Improving pan-genome annotation using whole genome multiple alignment

Samuel V Angiuoli
Julie C Dunning Hotopp
Steven L Salzberg
Hervé Tettelin
Improving pan-genome annotation using whole genome multiple alignment
M E T H O D O L O G Y A R T I C L E Open Access
Background: Rapid annotation and comparisons of genomes from multiple isolates (pan-genomes) is becoming commonplace due to advances in sequencing technology. Genome annotations can contain inconsistencies and errors that hinder comparative analysis even within a single species. Tools are needed to compare and improve annotation quality across sets of closely related genomes.Results:We introduce a new tool, Mugsy-Annotator, that identifies orthologs and evaluates annotation quality in prokaryotic genomes using whole genome multiple alignment. Mugsy-Annotator identifies anomalies in annotated gene structures, including inconsistently located translation initiation sites and disrupted genes due to draft genome sequencing or pseudogenes. An evaluation of species pan-genomes using the tool indicates that such anomalies are common, especially at translation initiation sites. Mugsy-Annotator reports alternate annotations that improve consistency and are candidates for further review.Conclusions: Whole genome multiple alignment can be used to efficiently identify orthologs and annotation problem areas in a bacterial pan-genome. Comparisons of annotated gene structures within a species may show more variation than is actually present in the genome, indicating errors in genome annotation. Our new tool Mugsy-Annotator assists re-annotation efforts by highlighting edits that improve annotation consistency.

Background

Advances in genome sequencing technologies have enabled sequencing of thousands of microbial genomes [1]. Often a single reference genome is insufficient to describe the genetic diversity of a species, leading to sequencing of many closely related isolates and subsequent comparative analysis. To aid in the analysis, an annotation process is typically performed using computational methods that include prediction of genes and their functions. Gene prediction algorithms for prokaryotes have been shown to perform well with relatively low error rates [2][3][4]. Limitations of gene prediction include accurate identification of the translation initiation start (TIS) sites and pseudogenes, and over-annotation in GC-rich genomes [5]. Specialized tools have addressed these issues, such as for improved TIS prediction [6]. In addition, post-processing can be used to identify annotation anomalies, as in GenePrimp [7].

While there are several tools for gene prediction of single genomes, relatively few tools exist to deal specifically with the simultaneous annotation of large numbers of nearly identical sequenced isolates, such as a species pan-genome. Also, despite low error rates in gene calling, the accumulation of errors across many genomes can cause problems for comparative analysis, such as identification of the conserved core genome [8]. Additionally, as genomes are sequenced and annotated by diverse scientists, annotations can vary due to choice of gene prediction algorithms or annotation procedures [7,[9][10][11].

Re-annotation efforts have been used to standardize annotation across many genomes to a single protocol [12]. This approach is particularly useful for updating out dated annotation with the latest available evidence. A challenge for standardization efforts is combining automated re-annotation while preserving curated edits, which may include corrections of gene prediction errors. This process requires integration of both manually curated structures and ab-initio gene predictions.

Comparative analysis of closely related sequences forms the basis of many annotation approaches [13]. Reference-based approaches that map annotation onto new genomes using a reference [14] are particularly well-suited to annotation within a species where many genes are expected to be identical in each sequenced isolate. For some species, the use of a single reference genome can be limiting and as a result, researchers often need to integrate annotations from multiple sources. Whole genome multiple alignment is well suited for comparative analysis of closely related genomes, including in a reference independent manner [15][16][17]. The multiple whole genome alignment tool, progressive Mauve [15], provides a feature for reporting orthologous genes as indicated by the alignment allowing for comparisons of genes across genomes. While fully automated approaches for comparison and annotation are of heightened interest as genome sequencing throughput has increased, the need for combining manual, expert curation with high-throughput automated approaches has been recognized [18].

In this study, we introduce a new tool, Mugsy-Annotator, that uses whole genome multiple alignment for two objectives: 1) identifying orthologs and 2) evaluating the quality of annotated gene structures in prokaryotic genomes. The method is effective for identification of aligned genes, such as orthologs, whose genomic position is conserved. The method also provides the foundation for comparing gene structures to identify annotation anomalies, including inconsistently annotated translation initiation sites (TIS), missing annotations, and disrupted genes due to sequencing and assembly errors, or pseudogenes, including frameshifted genes. Finally, Mugsy-Annotator identifies alternative annotations that can resolve anomalies and improve annotation consistency. The tool is freely available at http://mugsy. sf.net.

Methods

The method consists of three primary steps, (1) aligning multiple whole genomes, (2) mapping orthologs among the genomes, and (3) identifying annotation anomalies ( Figure 1). Two types of input files are required: genome sequences in FASTA format, and annotated gene structures (CDS features) in Genbank or GFF3 format. It is expected that a gene prediction algorithm has been run on all input genomes. For step 1, we generate referenceindependent whole genome multiple alignments (WGA) using Mugsy [16]. The alignments generated by Mugsy are restricted to a single region per genome and used by Mugsy-Annotator to define orthologous relationships between sequences. Mugsy outputs alignments in Multiple Alignment Format (MAF) that are passed to Mugsy-Annotator along with the genome annotations needed to complete steps 2 and 3. The genomic coordinates and alignment string of each aligned interval are extracted from the MAF files and stored in an interval tree [19] to provide fast querying of genomic intervals. The start and end coordinates of each gene are also extracted as intervals from the annotation files and stored in the interval tree. The interval tree is then queried by Mugsy-Annotator to build groups of orthologs and identify anomalies in gene boundaries. Although we utilize Mugsy for whole genome multiple alignment, Mugsy-Annotator accepts MAF files as input and other whole genome alignments tools can be used instead of Mugsy as long as the input is properly formatted.

Identification of orthologs

Sets of orthologs are determined by retrieving genes whose intervals are aligned via whole genome alignment (WGA). First, the input genes are sorted on length. The longest gene remaining in the input set, termed the query gene, is removed from the input and used to define a new ortholog group. Genes from other species that align to the query gene in the WGA are added to the ortholog group and removed from the input set. This ensures genes are placed in exactly one group. A configurable coverage cutoff can limit consideration to alignments that span a minimum percentage of the Gene annotations (GFF3) Figure 1 Identifying orthologs and comparing gene structures in a pan-genome using whole genome multiple alignments. The input is provided as a set of genomic sequences (FASTA format) and gene annotations (GFF3 format). Whole genome multiple alignments (top left) are first calculated using Mugsy [16]. Mugsy-Annotator then builds groups of orthologous gene structures that are conserved in sequence and genomic context according to the alignment. The alignment also indicates the location of each predicted translation initiation start and stop across the genomes, allowing for identification of annotation anomalies or missing annotations. query gene and other matching genes. In this study, we set these length cutoffs to 50%. The procedure continues in a greedy fashion using the longest remaining gene to seed new groups (or clusters) until no genes are remaining. Query genes with no overlapping genes above the cutoffs are reported as singleton groups. Using this method, the query gene in each ortholog group is at least as long as any other gene in the cluster and may span multiple adjacent genes in other genomes. This allows our method to identify apparent fragmented genes within a single region in cases where fragments are conserved in order with respect to an alignment to a single continuous gene. Mugsy-Annotator expects one segment per organism in the whole genome alignment. In cases of segmental duplications, Mugsy-Annotator will report separate ortholog groups for each copy only if whole genome alignment identifies orthologous copies in other genomes, using both sequence identity and position in determining which copy is orthologous. In other cases, Mugsy-annotator does not recognize duplications and will report them as singletons clusters. The Mugsy whole genome aligner does identify additional duplications but this information is not currently interpreted by Mugsy-Annotator.

