Paper Title :
A MetaAnalysis Of The Impact Of Education On Migration
Abstract
This paper represents a clear departure from the
migration literature and a first attempt in applying a metaanalysis
on the impact of education on migration behaviour. A threepart
metaanalysis is applied on twentytwo rigorously selected studies
examining education as one of the determinants of migration. The
first part, the standard metaanalysis, finds that most of the effect
sizes or the impact of the education variable to bepositive, i.e.
indicating that the more educated tend towards migrating. The
summary impact of the education coefficient estimate is found to have
a magnitude of approximately +0.300. The second part of the metaanalysis checks for publication bias; formal tests suggest no evidence
of such bias in our metaanalysis. In the final part, a metaregression
is used to account for the source of heterogeneity in the coefficient
estimates between studies, in which six of the study characteristics
are found significant.
Author:
JanJan Soon
Department of Economics & Agribusiness
Universiti Utara Malaysia
Sintok, Malaysia
Paper Transcript of Paper Titled :
A MetaAnalysis Of The Impact Of Education On Migration
A MetaAnalysis Of The Impact Of Education On Migration
JanJan Soon
Department of Economics & Agribusiness
Universiti Utara Malaysia
Sintok, Malaysia
Abstract— This paper represents a clear departure from the migration literature and a first attempt in applying a metaanalysis on the impact of education on migration behaviour. A threepart metaanalysis is applied on twentytwo rigorously selected studies examining education as one of the determinants of migration. The first part, the standard metaanalysis, finds that most of the effect sizes or the impact of the education variable to be positive, i.e. indicating that the more educated tend towards migrating. The summary impact of the education coefficient estimate is found to have a magnitude of approximately +0.300. The second part of the metaanalysis checks for publication bias; formal tests suggest no evidence of such bias in our metaanalysis. In the final part, a metaregression is used to account for the source of heterogeneity in the coefficient estimates between studies, in which six of the study characteristics are found significant.
Keywords skilled migration; education; metaanalysis
Introduction
Education has often been singled out as the predominant determinant of migration. Education is almost always included as one of the regressors when explaining what determines migration, regardless if the issue is about skilled or the more general type of migration such as regional or ruralurban migration. Although a relatively large proportion of studies conclude that education has a significant and positive impact on migration, there appears to be no consensus in the literature on the magnitude (i.e. size of the coefficient estimate) and the direction (i.e. coefficient sign) of the impact. Some studies even find education to be statistically insignificant in determining migration.
Until recently, studies on the determinants of migration typically review the literature narratively and manipulate arguments subjectively to suit the stance of the studies. A metaanalysis is a quantitative literature review, where the literature is reviewed in a methodologically rigorous way with formal hypothesis tests and statistics to support the review. The objective of a metaanalysis is not to discredit certain studies (e.g. studies with unconventional negative coefficient of the education variable), but rather to integrate and synthesize the many oftencontradicting findings and conclusions from the mushrooming literature.
Metaanalysis has its roots in the field of educational research, in which Glass [1, 2] is credited as the pioneer of this analytical approach. Metaanalysis is used in this field to assess, among others, the impact of teachers’ qualifications on students’ performance, and the types of learning intervention on examination scores. The metaanalysis approach gradually permeates into other fields such as psychology, biomedical science, pharmacology, ecology, criminology, and business. The breakthrough of metaanalysis into the field of economics is made through the seminal article in [3]. They went on to produce the first application of metaanalysis within economics in their study on the unionnonunion wage gap [4]. Following this, metaanalysis has been adopted in economic subfields such as education, labour, transportation, urban, and recreational economics.
In migration economics, metaanalysis has been applied mainly on the impact of migration – its impact on wages [5, 6], on employment [7], on income [8], and on international trade [9]. This paper looks at the other side of the coin, i.e. what determines migration. More specifically, this paper looks at how education impacts migration (both general and skilled migration). In the literature of migration economics, education plays a crucial role on migration behaviour; the bettereducated appear to be more mobile than their lesseducated counterparts.
