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REVIEW 5 major objections 6 minor 45 references

Refugees' path to legal stability is long and systematically unequal

T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that in Austria the time to legal stability for refugees varies from about two months for Ukrainians to 20 months for Afghans, with the gap shaped by nationality, gender, and how people enter the country.

desk verdict A genuinely useful descriptive study of legal-status transitions whose headline waiting times are model outputs from an untestable Markov assumption. read the letter →

arxiv 2506.07916 v1 pith:A4EVFC6I submitted 2025-06-09 physics.soc-ph cs.SIphysics.data-an

Ola Ali , Elma Dervic , Guillermo Prieto-Viertel , Carsten Källner , Rainer Stütz , Andrea Vismara , Rafael Prieto-Curiel This is my paper
classification physics.soc-phcs.SIphysics.data-an MSC 60J2091D20 PACS 89.65.-s
keywords legalstatustransitionsrefugeeintegrationMarkovchainAustriaasylumoutcomesgenderdisparityirregularentryadministrativedata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to show that legal integration in Austria is a systematically unequal process, not a uniform bureaucratic track. Tracking 358,327 migrants through daily changes among 12 legal statuses over two years, the authors build a network/Markov model of 'legal journeys' and simulate first-year and decade-long outcomes. They report that average time to a stable status is about two months for Ukrainians, nine months for Syrians, and twenty months for Afghans, with Afghan men averaging thirty months. They also find that entering without official border controls doubles to quadruples the chance of exiting within the first year and lowers the odds of gaining protection. If true, the results imply that institutional design and procedural entry points, not just individual circumstances, drive legal inequality.

What carries the argument

The load-bearing object is the transition matrix $T$ with entries $T_{ij} = P(X_{t+1}=j \mid X_t=i)$, estimated per country of origin (and per gender) from daily counts of switches among 11 legal statuses plus an absorbing 'exit' state. The matrix encodes each country's legal-pathway 'signature,' and it is used two ways: to simulate 10,000 synthetic migrants through a year or a decade, and to compute mean first-passage (settling) times by solving $(I - T + \varepsilon I)h = b$. The entire analysis—including the unequal timelines and entry-mode comparisons—flows through these simulated trajectories, so the Markov assumption and the constant transition probabilities carry the argument.

What would settle it

Estimate the transition matrices from the first observed year of the Austrian records, then use them to predict the status distribution and number of transitions in the second year; if the predicted counts differ substantially from what was actually recorded, the constant-Markov assumption fails and the simulated settlement times are unreliable. A direct comparison of simulated settling-time curves with raw cohort Kaplan-Meier curves would give the same verdict.

Watch

Extended reading notes

Core claim

The central claim is that a person's path from arrival to legal stability in Austria is strongly conditioned by nationality, gender, and mode of entry, and that these differences are visible in the structure of legal-status transition networks. Using daily administrative records, the authors estimate country-specific 12x12 Markov transition matrices, observe that 70% of migrants change status at least once within 734 days, and simulate cohorts of 10,000 migrants to compute settling probabilities and times. The headline results are the unequal timelines—two months for Ukrainian 'displaced persons,' nine months for Syrians reaching asylum or subsidiary protection, and twenty months for Afghans—and the entry-mode gap, where asylum seekers who bypass official border controls face two-to-four-times higher first-year exit rates and reduced chances of stable protection. A consistent female advantage appears for Syrian and Afghan women, with Afghan women settling in about 14 months versus 30 for men.

Load-bearing premise

The model assumes that a migrant's next legal status depends only on the current status and that the daily transition probabilities stay constant over a decade, yet the data average only about one status switch per person over 734 days—and the authors explicitly state in the supplementary material that this is too little history to test the Markov assumption.