To generate OrthoMCL clusters for comparison [20], we performed an all-against-all BLASTP searches of conceptual translations of the gene predictions. BLASTP alignments with e-value < 10 -5 were used as input to OrthoMCL v1.4 to predict groups of orthologs.

Identification of annotation inconsistencies

Mugsy-Annotator produces a report of the annotation consistency for each ortholog set. To classify annotation consistency for each ortholog set, we examine the location of the annotated start and stop codons for each gene in the multiple alignment. If all annotated start and stop codons are in the same location, the ortholog set is consistently annotated and we identify no inconsistencies. Otherwise, we classify the ortholog set into one or more classes: inconsistent starts, inconsistent stops, and multiple gene fragments. If the stop codon locations are the same for all annotated genes but the TIS differ, we classify the set as inconsistent starts (Figure 2a). If the start codon locations are the same for all genes but the stop codon locations differ, we classify the set as inconsistent stops (Figure 2b). If both start and end locations differ for some members of the group, we classify the group as a combination class. This class will include genes that overlap in the alignment but in different reading frames or strands. Aligned gene sets with multiple annotated genes in the same genome are classified as multiple gene fragments ( Figure 2c).

Alternative annotations

Mugsy-Annotator suggests edits that can resolve anomalies and improve the consistency of each aligned gene set. To determine the possible edits, start and stop codons pairs from each aligned set are checked against the WGA to determine if the aligned positions correspond to ORFs with a valid translation start and stop site (NCBI translation table 11) in each of the other aligned genomes. In cases where the region already contains gene predictions, only alternatives that are greater than a specified percentage (50% by default) of the annotated length are considered.

The procedure will also identify aligned gene sets with multiple gene fragments that can be merged into a single spanning gene by introducing a point mutation or frameshift into the annotation. If the aligned regions contain gaps, Mugsy-Annotator attempts to introduce a frameshift to create a valid ORF joining the start and stop codon pair. Start and stop codon pairs are then displayed ordered by the number of valid ORFs and their length, although this sort order is configurable. This procedure will also identify possible missing genes in regions of the genome that are aligned to other annotated genes (Figure 2d). To be considered a missing annotation, there must be no overlapping gene predictions in the aligned interval.

Data sets

The Neisseria meningitidis (Nmen) dataset of 20 genomes was the same as used in [21]. Two versions of the annotation were available, Nmen verA and Nmen verB. Nmen verA contained 13 genomes that had been annotated using one of two automated pipelines prior to any manual review. Unless noted, the annotation anomalies identified in this study used the Nmen verB annotations, which had undergone limited manual review. The remaining species pan-genomes used in this study (listed in Additional file 1, Table S1) were downloaded from the Refseq database [22]. MUMi [23] distance measurements were calculated for each pair of sequences with a named species.

Results

Mugsy-Annotator for finding orthologs

Mugsy-Annotator uses whole genome alignment (WGA) calculated by Mugsy [16] to identify conserved genes in a set of genomes ( Figure 1). In cases where the alignment represents orthologous regions, these aligned genes correspond to orthologs; i.e., genes descended from the same ancestral sequence. WGA aids in distinguishing orthologs from paralogs by identifying regions that are syntenic and conserved in both sequence and chromosomal position. By aligning genomic DNA, WGA can also identify erroneous gene predictions in a reading frame that produce a nonsense translation and escape detection by similarity methods that rely on conceptual translations, such as BLASTP. On the other hand, by relying on DNA alignment, Mugsy-Annotator might miss sequence conservation between genes that is only detectable at the protein level.

To evaluate the properties of WGA as a method for ortholog identification, we compared the groups of orthologs for 20 Neisseria meningitidis (Nmen) genomes reported by Mugsy-Annotator and a popular BLASTbased clustering method, OrthoMCL [21]. OrthoMCL performs a clustering of Reciprocal Best BLAST (RBB) matches between conceptual translations of genes to identify orthologs. In Nmen, Mugsy-Annotator identified 2,440 ortholog groups compared to 2,320 reported by OrthoMCL. The Mugsy-Annotator groups include nearly all the genes included in RBB matches used by OrthoMCL (38,905 of 39,593 total, 98%).

Both methods reported genes missing from groups reported by the other method, totaling 239 and 669 genes reported by Mugsy-Annotator and OrthoMCL exclusively (Figure 3). Many of the genes reported exclusively by one method appear to be paralogs based on intra-genome BLASTP matches (40% and 66% reported exclusively by Mugsy-Annotator and OrthoMCL methods respectively) or have functional names that indicate transposases (33% and 23% for WGA and RBB respectively) or hypothetical proteins (34% and 31% for Mugsy-Annotator and OrthoMCL respectively). The comparison of our WGA based method and a BLAST based method is illustrative of the differences between the methods in grouping paralogs. Clustering of RBB matches can collapse orthologs and paralogs into a single group. In the Nmen comparison, OrthoMCL reports 310 groups with multiple genes per genome that align to each other via BLASTP, indicating paralogs in a single group. Mugsy-Annotator will sometimes report groups with more than one gene per genome (Figure 2c, "Fragmentation"), but rather than paralogs, these groups represent fragmented genes due to draft genome sequencing (gaps or sequencing errors) or potential pseudogenes. As described in Methods, our tool, by utilizing WGA, incorporates genome context and synteny in determining matches and builds groups that are restricted to a single gene copy per genome, thus avoiding the grouping of orthologs and paralogs together. Identifying orthologs separately is needed for phylogenetic analysis of gene families that rely on orthologs, comparison of upstream regulatory regions, and examination of segmental duplications, where each duplicated copy has a distinct genomic context. In other cases, grouping of paralogs and orthologs together is desirable and as such our WGA-based method is expected to be complementary to BLAST based clustering methods for identifying gene families.

For genes grouped exclusively by Mugsy-Annotator, 23 have no reported intra-species BLASTP matches to other genes in Nmen, and include annotations that appear to be in an incorrect ORF (Additional file 2, Table S2). Although we found this class of anomaly to be rare in our evaluation, Mugsy-Annotator, by using WGA, is able to identify orthologs to such regions that lack BLAST matches within the dataset and may have a nonsense conceptual translation. An additional 68 genes (28%) reported exclusively by Mugsy-Annotator are adjacent to contig boundaries and may be truncated gene predictions that escape detection by BLAST.