There are, however, a number of studies that suggest otherwise, as revealed by the negative coefficient sign of the education variable [10, 11, 12, 13]. Such inconsistencies in individual studies may be due to either (i) real differences in how the education variable can impact migration, or (ii) differences in the characteristics of the migration studies. To date, there are yet to be any metaanalyses examining this issue – a gap this study is filling.
Methodology
Methodology Setup
The primary objective of a metaanalysis is to synthesize the often individually inconclusive and seemingly irreconcilable results of a large number of studies, to come up with a summary effect size, i.e. the average coefficient estimate of the education variable in the context of this study. Its ability to synthesize individually disparate studies can yield more rigorously sound statistical evidence than the relatively lessconvincing and often subjective narrative literature review. A metaanalysis starts by defining a dependent and an explanatory variable of interest. Here, the dependent variable is the migration behaviour (i.e. decision or intention), and the explanatory variable is the level or years of education obtained. A list of inclusion/exclusion criteria is then drawn up. Studies that meet the criteria will be included in the metaanalysis. These criteria are necessary so that the eventual dataset contains studies with a manageable degree of heterogeneity, and therefore facilitates comparison. Table 1 lists those criteria. Based on the above criteria, relevant literature on migration are scoured via electronic economic databases. When combing through these databases, the following keywords are used:
Inclusion/Exclusion Criteria
brain drain, migration intention/decision/behaviour, skilled migration, and mobility propensity. For databases providing only abstract and bibliographical citations, every feasible attempt has been made to obtain the full text by searching in another database, or contacting the relevant authors. As suggested by [9], after the databases have been searched through, a last search via Google Scholar and Google is used to round up unpublished or ‘fugitive’ articles [14]. For every study selected to be included in the metaanalysis, its references section is also checked for relevant studies. At the end of the search, a total of 22 studies that fulfil the inclusion/exclusion criteria are selected. To construct the metaanalysis database, I first extract the coefficients of the education variable and their corresponding standard errors from the selected studies. For studies with multiple model specifications, the coefficient and standard error from the base model is extracted. These coefficients are the impact of education on migration behaviour. Along with these two statistics, I also compile and code other characteristics from each of the selected studies. The ‘Metaregression’ section explains further.
FINDINGS & DISCUSSIONS
Metaanalysis
Pane A in Table 2 shows the effect size of each study, i.e. the coefficient estimate of the education variable. As these coefficient estimates are obtained from discrete choice models, their magnitudes are not directly interpretable. The metaanalysis obtains a summary effect of +0.300, i.e. the average coefficient estimate of the impact of education on migration behaviour. Pane A can also be read along with the forest plot shown in Figure 1. The squares in a forest plot represent the effect sizes while the horizontal lines represent the confidence intervals of the effects. A larger sample size gives a smaller confidence interval, indicating a more precise effect size.
The area of the squares reflects the weight that a particular study [15]. Studies with better precision are given more weight, which is a function of sample size. The location of the squares indicates the direction and the magnitude of the effect [16]. It is obvious from Figure 1 that most of the effect sizes border on the positive pane, indicating that the more educated tend towards migrating.
Pane B shows how the impact of education shifts over time, since the studies are sorted chronologically from 1993 to 2011. Pane B can be read along with Figure 2. The effect size displayed in each row is the summary effect based on all the studies up to and including that row. There appears to be three clusters of summary effects shown in Figure 2, with the impact of education stabilizing starting mid2000s. This indicates that in recent years, the impact of education has been consistently at a magnitude of approximately 0.300 (which is translated to its corresponding marginal change in the probability to migrate, depending on the discrete choice model used).
Figure 1. Forest plot
Figure 2. Cumulative forest plot
Pane C and Figure 3 reveal two influential studies in the metaanalysis – [17] and [12]. If we omit Fan’s study from the metaanalysis, the average coefficient estimate of the education variable on migration behaviour will drop from 0.300 to 0.147. Similarly, if we omit Chiquar & Hanson’s study, the average coefficient estimate will increase from 0.300 to 0.394. This finding is not surprising because from Pane A of Table 2, Fan obtained a coefficient estimate of 3.14 for its education variable, while Chiquar & Hanson obtained a negative estimate of 3.537. These two estimates stand out when compared to the others. In this sense, these two influential studies can be regarded as outliers.
