May 09th, 2014

Who moves and why? Determinants of interstate migration in the United States using data from the 2011 American population survey.

Duc Trinh

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Abstract

Many empirical studies done in the past have tried to establish the relationship between the interstate

migration and separate, seemingly inrelated variables such as sex, ethnicity, marital status, etc, which

are believed to be the main contributors to an individual’s decision to move. In this paper, I included

many of same variables together in a bigger model and found out many of them to be correlated to each

other but finally reached quite similar findings with past research.

I. Introduction

The phenomenon of migration has always been an interesting topic for economists

and politicians alike due to its importance to the dynamics of the economy. By

understanding the patterns, sources and reasons behind the decision to move from one

state and another, policy makers could come up with sound plans to leverage on the

benefical effects of migration or negate many of its unfavourable consequences.

The primary objective of this paper is to study the roles of many variables that

could possibly determine interstate migration in America using data from the 2011

IPUMS USA survey database such as age, races, marital status and occupations. The

remainder of the paper proceeds as follows. Section II provides a brief survey of the

relevant literature and also a theoretical explanation for the accompanying empirical

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models. Section III describes and summarizes the data used. Section IV reports and

interprets empirical results. Finally, section V draws some conclusions and describes

possible future studies based on the shortcomings of the data I used in this paper.

II. Literature review and theoretical analysis

Many studies in the past has found correlation between migration and many

socio-demographic variables. I will attempt to summarize and review these studies

below in order to provide theoritical basis for my empirical models.

It is common sense to notice that migration tends to peak around the age of 25

to 30 during which young people transition into aduilthood. Several factors such as

career, relationship and personal preferences for moving definitely play an important

role in the increased mobility for this sector of population. Older people move less often

as they settle in their communities. Rogers (1988) theorizes that there is a consistent

pattern of elderly migration in many countries around the world and used mathematical

expressions called “model schedule” in his paper to summarize out these regularities. By

fitting these immigration into observed data, Rogers found out that there are 4 peaks of

migration during the typical life cycle of a person. The first migration peak occurs

around age 16 when teens move away from parental home due to job shifts, household

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formation, marriage and transition into military or university. The second is around age

60 when people move away from family home due to housing related reasons. The last

peak of migration occurs at the age of 75 when older people move away from

retirement house due into a dependent status as they become more prone to illness of

old age. McInnis(1971) estimated a model of migration by means of linear regression for

age groups, occupation and education for males in Canada. He stated that the younger

group of migrants (between the age of 20-24) is most responsive to regional difference

in earnings. They move to places where there are better economic opportunities. For

older age group, the earning differentials between regions do not really affect

migration. Sandefur (1985) investigated the variations in migration of men during early

stages of life cycle defined by jobs, marriage and childbearing. He concluded that there

is a general inversed relationship between age and migration.

The race factor definitely affects the decisions of the individual to move. Wilson

et al (2009) tried to systematically compare the likelihood of return migration between

people from different racial groups and found out that there is a higher odds of return

migration for Hispanics and Blacks than for Whites. These return migration differentials

could be explained by established ethnic communities and also the distribution of initial

settlements.

Although not normally linked with migration, veteran status probably influences

the decision to move for many people. People on duty might have to move more

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frequently due to requirements of their jobs and this may affects their decision after

they retire from the armed forces. The required relocation could reduce the cost of

gathering information about new destinations and lessen the tie with initial home

communities. Military services also bring a diverse set of connection for the veterans

with people from vastly different geographical origins. Using 1% sample from the U.S

population cencus from1940 to 2000, Bailey(2011) concludes that veterans of both

Black and White are significantly more like to have recently moved compared to

nonveterans.

Sandefur (1985) used the model of life cycles with three typical stages: a single

stage of never-married and divorced men, the second stage of married men with no

children and a third stage of married men and with childen. He found out that the

probabilty of of migration decreases more rapidly over time for married males with

childen for singled males, which could be a direct result of strengthened ties with local

communities after having childeren over the years. Speare(1987) attemped to quantify

the effect of life cycle changes, namely ,marital status change, on the probabilty of

residetial mobility. Running logistic regressions on the panal data of Rhole Island adults

from 1967 and 1979, Speare found out that mobility rates are highest for the newly

married and for the separated and divorced but lowest for the widowed. However,

many people do not immediaty move the during the year of martial status change and

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the increased probablity of moving last for several years after the change in marital

status happen.