Our WGA method is computationally efficient and has a significant runtime performance advantage over BLAST. The comparison of 20 Nmen genomes runs on a single CPU in~4 h (~2 h for WGA with Mugsy and 2 h for comparing annotations with Mugsy-Annotator). By comparison, the exhaustive all-against-all BLAST of predicted proteins needed for OrthoMCL consumed~32 CPU hours and was run on a compute cluster to obtain a faster runtime. In addition, BLASTbased methods that rely on searches of conceptual translation may require additional search of the genomic DNA, such as with BLASTx, to confirm gene presence and avoid mis-prediction of paralogs as orthologs.

Missing annotations

Mugsy-Annotator can be used to identify missing annotations and putative genes by looking for regions of the alignment with a prediction in some genomes but not others (Figure 2d, "Missing annotation"). These missing annotations can arise from use of varying gene prediction tools and uncertainty in gene calling procedures, especially for short genes [24]. In our study of 20 Nmen strains, a majority of the aligned gene sets contain one annotated region from each of the genomes (Figure 4) and missing gene predictions were rare, totaling only 50 genes missing in alignments containing 18 or more genomes (Additional file 3, Table S3).

Mugsy-Annotator identifies missing annotations if DNA corresponding to a putative gene is an ORF that is conserved across genomes. However, it does not provide additional evidence to determine if a gene prediction is missing in some genomes (false negative) or there is an overcall in the other aligned genomes (false positive). Our methodology relies on sequence conservation between the input genomes, which by itself is insufficient to distinguish between these due to the short phylogenetic distance and high similarity of the genomes. Examination of additional evidence (eg. HMM or BLAST searches) or experimental validation is required to differentiate between these cases.

Identifying and resolving annotation anomalies

To aid in re-annotation efforts, Mugsy-Annotator identifies likely annotation problem areas and suggests alternative genes based on the whole genome multiple alignment. To find such problem areas, Mugsy-Annotator first examines each of the aligned gene sets for inconsistencies in annotated gene boundaries amongst members of the set (Figure 2). The reported anomalies include inconsistently located TIS, disrupted genes, or alternative ORFs. Mugsy-Annotator then generates a report for each aligned gene set that describes the inconsistency and possible resolutions. A browser of the annotations overlaid on the whole genome multiple alignment is also provided (Additional file 4, Figure S1).

To demonstrate the tool, we ran Mugsy-Annotator on nine bacterial species, all of which have multiple strains with complete genomes available ( Figure 5). The output indicates many inconsistencies in annotated gene structures, with inconsistent TIS locations as the most commonly identified anomaly. While the inconsistencies may indicate errors in the annotated gene structures in one or more of the genomes, the results are not surprising as the sequencing coverage, date of annotation, and annotation protocols vary. The presence of annotation errors in public repositories has been widely recognized [25][26][27] leading to a number of re-annotation efforts for genomes in a single species [28,29].

As a case study, we evaluated the Mugsy-Annotator report for the dataset of 20 Nmen genomes. Inconsistent TIS are the most commonly detected anomaly in Nmen

Composition of Nmen verB ortholog groups

Number of genomes with 30% of aligned gene sets containing more than one annotated TIS. Due to lack of precision in TIS prediction, we expect the number of TIS inconsistencies to increase as the number of genomes increases, especially since our method marks a group as inconsistent even if the annotation error is limited to a single genome. To see how overall consistency is affected by any single genome, Mugsy-Annotator reports the number of times a single genome is inconsistent in comparison to the set. An examination of the Nmen genomes shows that certain subsets of genomes have better internal consistency. In 27% of groups with TIS inconsistencies, an alternative annotation in a single genome will resolve the inconsistencies for the group (Figure 6). Although some of the Nmen genomes contributed to more annotation inconsistencies than others, all of the genomes contributed to inconsistencies in at least one group.

Mugsy-Annotator suggests alternative gene structures that improve annotation consistency. In Nmen core gene groups containing all genomes, 55% (400/725) of groups with inconsistent TIS can be resolved by an alternative annotation that is conserved across all the aligned genomes. In 50% of these cases, the alternative start site is upstream of the existing annotation, resulting in longer annotations. In the remaining cases, the most consistent TIS location results in a shorter gene in at least one genome. A majority of the alternative TIS locations are in the same coding frame and within 42 bp of the annotated TIS (Figure 7), indicating that annotation protocols have chosen inconsistently between sites that are nearby along the genome. Adjusting the TIS can result in an overlap with an adjacent gene. To help avoid mis-annotation of overlapping genes [30], Mugsy-Annotator flags edits that would result in an overlap with an adjacent gene. In alternative annotations of Nmen groups, 15% (63/400) introduce overlap with adjacent annotations indicating further evaluation is needed to determine the correct annotation.

When a single gene in one genome is aligned to multiple genes in other genomes, Mugsy-Annotator calls this an anomaly (Figure 2c, "Fragmentation"). These apparent gene fragments can arise from sequencing and assembly errors; from interesting novel gene fusions; or from pseudogenes, in which frameshifts or in-frame stop codons can split an open reading frame into multiple gene-like fragments. In our case study of Nmen,  draft genome sequencing appears to contribute to a vast majority of occurrences of this anomaly (Additional file 5, Table S4), although the tool has also aided in the identification of several novel gene fusions that are not fixed in the population. To aid in classifying this anomaly further, Mugsy-Annotator reports whether or not frameshifts can extend the interrupted gene fragments to match a longer annotated gene. Amongst the aligned gene sets containing all 20 Nmen genomes, Mugsy-Annotator found 48 cases where a single previously unannotated frameshift would resolve the anomaly and result in a consistently annotated set (Additional file 6, Table S5). In many other cases, some of the genes can be extended with a frameshift but other anomalies remain in the group. Additional review would be needed to further classify these anomalies. To aid with this, Mugsy-Annotator provides a view of the current annotations and reading frames overlaid on the whole genome multiple alignment (Additional file 4, Figure  S1). In the Nmen study, Mugsy-Annotator suggests alternative annotations that can improve consistency in up to 57% of ortholog groups. Although the alternatives improve consistency, in most cases an examination of additional evidence is required to ensure that edits improve quality. In this case study, the variability of the annotation is partly due to the multitude of sources and sequencing strategies. The Nmen genomes are available in varying stages of sequence quality including 9 draft and 11 complete genomes and the annotation evaluated came from a total of 5 laboratories using varying gene prediction protocols and levels of manual curation. To better accommodate draft genomes, the gene prediction procedure used in some of the Nmen genomes allows Figure 6 Annotation anomalies caused by a single genome. Each row provides a count of ortholog groups where the named genome is inconsistent with the remaining genomes in the group. In these cases, the annotated translation initiation site in the named genome in Nmen verB did not match any of the other annotated gene structures in the ortholog groups.

for partial open reading frames that terminate or initiate outside of a contig boundary. Mugsy-Annotator flags anomalies that are caused by these partial genes adjacent to contig boundaries. In Nmen, such cases contributed to~9% of start and stop site inconsistencies and at least 67% of all of the multiple gene fragment anomalies (Additional file 5, Table S4). Annotation anomalies due to draft genome assemblies will continue to be an issue in multi-genome analysis as current generation sequencing technologies have prompted an explosion in the number of draft genomes.