Editorial extensions

If this is right

  • If the unequal timelines are real, policy interventions that shorten waiting times for Afghan and Syrian applicants would directly compress the legal phase in which refugees lack housing, work, and health security.
  • Channeling legal aid toward groups with high instability and toward those who enter through irregular border crossings could reduce the two-to-four-fold exit gap seen in the first year.
  • Standardizing processing timelines and clarifying eligibility for stable statuses would be the levers the paper points to for narrowing nationality-based disparities.
  • The transition-matrix approach can serve as an early-warning instrument: repeated analysis of daily records could flag emerging bottlenecks or unequal treatment in real time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The entry-mode contrast is observational, and the paper does not control for selection: people who cross irregularly may differ in vulnerability, legal representation, or case quality, so the two-to-four-fold exit gap should not be read as purely causal without further identification.
  • The numerical settling times are tied to the paper's definition of 'stable status' and the absorbing exit assumption; regrouping statuses or allowing re-entry would shift the numbers even if the ordering by nationality stayed similar.
  • Extending the same daily-transition method to other countries' registers would test whether the Austrian inequality pattern is a national administrative artifact or a more general property of asylum systems.
  • A natural next step is a semi-Markov or Cox model that lets transition probabilities depend on time already spent in a status; the paper's own data weaknesses point to that as the decisive robustness check.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The manuscript uses administrative daily residence-status records for 358,327 migrants who entered Austria between November 2022 and 2024 to construct a 12-state Markov chain of legal statuses (11 legal statuses plus exit) for each country of origin and gender. From these fitted chains the authors simulate first-year status distributions and decade-long trajectories, reporting mean times to 'stable' status (residence permit for general migrants; asylum, subsidiary protection, humanitarian permit, or displaced-person status for refugees). The headline results are that Ukrainian refugees reach stability in about two months, Syrians about nine months, Afghans about 20 months (30 for Afghan men); that entering as an asylum seeker rather than as a foreigner is associated with two- to four-fold higher one-year exit rates; and that women have higher probabilities of gaining protection. The paper also introduces a network visualization of legal transitions as country-specific 'signatures.'

Significance. The paper addresses an important and understudied aspect of migrant integration—the dynamics of legal status itself—using a uniquely large administrative dataset (140 million daily records). The descriptive network signatures and raw transition counts are a valuable contribution, and the qualitative finding of inequality across nationalities and genders is plausible from the observed data. The authors are also transparent about a key limitation (SI 5.2: the Markov assumption cannot be tested with roughly one transition per person). If the headline waiting times were supported by robustness checks, the paper would be a significant policy-relevant result. In the current form, however, the quantitative claims are entirely model outputs from an assumption that the authors state they cannot validate, with no sensitivity analysis or out-of-sample validation; the significance is therefore conditional on additional evidence.