Figure 3. Influential studies
Publication Bias
I also check for publication bias to ensure no significant bias in the 22 studies selected for this metaanalysis (in which 18 are journal articles, 3 working/discussion papers, and 1 book chapter). Publication bias can arise when (i) statistically insignificant results are unpublished or being put away in the file drawer, (ii) publication in reports or working papers are excluded in the metaanalysis, and (iii) only publication in a certain language are taken into consideration.
Figure 4 shows a funnel plot typically used to check for presence of publication bias. A funnel plot is a scatterplot of the coefficient estimates from the studies and their corresponding standard errors. A funnel plot follows the rationale that when the sample size of a study increases, so does the precision of its coefficient estimate (i.e. as measured by the standard errors). In the absence of publication bias, the coefficient estimates will scatter symmetrically, with those from studies with smaller sample size making up the base of the plot, and coefficient estimates from larger studies will funnel up the plot. Publication bias however, is only one of the possible causes of funnelplot asymmetries [18].
Figure 4. Funnel plot
 TABLE 2: Metaanalysis and cumulative metaanalysis
Study 
IndividualA 
CumulativeB 
Study OmittedC 


Coeff 
Std err. 
Coeff 
Std err. 
Coeff 
95% Confidence interval 

Vijverberg 1993 
0.011 
0.021 
0.011 
0.021 
0.310 
0.001 
0.620 
Chen & Su 1995 
0.251 
0.179 
0.059 
0.116 
0.302 
0.014 
0.590 
De Jong et al 1996 
0.630 
0.670 
0.069 
0.107 
0.291 
0.008 
0.575 
Burda et al 1998 
0.471*** 
0.135 
0.238 
0.165 
0.290 
0.002 
0.579 
Zhao 1999 
0.346 
0.340 
0.249* 
0.149 
0.298 
0.011 
0.584 
De Jong 2000 
0.862** 
0.401 
0.314** 
0.152 
0.278 
0.008 
0.564 
Papapanagos & Sanfey 2001 
0.260** 
0.131 
0.291** 
0.123 
0.301 
0.013 
0.590 
Boheim & Taylor 2002 
0.157*** 
0.046 
0.226*** 
0.080 
0.306 
0.013 
0.599 
Fan 2002 
3.14*** 
0.039 
0.681 
0.558 
0.147† 
0.076 
0.218 
Drinkwater 2003 
0.073** 
0.031 
0.603 
0.433 
0.317 
0.021 
0.614 
Liebig & SousaPoza 2004 
0.292*** 
0.024 
0.573* 
0.336 
0.295 
0.011 
0.601 
Chiquar & Hanson 2005 
3.537*** 
0.679 
0.309 
0.326 
0.394† 
0.110 
0.677 
Fidrmuc 2005 
0.604 
0.517 
0.329 
0.315 
0.289 
0.005 
0.575 
Van Dalen et al 2005 
0.330*** 
(0.180) 
0.329 
(0.301) 
0.298 
0.010 
0.586 
Epstein & Gang 2006 
0.047** 
(0.021) 
0.324 
(0.250) 
0.307 
0.003 
0.617 
Mora & Taylor 2006 
0.144*** 
(0.018) 
0.327 
(0.209) 
0.299 
0.020 
0.619 
Brucker & Trubswetter 2007 
0.068 
(0.110) 
0.302 
(0.201) 
0.319 
0.030 
0.607 
Van Dalen & Henkens 2007 
0.250*** 
(0.087) 
0.299 
(0.194) 
0.302 
0.013 
0.591 
Fidrmuc & Huber 2007 
0.265 
(0.299) 
0.298 
(0.189) 
0.301 
0.015 
0.588 
Constant & Agosto 2008 
0.166 
(0.276) 
0.291 
(0.184) 
0.306 
0.019 
0.592 
Huber & Nowotny 2011 
0.273* 
(0.164) 
0.290 
(0.179) 
0.301 
0.013 
0.589 
Sun & Fan 2011 
0.169*** 
(0.011) 
0.300** 
(0.143) 
0.290 
0.060 
0.641 
Significant at the *10%, **5%, and ***1% level.