To demonstrate annotation improvements using Mugsy-Annotator, we scored annotation consistency in three versions of annotation for Nmen. An initial version of the Nmen annotation (Nmen verA), contained predominantly automated annotation in 13 newly sequenced genomes and curated annotation for 7 complete genomes. Nmen verA showed a large number of inconsistencies, encompassing 72% of ortholog groups ( Figure 5). As part of the study in [21], limited manual curation was performed and resulted in annotation of frameshifts and removal of many short, unsupported hypothetical gene predictions and resulted in the annotations in Nmen verB. Although this manual effort was aided by the Mugsy-Annotator report, the curation effort was not meant to be exhaustive and not all reported inconsistencies were examined during the review. Subsequent to this manual effort, Mugsy-Annotator was run again and generated a new set of alternative annotations (Nmen verC)suggesting additional improvements were possible. This resulted in consistent annotations in 59% of groups in Nmen verC, which was an increase from 28% in Nmen verA The improvement in annotation consistency between versions highlights the need for re-annotation and manual review subsequent to automated annotation.

Discussion

With the growing availability of numerous genomes for many bacterial species, there is an increasing need for tools that can integrate information to produce more consistent, higher quality annotation for each individual genome as well as the pan-genome. Mugsy-Annotator aids in identifying and comparing gene content across a pan-genome, including draft genomes, with an approach that is independent of a reference genome. For re-annotation efforts, Mugsy-Annotator can be used to direct curators to likely errors and make corrections across many genomes simultaneously, rather than one genome at a time. Our case study indicates that comparisons of annotated gene structures show considerable variation that is a consequence of bioinformatics methods rather than true biological differences. Differences appeared greater among translation initiation sites and in regions with poor sequencing coverage.

Mugsy-Annotator is also an efficient, accurate method for finding orthologs within a pan-genome. By using a fast whole genome alignment tool, Mugsy, our method is computationally efficient compared to BLAST-based approaches for finding orthologs. Our approach is also robust for certain types of annotation errors, such as missing annotations or incorrect reading frames. Since our method relies on accurate DNA alignment, it is most useful for closely related genomes that share a high percentage of identical DNA, such as isolates from the same or closely related species. Mugsy-Annotator is currently limited to analysis of annotation that does not contain spliced gene structures.

Mugsy-Annotator currently compares genomes using an existing set of gene predictions. We plan to integrate it into an automated pipeline for de-novo annotation of one or more newly sequenced genomes from an existing set of reference genomes. One option for an implementation of this would include integration of sequence conservation defined by WGA into a de-novo gene finder as additional evidence supporting the annotation, especially if a well chosen outgroup sequence is provided. Alternatively, a mapping approach, similar to approaches for mapping between two genomes [14], could be used to augment existing gene predictions and transfer names and functional annotations across the new genomes. Since gene prediction runs quickly on bacteria (usually minutes), we expect the speed of such an approach would be limited by the time required to calculate a whole genome multiple alignment.

Proximity of alternative TIS annotations

Distance from annotated start

Figure 2
2..................... Gene3 ..................... Gene1 AGGAGGCAAGTAAGCCATGCG Gene2 ..................... Gene3 .........-........... ..................... ..................... ..................... Gene3 .........T.........Annotation anomalies identified by Mugsy-Annotator. Four classes of anomalies are shown (a-d). On the right, examples of aligned genes are drawn with the boxed region indicating the location of the anomaly.On the left, a multiple alignment is depicted across the highlighted region with sequence identity indicated by dots. In (c), a gap indicated by a dash introduces a shift in reading frame that results in use of a termination codon that is inconsistent with the annotations in the other genomes. Translation initiation sites are marked as "start" and termination codons are marked as "stop" with an arrow indicating the direction of translation.

Figure 3
3Comparison of genes reported in orthology groups from Mugsy-Annotator and OrthoMCL. The intersection between Mugsy-Annotator and OrthoMCL reports the number of genes reported in ortholog groups by both methods. The remainder for Mugsy-Annotator and OrthoMCL reports the number of genes classified in ortholog groups by one of the methods only.

Figure 4
4Distribution of the number of genomes in ortholog groups identified by Mugsy-Annotator for 20 Nmen genomes. The number of genomes per orthology groups are provided for all orthology groups (top), consistently annotated groups only (middle), and exclusively groups with annotation inconsistencies (bottom).

Figure 5
5Consistency of annotated gene structures in several species pan-genomes as reported by Mugsy-Annotator. Each row provides the fraction of aligned gene sets in each class of anomaly and groups with no identified inconsistencies (blue). The number of genomes compared and their average MUMi similarity[23] distance is also provided, ranging from zero for most similar to 1, least similar. The bottom three rows describe three versions of annotations from the case study of Neisseria meningitidis (Nmen) . The last version (Nmen verC) demonstrates improvements in consistency using alternative annotations suggested by Mugsy-Annotator.

Figure 7
7Distance of alternative TIS from the annotated site. Distance between the annotated translation initiation site and the most consistent translation initiation site reported by Mugsy-Annotator.
Angiuoli et al. BMC Bioinformatics 2011, 12:272 http://www.biomedcentral.com/1471-2105/12/272 Page 6 of 11
AcknowledgementsThis work was supported in part by NIH grant R01-GM083873. We thank David Riley and Sean Daugherty for valuable discussions and feedback.As many genomes are currently sequenced to draft status and represented in multiple contigs, Mugsy-Annotator can aid in identifying truncated or missing genes that are due to poor sequencing coverage by examining anomalies identified by Mugsy-Annotator that are adjacent to contig boundaries. For investigating anomalies caused by sequencing coverage, at least one relatively complete genome is required in the alignment for the approach to be effective.Mugsy-Annotator looks for inconsistencies in gene structures to identify likely errors. It is also possible that consistency results from the propagation of an annotation error, especially since it is common to use reference genome annotations when annotating new genomes. In some cases, the annotated gene structures may be consistent but incorrect and Mugsy-Annotator will not identify any anomaly. On the other hand, due to the short evolutionary distance between the genomes under evaluation in our case study, inharmonious gene boundaries in orthologs are expected to indicate an improper gene boundary assignment in at least one genome. Importantly, additional evidence besides the WGA will often be needed to determine the correctness of the annotations including, but not limited to, gene boundaries of more distantly related orthologs, third position compositional bias, predicted ribosomal binding sites, and predicted signal peptides. As such, our tool stops short of determining the correctness of any gene calls, as this is best left to follow-up analysis or experimentation in the laboratory. Yet, our tool is ideally suited to direct the annotation curator towards the regions in most need of attention, and where Mugsy-Annotator suggestions will greatly facilitate rapid improvement of annotation consistency. Such tools are urgently needed in light of the explosion of genomes currently happening as researchers are sequencing hundreds of genomes for many individual species.ConclusionWhole genome multiple alignment can be used to efficiently identify orthologs and annotation problem areas in a bacterial pan-genome. Our new tool Mugsy-Annotator assists re-annotation efforts by highlighting potential annotation errors and suggesting alternative annotations that improve annotation consistency.Additional materialAdditional file 1:Table S1: List of Nmen genomes.Additional file 2:Table S2: List of genes found exclusively in whole genome alignment groups.Additional file 3:Table S3: List of missing gene annotations in Nmen.Additional file 4:Figure S1: Screenshot of Mugsy-Annotator report of annotation inconsistencies. An indel in one of the genomes (MENX) introduces a frameshift that results in a premature stop codon location when compared to the other genomes. Mugsy-Annotator identifies this anomaly and reports the location of the frameshift mutation (indicated by the red box). An alternative annotation in MENX that utilizes a +1 frameshift at the location of this single indel results in gene boundaries (TIS and stop codon) that are consistent with the other 19 genomes in the multiple alignment.Additional file 5:Table S4: List of causes of annotation anomalies. Additional file 6:Table S5: List of Nmen ortholog clusters from whole genome multiple alignment.Authors' contributions SVA devised the method, implemented the tool, and ran evaluations in consultation with JDH, HT, SLS. SVA drafted the manuscript. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests
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Variations in Migration in the Baltic States
2021