major comments (5)
  1. [Section 4.2, SI 5.2, Figure 5] The headline settling times (2, 9, 20, and 30 months) are first-passage times of a time-homogeneous Markov chain fitted to daily transitions, extrapolated over a decade from a 734-day observation window. SI 5.2 states that the average number of status changes per person is about 1.02 (Table 3), with no group exceeding 1.5, making the Markov assumption impossible to test with these data. Because the long-run behaviour of the chain is governed by the estimated self-loop probabilities and rare off-diagonal transitions, small errors in those probabilities will compound exponentially over a simulated decade. The manuscript provides no confidence intervals, perturbation analysis, or comparison with a semi-Markov model with realistic sojourn distributions. Please add such robustness checks, or explicitly present the decade projections as illustrative rather than empirical estimates.
  2. [Section 2.3, Table 1] The comparison of 'stable' status across refugee groups is not apples-to-apples: for Ukraine the absorbing stable state is 'displaced person', a temporary protection status under the Displaced Persons Act, while for Syria and Afghanistan the stable states are durable asylum statuses ('entitled to asylum' and 'subsidiary protection'). The manuscript classifies all of these as 'stable' but does not demonstrate that the legal quality and long-term security of displaced-person status are comparable to asylum. If they are not, the headline contrast (two months for Ukrainians versus nine and 20 months for Syrians and Afghans) may reflect the different nature of the endpoint rather than the speed of legal integration. Please either justify the equivalence or restrict the headline comparison to comparable durable statuses.
  3. [Section 2.3, Figure 5] The reported mean time to stability is not precisely defined. For Afghanistan, Figure 5 shows that only 56% of the simulated population reaches a stable status within a decade; the text reports an 'average around 20 months'. It is unclear whether this mean is taken over those who settle (with the rest excluded), over all simulated individuals with censoring at the horizon, or over the finite-horizon first-passage times including an infinite value for non-settlers. Each definition changes the number substantially and affects cross-country comparability because settling fractions differ. Please state the estimator precisely and report medians or quantiles alongside the means.
  4. [Section 2.2, Discussion] The comparison of 'entering as asylum seeker' versus 'entering as foreigner' is presented with causal language ('entry status plays a critical role in determining the likelihood of asylum acceptance') but is based on raw comparisons between two groups that very likely differ in observed and unobserved characteristics (age composition in Table 5, family status, circumstances of flight). The Discussion itself concedes that 'these differences may reflect, in part, variation in the profiles of those applying at the border'. Since the data include age and gender, the authors could at least stratify or adjust for these variables; otherwise the claims of two- to four-fold higher exit rates should be framed as descriptive associations, not as causal effects of entry mode.
  5. [Section 4.2, Section 2.3] The manuscript repeatedly uses the term 'predict' for the Markov-chain projections, but no out-of-sample or temporal validation is provided. Since the data span November 2022 through late 2024, a natural check would be to fit the transition matrices on the first year and compare the projected one-year distributions and settling times with the actually observed second-year outcomes. Even in the absence of code (data are privacy-restricted), the modelling code could be released, and a synthetic-data illustration would allow readers to assess sensitivity. Without any validation, the quantitative predictions are not empirically secure.
minor comments (6)
  1. [Section 4.1] The phrase 'we observe 367,146 legal switches daily' should read 'over the 734-day observation period', because 367,146 is the total number of switches, not a daily count.
  2. [Section 4.2] The notation 'state at weekt' appears to contain a typo; the time unit is elsewhere defined as days, so please make the notation consistent.
  3. [Figure 5] The x-axis label appears to contain a stray character ('ž') before 'Population %'.
  4. [Figure 2] The caption states 'dashed nodes have no migrants' but then describes dashed edges as indicating fewer than 100 migrants; please clarify what dashed nodes signify, since the node style is not otherwise explained.
  5. [Section 4.1] The treatment of re-entry is under-specified: 'If a migrant leaves and returns within the two years of observation, we treat this as a continuation of their original stay' conflicts with the statement that exit is an absorbing state; please explain how this rule is implemented in the transition counts and whether exit remains absorbing in the model.
  6. [Supplementary Information] The interactive figure URLs are a helpful resource, but please also provide archival or persistent links (e.g., DOI or repository) so that the visualizations remain accessible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported waiting times are Markov-simulation outputs computed from the observed transition matrix, not fitted quantities renamed as predictions, and the untestable Markov assumption is explicitly flagged in SI 5.2.

full rationale

Section 4.2 defines the 12x12 transition matrix from observed daily status switches, and Section 2.3 computes settling times by simulating that matrix over a decade, with SI 5.5 giving the hitting-time linear system. This is a transparent model-based estimate: the settling time is a deterministic functional of the transition matrix, but it is not used to define or fit that matrix, and no fitted parameter is relabelled as an independent prediction. The Markov assumption is the only structural addition, and SI 5.2 explicitly concedes that the data cannot test it (about 1.02 switches per person on average); that is a robustness or correctness caveat, not circularity. Self-citations appear (refs 3, 14, 36), but only as background statistics or examples of Markov modelling, not as load-bearing premises or imported uniqueness theorems. The headline figures of two, nine, twenty, and thirty months are model outputs, and the paper identifies them as simulated estimates; the raw transition counts independently support the qualitative inequality pattern. No equation reduces by construction to a parameter fitted to the claimed outcome, and no known result is merely relabelled.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central quantitative claims rest on transition matrices fitted to daily data and on the Markov assumption that the authors admit cannot be tested with the available data. The granularity choice (daily versus weekly) also changes the estimated transition counts materially. No new particles, forces, or external entities are introduced.

free parameters (2)
  • Country- and gender-specific transition matrices T = estimated from observed daily status switches
    All reported probabilities and settling times are computed from these fitted matrices, so the quantitative claims inherit their values.
  • Simulation population size per group = 10,000
    An arbitrary normalization used for display; it does not affect probabilities, only counts.
assumptions (4)
  • domain assumption Legal status transitions form a time-homogeneous Markov chain with daily steps.
    Stated in Methods Section 4.2 and SI Section 5.2; used to reconstruct trajectories from two years of data and to simulate 10-year outcomes.
  • domain assumption Exit is an absorbing state; re-entry within the observation period is treated as continuation of the original stay.
    Methods Section 4.1; this simplifies the model but may bias long-term estimates for circular or repeat migration.
  • domain assumption The 12 statuses and their grouping into entry, transitionary, stable, and exit categories are a faithful representation of the Austrian legal system.
    Based on Table 1; grouping choices affect the definition of 'stable' and therefore time-to-stability estimates.
  • domain assumption Daily observation granularity captures meaningful legal status changes.
    SI Section 5.1 notes that daily intervals yield 70% changers while weekly intervals yield 30%, so the choice of granularity strongly affects transition estimates.