† Influential study.
Pane A, B, C.
Using the randomeffects approach, the summary effect is computed to be 0.300 with a standard error of 0.143 and a pvalue = 0.036.
From Figure 4, there seems to be a bias towards publication with positive coefficient estimates. Formal tests of publication bias however, suggest otherwise. At the 5% significance level, both Egger’s test (pvalue = 0.586) and Begg’s test (pvalue = 0.055) suggest no evidence of publication bias. Rosenthal’s (1979) classic failsafe N is 2,642 (a pvalue much less than 1%), suggesting that the possibilities are remote for us to have missed out on more than 2,600 studies in the related migration literature. We however, cannot preclude the possibility of language bias since only Englishlanguage publications are searched. Nevertheless, it is believed that nonEnglish scholarly publications would not have been significant enough to cause a serious publication bias.
MetaRegression
Metaregression investigates the extent to which statistical heterogeneity between results of multiple studies can be related to one or more characteristics of the studies [19]. It is the best way to account for heterogeneity or betweenstudy variance [20]. Through metaregressions, observed heterogeneity can be accounted for, in which study characteristics explain some of the variations in the coefficient estimates between the studies. That is, metaregression can help to answer questions like why some studies obtained positive/negative coefficient estimates, why some of their magnitudes are larger than those of other studies, and why some of the estimates in certain studies are significant while others insignificant. In doing the metaregression here, we take into account study characteristics as listed in Table 3.
 Table 3. Study characteristics statistics and metaregression coefficient estimates.
Characteristic 
Defined as 
Mean 
MetaCoeff 
Std Err. 
yrpublish 
Year the study is published 
n.a. 
0.309* 
0.138 
size 
Sample size 
6932.14 
0.0001** 
0.00004 
multispec 
Number of model specification examined 
3.55 
0.108 
0.079 
impactfac 
Impact factor of publication 
0.30 
1.015 
0.831 
international 
1 if examining cross national border migration 
0.50 
0.300 
0.718 
actual 
1 if dependent variable is on actual migration decision 
0.55 
1.243 
1.096 
logit 
1 if a logit model is used 
0.32 
1.677 
1.141 
skilled 
1 if examining skilled migration or brain drain 
0.14 
3.986* 
2.075 
edulevel 
1 if the education variable is by education level 
0.82 
1.658* 
0.753 
cross 
1 if crosssectional data is used 
0.86 
1.107 
1.276 
student 
1 if respondents are students 
0.10 
4.906* 
2.223 
europe 
1 if the examined region is in Europe 
0.50 
1.628* 
0.758 
journal 
1 if the publication is in an academic journal 
0.81 
1.591 
0.872 
Dependent variable = effect size or the coefficient estimates of the education variable; n.a. = nonapplicable; Number of studies, n=22. Mean
for categorical study characteristics represents the proportion with the characteristic indicated. Significant at the *10%, and **5% level. Study
characteristics are also known as moderator variables (Stanley 2001).
Results from the metaregression indicate presence of heterogeneity in the coefficient estimates, with a betweenstudy variance (or the variance of the true effect sizes) of t2=0.836, significant at the 1% level. The proportion of observed variance reflecting real differences in effect size is I2=99.7%, i.e. 99.7% of the observed variance is due to real differences in the studies rather than to random error. Such heterogeneity is also obvious from Figure 1. Had there been no heterogeneity issues, all the squares would have aligned to a straight vertical line.