Annalise Simons
Colgate University
Colgate University

Annalise Simons
Colgate University
Colgate University

Variations in Migration in the Baltic States

Part of the International and Area Studies Commons, and the International Relations Commons Recommended Citation Recommended Citation Simons
Annalise20219202110.5642/urceu.202101.09Claremont-UC Undergraduate Research Conference on the Claremont-UC Undergraduate Research Conference on the European Union European Union Volume 2021 Article 9 9-30-2021 Follow this and additional works at: https://scholarship.claremont.edu/urceu Available at: https://scholarship.claremont.edu/urceu/vol2021/iss1/9 This Chapter is brought to you for free and open access by the Current Jounrals at Scholarship @ Claremont. It has been accepted for inclusion in Claremont-UC Undergraduate Research Conference on the European Union by an authorized editor of Scholarship @ Claremont. For more information, please contact firstname.lastname@example.com. Variations in Migration in the Baltic States Variations in Migration in the Baltic States Cover Page Footnote Cover Page Footnote I would like to thank my academic advisor, Professor Edward Fogarty, for his continued support throughout this process. Additionally, I would like to thank Professor Valerie Morkevicius for sparking my interest in researching the Baltic states. This chapter is available in Claremont-UC Undergraduate Research Conference on the European Union: https://scholarship.claremont.edu/urceu/vol2021/iss1/9 Claremont-UC Undergraduate Research Conference on the European Union 6 7net migrationpush factorpull factorunemployment
This paper seeks to answer a driving research question: Why do the Baltic Countries exhibit disparate levels of immigration and emigration? The paper considers the period from 2004, when Estonia, Latvia, and Lithuania became European Union member states, up to 2019. Lithuania, Latvia, and Estonia, while all small states in the same region, experience different levels of net migration. As Lithuania, Latvia, and Estonia maintain small populations with negative natural population increases, migration inflows and outflows are quite significant. Among the three, Latvia and Lithuania have experienced dramatically negative net migration. Examining the push and pull factors influencing net migration in Lithuania, Latvia, and Estonia, sheds light on key factors shaping migration patterns, which can aid in developing policies that align with the countries' interests regarding migration.

Introduction

Drastic population decline is one of the most pressing issues in the Baltic states (Estonia, Latvia, Lithuania) (ERR, 2011). The population of Lithuania, for example, has declined by 24. 4% since 1990 (European Migration Network, n.d.). Migration inflows and outflows from these states have significant impacts for the population levels, as the countries are small in terms of overall population. As of January 1, 2020, the populations of Estonia, Latvia, and Lithuania were 1,328,976,1,907,675,and 2,794,090, and were ranked 24 th , 23 rd , and 21 st of the 27 European Union (EU) countries in terms of population, respectively (Eurostat, 2020a). As member countries of the EU, citizens in the Baltic states have equal opportunity and access to destination countries for migration in the EU. My research question is thus: why do Latvia and Lithuania experience higher levels of emigration compared to immigration than Estonia? I focus on voluntary migration as opposed to forced migration, as most external migration in the Baltic states is not a result of forced migration (Asylum Statistics, 2020). In this paper, I argue that the differences in immigration levels compared to emigration levels in the Baltic countries derive from disparities in economic well-being and in immigration policies.

The paper commences with an explanation of the overall argument of the paper. Then, I outline the methodology for my empirical analysis. After describing the methodology, I explore the validity of hypotheses explaining variations in emigration and in immigration in Estonia, Latvia, and Lithuania. Finally, I conclude the paper with implications of my findings and opportunities for future research.

Argument

My overall assertion is that economic disparities, as well as differences in immigration policy, explain the variation in emigration and immigration levels between the Baltic states. The independent variables I focus on in this paper are levels of unemployment, wages, and strictness of immigration policy. Emigrants from the Baltic states emigrate to pursue improved economic well-being. Additionally, immigration policy shapes immigration to the Baltic States, which inherently impacts the levels of immigration compared to emigration. Policies that promote less arduous immigration procedures facilitate immigration. Therefore, the primary causal mechanism is individuals deciding to migrate after weighing the costs and benefits of migration in terms of economic opportunities and ease of immigration to the receiving country.

Methodology

The outcome variable I explore is the level of immigration compared to emigration. The measurement for this variable is net migration (total immigrants minus total emigrants). I focus my study on Estonia, Latvia, and Lithuania due to the similar nature of the three countries. The countries have a shared history of occupation by the Russian Empire and later the Soviet Union, and they simultaneously experienced independence and economic transition in the early 1990s. The Baltic states, especially Estonia and Lithuania, have similar Gross Domestic Product (GDP) per capita (PPP based). The GDP per capita in Estonia and Lithuania in 2019 were 38,968 US dollars (USD) and 38,401 USD, respectively. Latvia's GDP per capita in 2019 was reported as 32,184 USD (OECD, 2020b). However, Latvia and Lithuania have maintained significantly negative net migration (more emigrants than immigrants), and Estonia has experienced positive net migration since 2015 (more immigrants than emigrants).

https://scholarship.claremont.edu/urceu/vol2021/iss1/9 I explain both cross-national and over-time variations in net migration. For the overtime aspect, I consider the time period from 2004 to 2019. I limit my focus to this time period because Estonia, Latvia, and Lithuania became EU member states in 2004, which drastically eased the process of migrating to EU states from the Baltic states. It is, however, important to note that all EU states besides the United Kingdom, Ireland, and Sweden restricted migration from the Baltic states for the first few years after the Baltic states became member countries of the EU (Thaut, 2009, p. 192). Even so, the Baltic countries had greater opportunity to migrate to other states with more ease than before 2004.

In order to understand why countries experience varying migration outflows and inflows, one must consider push and pull factors. Push factors explain why migrants choose to emigrate from their country of residence, and pull factors explain the reasoning for settling in a particular destination. There is already a very active conversation among scholars regarding the incentives to voluntarily migrate. However, there are areas to expand the conversation. Notably, examining similar countries that experience differing levels of migration can aid an in-depth analysis exploring the manner in which incentives differ on the country level. In this paper, I will structure the incentives to migrate in terms of push and pull factors.