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Pith. "Pith review of Refugees' path to legal stability is long and systematically unequal." pith.science (2026). https://pith.science/paper/A4EVFC6I

@misc{pith2026250607916,
  author       = {Pith},
  title        = {Pith review of: Refugees' path to legal stability is long and systematically unequal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A4EVFC6I}},
  note         = {Machine review of arXiv:2506.07916}
}
read the original abstract

Legal systems shape not only the recognition of migrants and refugees but also the pace and stability of their integration. Refugees often shift between multiple legal classifications, a process we refer to as the "legal journey". This journey is frequently prolonged and uncertain. Using a network-based approach, we analyze legal transitions for over 350,000 migrants in Austria (2022 to 2024). Refugees face highly unequal pathways to stability, ranging from two months for Ukrainians to nine months for Syrians and 20 months for Afghans. Women, especially from these regions, are more likely to gain protection; Afghan men wait up to 30 months on average. We also find that those who cross the border without going through official border controls face higher exit rates and lower chances of securing stable status. We show that legal integration is not a uniform process, but one structured by institutional design, procedural entry points, and unequal timelines.

Figures

Figures reproduced from arXiv: 2506.07916 by the authors.

Figure 1
Figure 1. A Walk Through The Legal System. This illustration shows a refugee’s average legal transition journey (Top) from a foreigner, applying for asylum, being approved as an asylum seeker, to being entitled to asylum, to exit. Compared with a migrant (Bottom), going from the status of a foreigner to having a residence permit, to exit. While the signatures reveal broad patterns in legal trajectories across countries, they … view at source ↗
Figure 2
Figure 2. Legal Status Network of Migrants in Austria. Nodes correspond to legal statuses, their size corresponds to the total incoming and outgoing migrants, and dashed nodes have no migrants. Edge width represents the daily average number of migrants transitioning between statuses, and dashed edges indicate fewer than 100 migrants. This figure illustrates the unique signatures of legal transitions for the two largest migran… view at source ↗
Figure 3
Figure 3. Population Legal Distribution within the First Year of Entry. Modeled share of 10,000 migrants from various countries of origin who enter as foreigners and transition through different legal statuses. The probability of attaining stability within the first year is estimated based on observed transition patterns. An interactive version of this plot of all countries of origin is available at: https://vis.csh.ac.at/mig… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Entering as an Asylum Seeker vs Foreigner. Modeled share of 10,000 migrants from conflict-affected countries of origin who enter as asylum seekers (Top) vs as foreigners (Bottom) and transition through different legal statuses. The probability of attaining stability wi…
Figure 5
Figure 5. Figure 5: Settling Time within a Decade of Entry. The expected time in days to obtain a stable status for migrants (which is residence permit) and refugees (one of the four refugee statuses, more on the stable statuses in SI part 1) on the horizontal line while the percentage of…
Figure 6
Figure 6. Figure 6: Gender Differences. Modeled share of 10,000 female and male migrants transition through different legal statuses. The probability of attaining stability within the first year is estimated based on observed transition patterns. An interactive version of this figure and …

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Works this paper leans on

45 extracted references · 45 canonical work pages

  1. [1]

    Estimation of international migration flow tables in Europe

    Guy J Abel. “Estimation of international migration flow tables in Europe”. In: Journal of the Royal Statistical Society Series A: Statistics in Society 173.4 (2010), pp. 797–825

    work page 2010

  2. [2]

    Quantifying global international migration flows

    Guy J Abel and Nikola Sander. “Quantifying global international migration flows”. In: Science 343.6178 (2014), pp. 1520–1522

    work page 2014

  3. [3]

    Quantifying the stability of refugee populations: a case study in Austria

    Ola Ali et al. “Quantifying the stability of refugee populations: a case study in Austria”. In: Genus (2024)

    work page 2024

  4. [4]

    Limbo or leverage? Asylum waiting and refugee integra- tion

    Olof Åslund, Mattias Engdahl, and Olof Rosenqvist. “Limbo or leverage? Asylum waiting and refugee integra- tion”. In: Journal of Public Economics 234 (2024), p. 105118

    work page 2024

  5. [5]