It is very important to take into account the effect of the individuals’

metropolitan status and types of migration destination as those variables certainly

affect their decision to move. It is generally accepted that people from rural areas,

especially younger ones, often migrates from their original places to bigger metropolitan

areas due to better economic prospects. However, in their study, Mills et al(2001) found

out that non-metropolitan counties are attractive alternative destinations for educated

non-metropolitan youth due to high returns to schooling. These findings could be

explained by lower cost of living and area specific amenity values.

The type of careers one is in and the industry one is employed in are generally

believed to determine the mobility of that person. For example, one who works in the

tourism industry would necessarily has to move a lot more than one who works in the

manufacturing industries due to the nature of their work. The employment status of

individuals also definitely affect their migration pattern. One might not be able to find a

job in his current community and might needs to migrate to another state in order to

find jobs.

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Based on the literature review, I believe all the variable mentioned in the studies

done above should be incorporated into the model, namely age, sex, education, race,

employment status, veteran status, marital status, career, industries, occupations and

metropolitan status. My econometrics regression models are describe by the equation

below:

Yi= βXi+ ε

Where Y is the dichotomous dependent variable moved (where 1 indicates the

person has moved since last year and 0 indicates no change in living place) and Xs are

the independent variables that I plan to run regressions on. ε is the error term that

contains omitted variables that have not been included in the data and represents the

results of a chance process. This model adheres to the classical econometrics model in

which the error terms are randomly drawn from a normally distributed box of tickets with

average of 0 and unknown, constant SD. The errors are also independent of each other

and independent of the Xs. The Xs includes age, sex, education, race, employment

status, veteran status, marital status, career, industries, occupations and metropolitan

status and are fixed in repeated samplings.

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III. Data and Measurement

The data are cross sectional and were collected from the IPUMS USA current

population survey in 2011. By generating new variables, I have transformed most of the

continuous variables into dummy variables for ease of interpretation. I also dropped

some “Not Availabe” observations and reduced the total size of the data set. All

descriptions and summary statistic of the variables are described in the tables below.

Variables Descriptionage Age of the person in the surveyincwage Income wage of the person in the surveyage16 If the person is 16 years old, age16=1.If not, age16=0 age60 If the person is 60 years old, age60=1.If not, age60=0age75 If the person is 75 years old, age75=1.If not, age75=0single If the person is single, single=1.If not, single=0married If the person is married, married=1.If not, married=0white If the person is white, white=1.If not, white=0black If the person is black, black=1.If not, black=0employed If the person is employed, employed=1.If not, employed=0unemployed If the person is unemployed, unemployed=1.If not, unemployed=0recreational If the person is working in the recreational industry, recreational=1.If not, recreational=0manufacturing If the person is working in the manufacturing industry, manufacturing=1.If not, manufacturing=0metrostatus If the person is lived in a metrololitian area 1 year ago, metrostatus=1.If not, metrostatus=0movedin9 If the person has lived in the current residence for fewer than 10 years, movedin9=1.If not, movedin9=0veteran If the person is a veteran, veteran=1.If not, veteran=0male If the person is a male, male=1.If not, male=0moved this is the dependent variable, if the person has moved outside of state since last year, moved =1, if not, moved =0

Table 1: Description of variables

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male 173815 .9467192 .2245935 0 1 moved 173815 .0041078 .0639607 0 1 veteran 173815 .0943647 .2923363 0 1 movedin9 173815 .2432011 .4290168 0 1 metrostatus 173815 .041222 .1988038 0 1manufactur~g 173815 .0810747 .2729506 0 1recreational 173815 .002589 .0508161 0 1 unemployed 173815 .5757156 .4942353 0 1 employed 173815 .4242844 .4942353 0 1 black 173815 .0848028 .2785888 0 1 white 173815 .8724736 .3335627 0 1 married 173815 .6749187 .4684066 0 1 single 173815 .071484 .2576324 0 1 age75 173815 .0207002 .142379 0 1 age60 173815 .0226735 .1488609 0 1 age16 173815 0 0 0 0 incwage 173815 23698.49 44224.33 0 607000 age 173815 63.87702 15.47354 18 95 Variable Obs Mean Std. Dev. Min Max