In my empirical analysis, I focus on economic disparities between the Baltic states and differing immigration policies in order to explain the contrast in net migration. The data for the empirical analysis is primarily quantitative, which enables a thorough over-time and cross-national comparison. The independent variables included in the empirical study are wages, unemployment rate, and relative ease of immigration for non-EU citizens to a given country. I measure the relative ease of immigration through comparing immigration policies in Estonia, Latvia, and Lithuania in order to explain differing incentives that the countries provide for certain types of immigrants. Comparing how arduous the immigration process is in each country attests to the costs of immigration.

Empirical Analysis: The Baltic States

Net Migration in the Baltic States

Since accession to the EU, overall migration, especially immigration, has increased in each of the three countries. As both emigration and immigration affect net migration, I will divide the empirical analysis into two parts: emigration and immigration. Lithuania experiences the largest inflows and outflows of migrants, and Lithuania has maintained negative net migration since independence. Latvia also has experienced prolonged negative net migration. Alternatively, Estonia experienced negative net migration but began to see positive net migration in 2015. External migration, both immigration and emigration, is significant for these small countries, especially given the negative natural population growth rates in the Baltic countries. Population Reference Bureau notes Estonia's natural population increase in 2019 as -0.1%, Latvia's as -0.5%, and Lithuania's as -0.4% (Population Reference Bureau, 2020). The overall population will decline at a faster rate with negative net migration and a negative natural increase in population, which may prove burdensome for the already small populations in the Baltic states. Figure 1 visualizes the estimates for net migration in Estonia, Latvia, and Lithuania over 5-year periods. As Figure 1 depicts, Lithuania experiences the most negative net migration (immigration minus emigration). Estonia experiences a net migration closer to zero, becoming positive in the 2015-2020 time period. Latvia experiences negative net migration to a lesser extent than Lithuania but greater extent than Estonia. It is important to note that Lithuania has a larger overall population than Estonia and Latvia, and there are overall greater numbers of migrants.

Variations in Migration in the Baltic States

Emigration

It is imperative to examine both emigration and immigration to understand net migration. The first aspect of net migration I examine in the Baltic countries is emigration. The hypotheses I focus on in this section are average wages and unemployment rates. Table  1 demonstrates emigration in the Baltic states.

Wages

Some scholars argue low wages inspire emigration in pursuit of countries with better wages (Doerschler, 2006(Doerschler, , p. 1101. Other scholars note that income differentials and globalization foster migration from countries with lower income levels to higher income levels (Milanovic, 2019, p. 138). To test if this hypothesis is consistent with income levels and emigration from the Baltic states, I consider wage levels in the Baltic states alongside other Organization for Economic Cooperation and Development (OECD) countries in order to compare wages to other developed countries. I focus on averages wages to represent income resulting solely from employment. Figure 2 compares the average wages in the Baltic countries to other OECD countries in 2018.  indicates Latvia, Lithuania, and Estonia have significantly lower average wages than most other OECD countries, including most of the countries in the EU. In 2018, average wages were 28,499 USD in Estonia, 26,765 USD in Latvia, and 27,368 USD in Lithuania. As wages in the Baltic countries are lower than in other EU and OECD countries, it would be expected, on the wage push factor hypothesis, that residents of those countries would leave to pursue higher wages in another country, especially people from Latvia and Lithuania.

Unemployment

Another push factor I analyze with respect to Estonia, Latvia, and Lithuania is unemployment. Scholars, such as Laura Thaut, assume that migrants would leave a country with relatively few employment opportunities, for a country with more employment opportunities (Thaut, 2009, p. 204). Figure 3 shows the unemployment rate as percent of labor force from 2004 to 2019. Estonia maintains a lower unemployment rate than Lithuania and Latvia. All three Baltic countries experienced dramatic increases in the unemployment rate following the recession in 2008. Over time, however, Estonia has maintained a lower unemployment rate. Estonia is close to the median unemployment rate for OECD countries, but Lithuania and Latvia have higher unemployment rates relative to most other OECD countries. Estonia, compared to Lithuania and Latvia, has greater demand for workers compared to supply, which would be attractive to immigrants and would not push Estonian residents to emigrate to the same extent as Latvian and Lithuanian residents.

https://scholarship.claremont.edu/urceu/vol2021/iss1/9 One potential pull factor is greater opportunity for employment, evidenced by a lower unemployment rate. For this study, I examine the connection between emigration from the Baltic states and the unemployment rate in predominant destination countries. Krisjane, Berzins, and Apsite (2013) note that Estonians most commonly migrate to Finland. A majority of Latvian and Lithuanian emigrants settle in the United Kingdom (Krisjane, Berzins & Apsite, 2013, p. 92).

In the case of Lithuania, migration to the United Kingdom has been a significant portion of emigration. Statistics Lithuania only had data on the number of emigrants to the United Kingdom beginning in 2010. The Central Statistical Bureau of Latvia had limited data available on destination countries, which is why I focus on Lithuania and Estonia in this section. Migration from Lithuania to the United Kingdom has decreased since 2016, which is potentially an impact of Brexit, yet the United Kingdom remains the destination for over 30% of Lithuanian emigrants ( Figure 5).

Variations in Migration in the Baltic States

Figure 5. Percent of Lithuanian Emigrants Settling in the United Kingdom

As Figure 6 indicates, the unemployment rate in the United Kingdom has been consistently lower than in all the Baltic states, except during the recession, indicating increased employment opportunities. Additionally, Laura Thaut notes that there are recruitment agencies from certain EU member states, including the UK, increasing their presence in Lithuania to attract high-skilled labor (Thaut, 2009, p. 204). The percentage of Estonians migrating to Finland has decreased since 2015, as Figure  7 depicts, which indicates a more diversified set of destination countries. Even so, Finland is still the most common destination country for Estonians.

Figure 7. Percent of Estonian Emigrants Settling in Finland

Source: Adapted from Statistics Estonia (2020a) The unemployment rate in Finland has been higher than in Estonia since 2013, as Figure 8 demonstrates, which corresponds to the decline in the percentage of Estonian emigrants settling in Finland. Finland had a more stable unemployment rate during the recession until 2013, explaining the high percentage of Estonian emigrants settling in Finland during those years.

Figure 8. Unemployment Estonia Compared to Finland

Wages

As Figure 2 details, the United Kingdom maintains higher average wages than the Baltic states. There are several other factors potentially involved, including the fact the United Kingdom was one of few countries in the EU that did not initially restrict migra-Variations in Migration in the Baltic States tion from the EU8 -the eight countries that were formerly part of the Soviet Union that joined the EU in 2004 (Lithuania, Latvia, Estonia, Hungary, Slovakia, Slovenia, the Czech Republic).

As for Estonian migration to Finland, average wages are consistent with hypotheses predicting migration. As Figure 2 indicates, Finland has higher average wages than Estonia, which is consistent with the hypothesis that emigrants pursue higher wages. Therefore, for Estonian emigrants that settle in Finland unemployment and average wages together explain the decrease in, yet still significant, number of Estonian emigrants settling in Finland. Other factors such as linguistic similarity and cultural connection between Estonia and Finland may impact emigration from Estonia to Finland as well.