    Austria Statistics

    Asylum Information Database (AIDA). Austria Statistics. Accessed: 2024-11-13. 2023. URL: https : / / asylumineurope.org/reports/country/austria/statistics/

    work page 2024

  6. [6]

    Temporary Protection: Austria

    Asylum Information Database (AIDA). Temporary Protection: Austria. https://asylumineurope.org/wp- content/uploads/2023/05/AIDA- AT_Temporary- Protection_2022.pdf . Accessed on: 2024-06-06. 2022

    work page 2023

  7. [7]

    “The importance of resources and security in the socio- economic integration of refugees

    Linda Bakker, Jaco Dagevos, and Godfried Engbersen. “The importance of resources and security in the socio- economic integration of refugees. A study on the impact of length of stay in asylum accommodation and residence status on socio-economic integration for the four largest refugee groups in the Netherlands”. In: Journal of International Migration and ...

    work page 2014

  8. [8]

    Refugees’ trajectories in Switzerland: Impact of residence permits on labour market integration

    Anne-Laure Bertrand. “Refugees’ trajectories in Switzerland: Impact of residence permits on labour market integration”. In: Quetelet Journal 7.1 (2019), pp. 71–99

    work page 2019

Show all 45 references
  1. [9]

    Migration in Austria after the Fall of the Iron Curtain

    Gudrun Biffl. “Migration in Austria after the Fall of the Iron Curtain”. In: Austrian History Yearbook (2024), pp. 1–15

    2024

  2. [10]

    Research-Policy Dialogues in Austria

    Maren Borkert. “Research-Policy Dialogues in Austria”. In: Integrating Immigrants in Europe: Research-Policy Dialogues. Ed. by Peter Scholten et al. Cham: Springer International Publishing, 2015, pp. 143–164. ISBN : 978-3-319-16256-0. DOI: 10.1007/978-3-319-16256-0\_9

    2015 doi

  3. [11]

    EU law and the detainability of asylum-seekers

    Cathryn Costello and Minos Mouzourakis. “EU law and the detainability of asylum-seekers”. In: Refugee Survey Quarterly 35.1 (2016), pp. 47–73

    2016

  4. [12]

    Inside Asylum Bureaucracy: Organizing Refugee Status Determination in Austria

    Julia Dahlvik. “Inside Asylum Bureaucracy: Organizing Refugee Status Determination in Austria”. In: Cham: Springer International Publishing, 2018, E3–E3. ISBN : 978-3-319-63306-0. DOI: 10.1007/978-3-319-63306- 0\_12

    2018 doi

  5. [13]

    Gender in waiting: Men and women asylum seekers in European reception facilities

    Giorgia Demarchi and Giorgia Demarchi. Gender in waiting: Men and women asylum seekers in European reception facilities. https://documents1.worldbank.org/curated/en/532181547235243643/133693- Gender-in-Waiting-Men-and-Women-Asylum-Seekers-in-European-Reception-Facilities.pdf ....

    2024

  6. [14]

    Healthcare Utilization Patterns Among Migrant Populations: Increased Readmissions Suggest Poorer Access. A Population-Wide Retrospective Cohort Study

    Elma Dervi´c et al. “Healthcare Utilization Patterns Among Migrant Populations: Increased Readmissions Suggest Poorer Access. A Population-Wide Retrospective Cohort Study”. In: arXiv preprint arXiv:2408.16317 (2024)

    2024 arXiv

  7. [15]

    Migration and Education

    Christian Dustmann and Albrecht Glitz. “Migration and Education”. In:Handbook of the Economics of Education. V ol. 4. Radarweg 29, 1043 NX Amsterdam, The Netherlands: Elsevier, 2011, pp. 327–439

    2011

  8. [16]

    Access to counsel in immigration court

    Ingrid Eagly and Steven Shafer. “Access to counsel in immigration court”. In: American Immigration Council 28 (2016)

    2016

  9. [17]

    Access to the Labour Market and Labour Market Integration of Asylum-Seekers in Austria

    Petra Ebner. Access to the Labour Market and Labour Market Integration of Asylum-Seekers in Austria. Report. Vienna: International Organization for Migration (IOM), 2023

    2023

  10. [18]