Table 2: Variables summary statistics

IV. Results and Interpretation

The tables below display linear probabilty model and dprobit regression results on

different empirical models:

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* p<0.05, ** p<0.01, *** p<0.001 (0.00) (0.00) (0.00) (0.00) Constant 0.029*** 0.032*** 0.033*** 0.010*** (0.00) veteran 0.023*** (0.00) movedin9 0.007*** (0.00) metrostatus -0.016*** (0.00) (0.00) manufacturing -0.005*** -0.003*** (0.00) (0.00) recreational -0.002 -0.001 (0.00) (0.00) unemployed 0.001** 0.000 (.) (.) employed 0.000 0.000 (0.00) (0.00) (0.00) male -0.000 -0.000 0.001 (0.00) (0.00) (0.00) black -0.003*** -0.003*** -0.002* (0.00) (0.00) (0.00) white -0.003*** -0.003*** -0.003*** (0.00) (0.00) (0.00) (0.00) married -0.001 -0.000 -0.000 -0.000 (0.00) (0.00) (0.00) (0.00) single 0.000 0.000 0.000 -0.000 (0.00) (0.00) (0.00) (0.00) age75 0.001 0.001 0.000 -0.000 (0.00) (0.00) (0.00) (0.00) age60 -0.003** -0.003** -0.003* 0.000 (.) (.) (.) (.) age16 0.000 0.000 0.000 0.000 (0.00) (0.00) (0.00) (0.00) Wage and salary in~e -0.000* -0.000* 0.000 -0.000 (0.00) (0.00) (0.00) (0.00) Age -0.000*** -0.000*** -0.000*** -0.000*** b/se b/se b/se b/se m1 m2 m3 m4

Table 3: LPM regression output

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* p<0.05, ** p<0.01, *** p<0.001 (0.06) (0.08) (0.08) (0.12) Constant -1.114*** -0.993*** -0.986*** -2.250*** (0.04) veteran 0.661*** (0.04) movedin9 0.401*** (0.04) employed -0.041 (0.07) (0.07) manufacturing -0.415*** -0.321*** (0.35) (0.35) recreational -0.165 -0.076 (0.04) unemployed 0.061 (0.05) (0.05) (0.05) male 0.021 0.039 0.047 (0.06) (0.06) (0.07) black -0.109 -0.106 -0.100 (0.05) (0.05) (0.05) white -0.175*** -0.169*** -0.176*** (0.03) (0.03) (0.03) (0.04) married -0.056 -0.053 -0.053 -0.068 (0.05) (0.05) (0.05) (0.05) single -0.081 -0.084 -0.087 -0.076 (0.20) (0.20) (0.20) (0.21) age75 -0.036 -0.032 -0.060 -0.160 (0.10) (0.10) (0.10) (0.11) age60 -0.007 -0.006 0.012 0.117 (0.00) (0.00) (0.00) (0.00) Wage and salary in~e 0.000 0.000 0.000* 0.000 (0.00) (0.00) (0.00) (0.00) Age -0.028*** -0.027*** -0.028*** -0.010*** b/se b/se b/se b/se m5 m6 m7 m8

Table 4: dprobit regression output

We can see from the output tables that the coefficients obtained from LPM

regressions are quite different from coefficients obtained from the dprobit regressions.

A possible reason for this inconsistency is that LPM probably suffers from

heteroskedasticity. I ran a Breusch-Pagan test for model 4 (the most conclusive model)

after running a LPM regression to check whether there is heteroskedasticity.

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Prob > chi2 = 0.0000 chi2(1) =397340.90

Variables: fitted values of moved Ho: Constant varianceBreusch-Pagan / Cook-Weisberg test for heteroskedasticity