Immigration

In order to gain a complete understanding of net migration in Estonia, Latvia, and Lithuania, I discuss pull factors incentivizing immigration to these three countries. Beyond appealing to countries with lower wages and higher unemployment, such as Ukraine, it is necessary to examine immigration policies, as most immigrants migrate from non-EU countries or are citizens of the migration destination (Central Statistical Bureau of Latvia, 2020a; European Migration Network (n.d); Statistics Estonia, 2020b). Table 2 details overall immigration in the Baltic states. https://scholarship.claremont.edu/urceu/vol2021/iss1/9

As Table 2 indicates, immigration has increased to the Baltic states overall since 2004. Table 2 shows that Lithuania witnessed a significant increase in immigration between 2018 and 2019. Of the immigrants in 2019, 20,412 were Lithuanian nationals returning to Lithuania (European Migration Network, n.d.). Further exploration into the explanation of the drastic increase in immigration to Lithuania in 2019 is necessary. There is not substantial data and information available to examine this particular increase, but future research into this occurrence could shed light on immigration patterns and push and pull factors. Table  2 details a drastic increase in immigrants to Latvia in 2011. Estonia experienced a rapid increase in immigration between 2014 and 2015, which contributes to Estonia's positive net migration since 2015.

A significant proportion of the immigrant populations, especially for Lithuania, are citizens returning to the Baltic states of nationality. Lithuania has maintained the highest percentage of immigration of its nationals. In 2011, the percentage of Lithuanian nationals immigrating to Lithuania out of total immigrants reached 89%, and, in 2019, 45% of immigrants to Lithuania were Lithuanian nationals (Statistics Lithuania, 2020c). In Latvia, Latvian nationals immigrating to Latvia as a percent of total immigration also peaked in 2011 at 72%, and, in 2019, still 41% of all immigrants to Latvia were Latvian nationals (Central Statistical Bureau of Latvia, 2020a). In 2011, Estonian nationals immigrating to Estonia were 55% of total immigration and, in 2019, this figure was 40%. All of the Baltic states, however, have experienced a decline in the percentage of nationals immigrating since 2011, indicating the countries now experience more diversified immigration. Immigration policies are pertinent to understanding levels of immigration.

Immigration Policies in Estonia

A distinct aspect of immigration policy that persists in Estonia is the annual immigration quota for long-term migration of third-country nationals (immigrants that are not citizens of EU countries, Switzerland, Lichtenstein, or Norway). Citizens of EU member countries and the European Economic Area only have to register with the Estonian government, as opposed to third-country nationals, who must apply for a residence permit. The annual immigration quota is 0.1 percent of the population of Estonia from the previous year. While this is a small percentage, there are numerous employment positions that are not included in the quota, such as Information and Communications Technology (ICT) positions, founders and workers for start-ups, investors in startups, and those with the EU Blue Card (Labour and Entrepreneurial Migration, n.d.). The EU Blue Card is a work and residence permit for citizens of countries not in the EU or the European Economic Area that can lead to permanent residence and citizenship (Live and Work in the European Union!, n.d.). Citizens of the United States and Japan are also not included in the immigration quota restrictions (Aliens Act, 2019).

Due to the immigration quotas, migration for short-term employment has increased in Estonia. Once the immigration quota is reached for residence permits, immigrants can still migrate to Estonia for short-term employment. In 2018, the immigration quota was fulfilled in June, and short-term employment migration increased (Luik, 2019, p. 22). In 2018, 80% of migration to Estonia was for short-term employment (Luik, 2019, p. 7). In 2018, the government extended short-term employment to last 365 days rather than 270 days, and new types of employment now qualified for short-term employment without requiring a residence permit. The number of immigrants registered for short-term employment in 2018 was almost three times the number in 2017 (Luik, 2019, p. 21). Despite immigration quotas, Variations in Migration in the Baltic States more individuals are immigrating to Estonia for short-term employment.

Another change in immigration legislation occurred in 2017 to encourage new startups to develop in Estonia. Already, founders, employees, and investors in startups could immigrate without being limited by the immigration quota. The number of founders and employees in start-ups that immigrated to Estonia increased by 236% from 2017 to 2018 (Luik, 2019, p. 22). The Startup Estonia program, which commenced in 2011 and launched again for the period from 2015 to 2023, seeks to increase funding and opportunities for startups in Estonia (Michelson, 2018, p. 1). The 2017 amendment created a specific visa for those involved in the creation and development of startups in Estonia for either short-or long-term residence. The spouse and dependent(s) of the individual employed will be also issued a visa. The length of stay for long-term visas for startups was extended in the 2017 amendments (Aliens Act, 2019). The amendments seek to encourage start-ups in Estonia and immigration to sustain start-ups. These initiatives are consistent with the dramatic rise of immigration since 2015 to Estonia.

Immigration Policies in Latvia

Latvia has also implemented new policies regarding immigration in recent years. In 2017, the Latvian government passed amendments to the Immigration Law, some of which facilitate immigration in certain sectors and others restrict immigration flows for sectors. Notably, Latvia eased requirements for obtaining residence permits for individuals employed in sectors experiencing labor shortages in order to incentivize migration in those specific sectors (Silina-Osmana, 2018, p. 10). The Latvian government reduced the restrictions for third-country nationals involved in creating startups. Individuals involved in the creation of startups no longer require a sponsor in the process of registering for a residence permit. Still, there are certain requirements to receive residence permits as part of the development of a startup, such as securing 60,000 Euros of investments within 18-months (Silina-Osmana, 2018, p. 11). Through these policies, the Latvian government seeks to promote innovation.

At the same time, Latvia made immigration requirements and procedures more stringent for certain sectors. Notably, the 2017 amendments to the Immigration Law added requirements for offices of merchants and the employees that seek temporary residence permits. Any foreign company dealing with selling or trading goods must have been registered abroad for five years as well as maintain at least 50 employees and annual turnover of at least 10 million Euros before being able to gain residence permits for employees (Silina-Osmana, 2018, p. 12). The recent immigration policy changes in Latvia prioritize immigration in ICT and startups, as well as sectors experiencing labor shortages, and seek to disincentivize immigration for foreign companies.

Immigration Policies in Lithuania

Similarly, Lithuania has facilitated immigration processes in recent years for certain employment positions. In 2017, amendments to the Law on the Legal Status of Aliens eased the immigration procedure for workers in positions in which Lithuania maintains a labor shortage. Applicants for professions on the list of professions with labor shortages will receive a visa within 15 days and do not need a separate work permit. As of 2018, immigrants that are third-country nationals can apply for positions on a new "Whitelist" of certain companies and benefit from fewer requirements in the immigration process. Companies can get onto the "Whitelist" for periods of three years and have to meet certain requirements, including not owing any debt to the Lithuanian government. Third-country workers for https://scholarship.claremont.edu/urceu/vol2021/iss1/9 companies on the "Whitelist" do not need to apply for a separate work permit and avoid needing to supply additional documents (Sipaviciene, 2018, p. 6). These amendments seek to encourage labor in positions in which Lithuania maintains a labor shortage.