    Asylum applications - annual statistics

    Eurostat. Asylum applications - annual statistics. Accessed: 2025-05-05. 2024. URL: %7Bhttps://ec.europa. eu / eurostat / statistics - explained / index . php ? title = Asylum _ applications_ - _annual _ statistics#Main_trends_in_the_number_of_asylum_applicants%7D

    2025

  11. [19]

    Settlement and Residence

    Federal Ministry Replublic of Austria European and International Affairs. Settlement and Residence. https: //www.bmeia.gv.at/en/travel-stay/entrance-and-residence-in-austria/settlement-and- residence. Accessed: 2024-12-16. 2024

    2024

  12. [20]

    Education for Refugee and Asylum Seeking Children: Access and Equality in England, Scotland and Wales

    Catherine Gladwell and Georgina Chetwynd. Education for Refugee and Asylum Seeking Children: Access and Equality in England, Scotland and Wales. Tech. rep. UNICEF UK, July 2018. URL: https://www.unicef. org.uk/wp-content/uploads/2018/09/Access-to-Education-report-PDF.pdf

    2018

  13. [21]

    When lives are put on hold: Lengthy asylum processes decrease employment among refugees

    Jens Hainmueller, Dominik Hangartner, and Duncan Lawrence. “When lives are put on hold: Lengthy asylum processes decrease employment among refugees”. In: Science Advances 2.8 (2016), e1600432

    2016

  14. [22]

    The Missing Link: Connecting Eligible Asylees and Asylum Seekers with Benefits and Services

    Stephanie Heredia. The Missing Link: Connecting Eligible Asylees and Asylum Seekers with Benefits and Services. Tech. rep. Migration Policy Institute, July 2022. URL: https://www.migrationpolicy.org/research/ asylees-asylum-seekers-benefits . 10 Refugees’ path to legal stabili...

    2022

  15. [23]

    Prolonged periods of waiting for an asylum de- cision and the risk of psychiatric diagnoses: a 22-year longitudinal cohort study from Denmark

    Camilla Hvidtfeldt, Jørgen Holm Petersen, and Marie Norredam. “Prolonged periods of waiting for an asylum de- cision and the risk of psychiatric diagnoses: a 22-year longitudinal cohort study from Denmark”. In:International Journal of Epidemiology 49.2 (2020), pp. 400–409

    2020

  16. [24]

    Waiting for family reunification and the risk of mental disorders among refugee fathers: a 24-year longitudinal cohort study from Denmark

    Camilla Hvidtfeldt, Jørgen Holm Petersen, and Marie Norredam. “Waiting for family reunification and the risk of mental disorders among refugee fathers: a 24-year longitudinal cohort study from Denmark”. In: Social Psychiatry and Psychiatric Epidemiology (2022), pp. 1–12

    2022

  17. [25]

    The Organization of Asylum and Migration Policies in Austria

    International Organization for Migration. The Organization of Asylum and Migration Policies in Austria. Tech. rep. 17 Route des Morillons, P.O. Box 17, 1211 Geneva 19, Switzerland: International Organization for Migration (IOM), 2015, p. 109

    2015

  18. [26]

    Barriers to health care access and service utilization of refugees in Austria: evidence from a cross-sectional survey

    Judith Kohlenberger et al. “Barriers to health care access and service utilization of refugees in Austria: evidence from a cross-sectional survey”. In: Health Policy 123.9 (2019), pp. 833–839

    2019

  19. [27]

    Waiting as probation: selecting self-disciplining asylum seekers

    Nick Gill Lorenzo Vianelli and Nicole Hoellerer. “Waiting as probation: selecting self-disciplining asylum seekers”. In: Journal of Ethnic and Migration Studies 48.5 (2022), pp. 1013–1032. DOI: 10.1080/1369183X. 2021.1926942

    2022

  20. [28]

    From refugees to workers: mapping labour market integration support measures for asylum- seekers and refugees in EU member states

    Iván Martin et al. “From refugees to workers: mapping labour market integration support measures for asylum- seekers and refugees in EU member states”. In: Volume I: Comparative Analysis and Policy Findings(2016)

    2016

  21. [29]

    Migration, labor markets, and integration of migrants: An overview for Europe

    Rainer Münz. “Migration, labor markets, and integration of migrants: An overview for Europe”. In: (2007)

    2007

  22. [30]

    Stuck in reception: How refugees in Austria and Germany experience long-term reception constellations