. estat hettest

_cons .0101794 .0013276 7.67 0.000 .0075774 .0127815 veteran .0228416 .0006482 35.24 0.000 .0215711 .024112 movedin9 .0065748 .0004093 16.06 0.000 .0057726 .007377 metrostatus -.0162115 .0008089 -20.04 0.000 -.0177969 -.0146261manufactur~g -.0028938 .0005695 -5.08 0.000 -.00401 -.0017776recreational -.0008403 .0029913 -0.28 0.779 -.0067033 .0050226 unemployed .0004402 .0004296 1.02 0.306 -.0004018 .0012822 employed (dropped) male .0007102 .0007029 1.01 0.312 -.0006674 .0020878 black -.0022841 .0009031 -2.53 0.011 -.004054 -.0005141 white -.0029439 .0007578 -3.88 0.000 -.0044293 -.0014586 married -.0004475 .0003644 -1.23 0.219 -.0011617 .0002666 single -.0002733 .0006588 -0.41 0.678 -.0015645 .001018 age75 -.0000567 .0010739 -0.05 0.958 -.0021616 .0020481 age60 .0000869 .0010257 0.08 0.932 -.0019234 .0020972 age16 (dropped) incwage -6.64e-10 4.23e-09 -0.16 0.875 -8.95e-09 7.63e-09 age -.0001056 .000015 -7.03 0.000 -.0001351 -.0000762 moved Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 711.06702173814 .004090965 Root MSE = .06334 Adj R-squared = 0.0194 Residual 697.208397173799 .004011579 R-squared = 0.0195 Model 13.8586227 15 .923908181 Prob > F = 0.0000 F( 15,173799) = 230.31 Source SS df MS Number of obs = 173815

. reg moved age incwage age16 age60 age75 single married white black male employed unemployed recreational manufacturing metrostatus movedin9 veteran

Table 5: Breusch-Pagan test for LMP model

The result is extremely statistically significant, which means that

heteroskedasticity is present in the data. I fixed for that using robust SEs and obtained

the new output table for LPM regression.

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* p<0.05, ** p<0.01, *** p<0.001 (0.00) (0.00) (0.00) (0.00) Constant 0.029*** 0.032*** 0.033*** 0.010*** (0.00) veteran 0.023*** (0.00) movedin9 0.007*** (0.00) metrostatus -0.016*** (0.00) (0.00) manufacturing -0.005*** -0.003*** (0.00) (0.00) recreational -0.002 -0.001 (0.00) (0.00) unemployed 0.001** 0.000 (.) (.) employed 0.000 0.000 (0.00) (0.00) (0.00) male -0.000 -0.000 0.001 (0.00) (0.00) (0.00) black -0.003* -0.003* -0.002 (0.00) (0.00) (0.00) white -0.003** -0.003** -0.003* (0.00) (0.00) (0.00) (0.00) married -0.001 -0.000 -0.000 -0.000 (0.00) (0.00) (0.00) (0.00) single 0.000 0.000 0.000 -0.000 (0.00) (0.00) (0.00) (0.00) age75 0.001 0.001 0.000 -0.000 (0.00) (0.00) (0.00) (0.00) age60 -0.003*** -0.003*** -0.003** 0.000 (.) (.) (.) (.) age16 0.000 0.000 0.000 0.000 (0.00) (0.00) (0.00) (0.00) Wage and salary in~e -0.000 -0.000 0.000 -0.000 (0.00) (0.00) (0.00) (0.00) Age -0.000*** -0.000*** -0.000*** -0.000*** b/se b/se b/se b/se m1 m2 m3 m4

Table 6: Robust SE of LMP regressions

The results obtained after using Robust SEs are radically different from before.

We can see that people at the age of 60 have a 0.3% decrease in the probability of

migration in the first there models (Table 6). However, the same age group do not

produce a statistically significant result in the last model. Being black also leads to a

0.3% decrease in the probability of migration in the second and third models but not for

the last models. Being unemployed leads to a 0.1% increase in the probability of

migration in the third models but not for the last models. We generally see less

statistically significant results for the same variables as we incorporate more variables

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into the model. This could be to the problem of multicollinearity in which the variable

age60 is correlated with other variables in model 4. Therefore, I ran a regression of

age60, black and unemployed on the other new variables that are only present in model

4 and got the following result:

_cons .0244591 .0004129 59.24 0.000 .0236498 .0252684 veteran -.0212708 .0013059 -16.29 0.000 -.0238303 -.0187114 movedin9 .0012249 .0009233 1.33 0.185 -.0005847 .0030345 metrostatus -.0018493 .0018959 -0.98 0.329 -.0055652 .0018667 age60 Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 3851.64361173814 .022159571 Root MSE = .14874 Adj R-squared = 0.0017 Residual 3845.13077173811 .022122482 R-squared = 0.0017 Model 6.51283811 3 2.17094604 Prob > F = 0.0000 F( 3,173811) = 98.13 Source SS df MS Number of obs = 173815

Table 7: regression of age60 on metrostatus, movedin9 and veteran

There is a statistically significant direct relationship between being at the age of

60 and being a veteran (Table 7). A person reduces her probability of being 60 years old

by 2.1% when she is a veteran.