In 2017, Lithuania also passed amendments to encourage immigration for startups and high skilled labor. Applications for residence permits under this procedure are expedited, and families may join the immigrants immediately. Temporary residence permits will be issued for founders of startups for one year and can be extended for an additional year. Additionally, Lithuania accelerated the process for receiving an EU Blue Card to one month and extended the Blue Card to last three years instead of two. Third-country nationals with five years of professional experience do not need to meet the requirement of higher education to receive a Blue Card. In February of 2017, the Lithuanian government approved 27 professions for the in-high-demand list, including ICT professions, that exempt third-party nationals from the labor market test that precedes the Blue Card application in Lithuania. The process for applying for a Blue Card for skilled professions in which Lithuania maintains a shortage expedites the Blue Card process (Sipaviciene, 2018, p. 7).

However, the process for seasonal employment (short-term employment) is stricter in Lithuania. Seasonal employment can last no longer than six months in a 12-month period, as opposed to 12 months in Estonia. Additionally, employers in Lithuania can only request a work permit for third-country nationals if there is no potential seasonal worker in Lithuania, prioritizing Lithuanian citizens for employment (Sipaviciene, 2018, p. 8). Through seasonal employment, Lithuania restricts the period of employment and prioritizes Lithuanian citizens, discouraging immigration for seasonal employment.

Conclusions

Estonia experienced significantly less negative net migration as compared to Latvia and Lithuania in the first decade of its EU membership, and its net migration turned positive in 2015, again in contrast with the other two Baltic states. Estonia's positive net migration in 2015-2019 resulted from an increase in immigration that outpaced the increase in emmigration during the same time period.

The hypotheses that wage levels and unemployment rate serve as push and pull factors for emigration are consistent with the higher emigration levels observed in Latvia and Lithuania as compared to Estonia. Estonia reports slightly higher wage levels and a lower unemployment rate than Latvia and Lithuania. This is consistent overtime from 2004 to 2019. The relevant hypotheses therefore correctly predict that Latvia and Lithuania would experience relatively higher levels of emigration compared to Estonia.

Additionally, Estonia, despite having an immigration quota, has decreased restrictions to a greater extent than Latvia and Lithuania, especially for short-term employment. All three Baltic states have witnessed increases in immigration since 2010 and have implemented policies to attract certain types of immigrants and disincentivize immigration for other sectors. Latvia and Lithuania have increased restrictions for employment in sectors without labor shortages and facilitated the immigration process for sectors in which there are labor shortages. All three countries have adopted policies simplifying immigration for founders and employees involved in creating startups to foster innovation, especially Estonia with the Startup Estonia program. Overall, immigration policy in all three countries focuses on innovation and filling high skilled positions as well as labor shortages.

This study reveals that there is more research to be done, especially regarding social factors of immigration and emigration. While economic factors shape voluntary migration, Variations in Migration in the Baltic States social factors, such as migrant networks, may be beneficial to understanding the maintenance of migration levels. Additional research regarding immigration with the same economic factors focusing on countries of departure compared to the Baltic states would add to this discussion as well. This research accompanied by further research regarding migration patterns and motivations will aid in developing policies that align with the countries' interests regarding migration.

Figure 1 .
1Net Migration in the Baltic StatesSource: Adapted from United Nations(2019)

From 2004 to 2019 ,
2019Lithuania has consistently maintained higher levels of emigration than Latvia and Estonia. For 2019, Lithuania experienced positive net migration with a dramatic increase in immigration and a decrease in emigration. There is limited evidence available to explain the increase in immigration and decrease in emigration in this year. For this reason, most of the data focuses on 2004 to 2018. During the years of the recession in 2008 to 2010, Latvia and Lithuania experienced higher levels of emigration. Estonia, however, experienced an increase in emigration in 2015. 4.3. Push Factors.

Figure 2 .
2Average Wages Source: OECD (2020a) Variations in Migration in the Baltic States

Figure 2
2Figure 2 indicates Latvia, Lithuania, and Estonia have significantly lower average wages than most other OECD countries, including most of the countries in the EU. In 2018, average wages were 28,499 USD in Estonia, 26,765 USD in Latvia, and 27,368 USD in Lithuania. As wages in the Baltic countries are lower than in other EU and OECD countries, it would be expected, on the wage push factor hypothesis, that residents of those countries would leave to pursue higher wages in another country, especially people from Latvia and Lithuania. 4.3.2. Unemployment Another push factor I analyze with respect to Estonia, Latvia, and Lithuania is unemployment. Scholars, such as Laura Thaut, assume that migrants would leave a country with relatively few employment opportunities, for a country with more employment opportunities (Thaut, 2009, p. 204). Figure 3 shows the unemployment rate as percent of labor force from 2004 to 2019. Estonia maintains a lower unemployment rate than Lithuania and Latvia. All three Baltic countries experienced dramatic increases in the unemployment rate following the recession in 2008. Over time, however, Estonia has maintained a lower unemployment rate.

Figure 3 .
3Unemployment Rate of the Baltic States 2004-2019 Source: OECD (2020c) Figure 4 compares unemployment in the Baltic countries to other OECD countries in 2018.

Figure 4 .
4Unemployment

Figure 6 .
6Unemployment Rate Baltics Compared to United KingdomSource: OECD (2020c)

Table 1 .
1Emigrants Leaving the Baltic States per 1000 PeopleYear
Estonia
Latvia
Lithuania

2004
2.1
8.9
11.1

2005
3.4
7.8
17.3

2006
4.1
7.6
9.9

2007
3.3
7.0
9.4

2008
3.3
12.3
8.0

2009
3.5
17.7
12.1

2010
4.0
18.7
26.5

2011
4.67
14.6
17.7

2012
4.77
12.3
13.7

2013
5.11
11.2
13.1

2014
3.52
9.5
12.4

2015
9.89
10.1
15.2

2016
10.48
10.5
17.4

2017
9.39
9.1
16.8

https://scholarship.claremont.edu/urceu/vol2021/iss1/9

Table 2 .
2Immigrants to the Baltic States per 1000 People in the Receiving CountryYear
Estonia
Latvia
Lithuania

2004
0.8
2.1
1.6

2005
1.1
3.0
2.0

2006
1.7
3.7
2.4

2007
2.8
3.4
2.7

2008
2.7
2.1
2.9

2009
2.9
1.7
2.0

2010
2.1
1.9
1.7

2011
2.8
4.9
5.1

2012
2.0
6.5
6.6

2013
3.1
4.1
7.4

2014
3.0
5.2
8.3

2015
11.7
4.8
7.6

2016
11.3
4.2
7.0

2017
13.4
5.1
7.2

2018
13.3
5.6
10.3

2019
13.8
5.9
14.3

Table 2 Notes: Total immigrant data compiled from Central Statistical Bureau of Latvia (2020b); Statistics Estonia (2020b);
Statistics Lithuania (2020a); total population data from Eurostat, 2020b. To reflect immigrants in relation to population, the
data was calculated in the formula (Total immigrants/total population) x 1000 for each year.

https://scholarship.claremont.edu/urceu/vol2021/iss1/9

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