    Alexander-Kenneth Nagel and Ursula Reeger. “Stuck in reception: How refugees in Austria and Germany experience long-term reception constellations”. In: Politics of Subsidiarity in Refugee Reception. England, UK: Routledge, 2023, pp. 11–23

    2023

  23. [31]

    Making integration work: Refugees and others in need of protection

    OECD. Making integration work: Refugees and others in need of protection. Paris, France: OECD Publishing, 2016

    2016

  24. [32]

    ODA In-Donor Refugee Costs: Austria

    OECD. ODA In-Donor Refugee Costs: Austria. Accessed: 2025-01-29. 2021. URL: https://www.oecd.org/ content/dam/oecd/en/topics/policy-issue-focus/in-donor-refugee-costs-in-oda/oda-in- donor-refugee-costs-austria.pdf

    2025

  25. [33]

    A female advantage in asylum application decisions? A gendered analysis of decisions on asylum applications in Italy from 2008 to 2022

    Livia Elisa Ortensi, Giorgio Piccitto, and Sara Morlotti. “A female advantage in asylum application decisions? A gendered analysis of decisions on asylum applications in Italy from 2008 to 2022”. In: Genus 80.1 (2024), p. 13

    2024

  26. [34]

    Stochastic processes

    Emanuel Parzen. Stochastic processes. Philadelphia, USA: Society for Industrial and Applied Mathematics (SIAM), 1999

    1999

  27. [35]

    The violence of uncertainty: Empirical evidence on how asylum waiting time undermines refugee health

    Jenny Phillimore and Sin Yi Cheung. “The violence of uncertainty: Empirical evidence on how asylum waiting time undermines refugee health”. In: Social Science & Medicine 282 (2021), p. 114154

    2021

  28. [36]

    Mobility between Colombian cities is predominantly repeat and return migration

    Rafael Prieto Curiel et al. “Mobility between Colombian cities is predominantly repeat and return migration”. In: Computers, Environment and Urban Systems 94 (2022), p. 101774

    2022

  29. [37]

    The diaspora model for human migration

    Rafael Prieto-Curiel et al. “The diaspora model for human migration”. In: PNAS nexus 3.5 (2024), pgae178

    2024

  30. [38]

    Implementing and rethinking the European Union’s asylum legislation: the asylum proce- dures directive

    Karin Schittenhelm. “Implementing and rethinking the European Union’s asylum legislation: the asylum proce- dures directive”. In: International Migration 57.1 (2019), pp. 229–244

    2019

  31. [39]

    The educational and mental health needs of Syrian refugee children

    Selcuk R Sirin and Lauren Rogers-Sirin. The educational and mental health needs of Syrian refugee children. Washington, DC: Migration Policy Institute, 2015

    2015

  32. [40]

    Markov chain analysis and specialization in criminal careers

    Julian Stander et al. “Markov chain analysis and specialization in criminal careers”. In: The British Journal of Criminology 29.4 (1989), pp. 317–335

    1989

  33. [41]

    Migration and Naturalisation rates in Austria

    Statistik Austria. Migration and Naturalisation rates in Austria . https : / / www . statistik . at / en / statistics / population - and - society / population / migration - and - naturalisation / naturalisation. Accessed: 2025-03-26

    2025

  34. [42]

    UNHCR Refugee Statistics

    UNHCR. UNHCR Refugee Statistics. https://www.unhcr.org/refugee-statistics. Accessed: 2024-05-

    2024

  35. [43]

    Family Reunification in Austria

    UNHCR Austria. Family Reunification in Austria . https : / / help . unhcr . org / austria / family - reunification/. Accessed: 2025-01-29. 2025

    2025

  36. [44]

    Crime in a Birth Cohort

    Marvin E. Wolfgang. “Crime in a Birth Cohort”. In: Proceedings of the American Philosophical Society 117.5 (1973), pp. 404–411. ISSN : 0003049X. URL: http://www.jstor.org/stable/986609 (visited on 02/06/2025)

    1973

  37. [45]

    Waiting in the Austrian asylum system: The well-being of asylum-seeking children in a phase of liminality

    Stella Wolter, Rosa Tatzber, and Birgit Sauer. “Waiting in the Austrian asylum system: The well-being of asylum-seeking children in a phase of liminality”. In: Children & Society 37.3 (2023), pp. 806–819. 11 Refugees’ path to legal stability is long and systematically unequal....

    2023

Pith tools

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