_cons .0733674 .0007713 95.12 0.000 .0718555 .0748792 veteran .0235208 .0024395 9.64 0.000 .0187395 .0283022 movedin9 .0335476 .0017248 19.45 0.000 .0301671 .0369281 metrostatus .0256438 .0035418 7.24 0.000 .0187019 .0325856 black Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 13490.0066173814 .077611738 Root MSE = .27786 Adj R-squared = 0.0052 Residual 13419.0813173811 .077205017 R-squared = 0.0053 Model 70.9253441 3 23.6417814 Prob > F = 0.0000 F( 3,173811) = 306.22 Source SS df MS Number of obs = 173815

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_cons .6403107 .0013098 488.87 0.000 .6377436 .6428778 veteran -.4365441 .0041423 -105.39 0.000 -.4446629 -.4284252 movedin9 -.0963819 .0029287 -32.91 0.000 -.1021221 -.0906417 metrostatus .0009567 .006014 0.16 0.874 -.0108306 .0127441 unemployed Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 42457.2954173814 .244268559 Root MSE = .47181 Adj R-squared = 0.0887 Residual 38690.771173811 .222602545 R-squared = 0.0887 Model 3766.52443 3 1255.50814 Prob > F = 0.0000 F( 3,173811) = 5640.13 Source SS df MS Number of obs = 173815

Table 8: regression of black and unemployed on metrostatus, movedin9 and veteran

We can see that the variables black and unemployed also sufffer from the same

multicollinearity problem. They are directly correlated with the ommitted variable and

therefore violate the basic rules of the Classical Econometrics Model.

Dprobit regressions (Table 4) would probably do a better job at getting the

correct estimation and therefore I will interpret the results using the dprobit regression

outputs. We see that the marginal effect of increasing age by one would increase the

probability of migration by 1% (this result is very exact because the SE is very small

compared the statistically significant estimated coefficient). Being white and working in

manufacturing industries in a metropolitan area during last year reduce the probability

of moving while being a veteran and having lived in the current residency increase the

chance of moving to another state.

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V. Conclusion

The results obtained from my empirical analysis very much agree with the

common sense knowledge and the result fromother studies done in the past. White,

older people who lives in a metropolitan area and works in a manufacturing industry

tend to move less and veterans tend to move more. Although my data and analysis are

certainly far from complete, the conclusion one draws from this study could potentially

help create better immigration policies that benefit the economy as a whole. Future

research could be improved by using a panel or time series data set and include more

variables such as education, occupation, distance and economic conditions of different

states in order to draw a clearer conclusion about the determinants of migration.

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References

Alden Speare, Jr. and Frances Kobrin Goldscheider. 1987. "Effects of Marital Status Change on Residential Mobility." Journal of Marriage and Family 455-464.

Bailey, Amy Kate. 2011. "Race, Place, and Veteran Status: Migration among Black and White Men, 1940-2000." Population Research and Policy Review 701-728.

Beth A. Wilson, E. Helen Berry, Michael B. Toney, Young-Taek Kim and John B. Cromartie. 2009. "A Panel Based Analysis of the Effects of Race/ Ethnicity and Other Individual Level Characterisitics at Leaving on Returning." Population Research and Policy Review 405-428.

Hazarika, Bradford Mills and Gautam. 2001. "The Migration of Young Adults from non-Metropolitan Counties." American Journal of Agricultural Economics 329-340.

McInnis, Marvin. 1971. "Age. Education and Occupation Differentials in Interregional Migration: Some Evidence for Canada." Demography 195-204.

Rogers, Andrei. 1988. "Age Patterns of Elderly Migration: An International Comparion." Demography 355-370.

Sandefur, Gary D. 1985. "Variations in Interstate Migration of Men Across the Early Stages of life Cycle." Demography 353-366.