: Springer publishers, 2013.
Guthrie, James W. Modern education finance and policy. Boston: Pearson/Allyn and Bacon, 2007.
Hawley, Zackary B. "The Case of State Funded Higher Education Scholarship ." Regional Science and (2013): 675.
Hsing, Yu. "A Regional Study of Net Migration Rates Of College Students." The southern regional science association (1996): 199.
Ishitani, Terry T. "Research in Higher Education ." The Determinants of Out-migration Among In-state College (2011): 122.
Koyama, Jill and Mathangi Subramanian. US Education in a World of Migration : Implications for Policy and Practice. Hoboken: Taylor and Francis, 2014.
Kyung, Wonseon. "The Journal of Higher Education." In-migration of College Students to the State of New (1996): 358.
Morgan, James N. "Tuition Policy and the Interstate Migration of College ." Research in Higher Education (1983): 183-195.
Orsuwan, Meechai, and Ronald H. Heck. "Merit-based Student Aid and Freshman ." Research in Higher Education (2009): 24-51.
Smith, Edwin R., and Kathleen K. Bissonnette. "The Economic Impact of Nonresident Students on West Virginias Economy." Research in Higher Education (1989): 229-238.
Steahr, Thomas E., and Calvin F. Schmid. "College Student Migration in the United States." The Journal of Higher Education (1972): 441-463.
Thomas J., and Paul Boyle. The Migration of High School Graduates to . new york: routledge, 2011.
Ward, Michael Don and Kristian Skrede Gleditsch. Spatial regression models. Thousand Oaks: Sage Publications, 2008.