Results from Table 3 suggest that the source of heterogeneity in the coefficient estimates of the education variable come from six of the study characteristics, i.e. the significant metaregression coefficients. In running a metaregression, we are basically estimating the following.
where,
bj = the reported coefficient estimate of the education variable of the jth study from a total of L studies
= the ‘true’ value of the parameter of interest
= the study characteristics
= the metaregression coefficient that reflects the biasing effect of particular study characteristics
= the metaregression error term
j = 1, 2, …, L
The publication year of a study is positively associated with the coefficient magnitude of the original studies, i.e. +0.309; more recent publications found larger positive impact of education on migration intention/decision. This result is also supported by the absence of publication bias discussed in the previous section. Publication bias tends to occur when publications are
focused on obtaining positive and statistically significant results. The fact that we found no evidence of publication bias and that the metacoefficient shows a significant +0.309, may be pointing to the fact of an increasing impact of education after all. The positive metacoefficient of the ‘skilled’ variable (i.e. 3.986) indicates that when a study examines skilled migration or the brain drain phenomenon, the effect size of the education variable increases, resulting in corresponding increases in the probability to migrate. When skilled migration is the issue, the education variable is understandably important and has a significant positive impact on the migration probability of the highly educated. The metacoefficient of the education variable is +1.658, suggesting that when education level dummies are used in the original studies instead of using years of education, the impact of education on migration intention/decision tends to be positive. One possibility is perhaps the real effect of education is more readily captured by the level of education (i.e. a real difference between the level of a high school diploma and that of a doctoral degree, for instance), than by the years of having been in formal schooling.
At –0.0001, the negative impact of sample size on the coefficient magnitudes is relatively negligible although it has a stronger statistical significance. The negative impact of sample size here suggests that as more respondents are surveyed on their migration intention/decision, the impact of education (i.e. the coefficient magnitude of the education variable) decreases. The negative relationship casts doubts on the presence of genuine empirical effect of the impact of education on migration intention/decision [21]. This result however, could also be due to noise from a heterogeneously large crosssectional sample. The practical significance of sample size nevertheless remains somewhat trivial. When the migration issue is examined in European region, the impact of the education variable decreases and translates into decreasing probabilities to migrate. This suggests plausibility of easier mobility within the European region, and therefore less importance might be placed on education as a mobility passport. Similarly, when students instead of working professionals are examined, the impact of education on migration behaviour also decreases. The students examined in the original studies are typically foreign students studying in a host country. Since they are already in the host countries, the level of education they are pursuing there is not as important as say other reasons such as assimilation process, insiders’ information, and networking, for example. On the contrary, the education level or years of education that working professionals possess might be more crucial in influencing their migration behaviour.
CONCLUSION
This paper may well represent the first metaanalysis application on the impact of education on migration. This is a clear departure from the typical analyses used in the migration literature. Here, we have delineated the standard setup of the analysis, by first doing a metaanalysis to obtain the summary effect size, followed by a publication bias check, and a metaregression to identify the source of heterogeneity in effect size. A total of 22 microlevel studies that look at the determinants of migration have been analysed. These papers have included the education variable as one of the regressors, where it is operationalized either as the years of education or the highest level of education obtained. The outcome or dependent variable of these selected studies is either on actual migration behaviour, or on the intention to migrate. The metaanalysis conducted here comprises three parts: the metaanalysis, publication bias check, and metaregression. Results from the metaanalysis conclude the summary impact of the education coefficient to be approximately +0.300. Results from formal tests on publication bias show no evidence of such bias. A metaregression is used to account for the source of heterogeneity in the coefficient estimates. Six of the study characteristics are identified to be contributing to the heterogeneity.
An extension of this paper will incorporate coefficient estimates of the education variable from all different model specifications used within a study, instead of only including the coefficient from the base model. Another possible extension is to do a metaanalysis on a vector of coefficients of a number of variables, and not just limited to metaanalysing the impact of the education variable on migration decision.
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 AUTHORS PROFILE
 Dr. JanJan Soon is currently Senior Lecturer at Universiti Utara Malaysia. Her expertise areas include migration economics, labour economics, and applied microeconometrics..